Seismic anisotropy -- the directional dependence of seismic wave speeds -- provides a unique view into the past and present deformation of Earth's interior. However, constraining Earth's anisotropic heterogeneity remains a challenge primarily due to imperfect data coverage combined with the increased number of free parameters required to describe elastic anisotropy. And yet, exploring this more complex model space is critical for the interpretation of seismic velocity anomalies which may be significantly distorted if anisotropy is neglected. In this presentation, I will review new imaging strategies, developed by myself and colleagues, for constraining 3D anisotropic structures and their application to studying subduction zone dynamics and volcanic processes. Key developments include moving beyond simplified assumptions regarding the orientation of anisotropic fabrics (i.e. from azimuthal and radial parameterisations to tilted-transversely isotropic models), the integration of multiple and complementary seismic observables (P and S body wave arrivals, shear wave splitting measurements, and surface wave constraints), and the use of probabilistic inversion algorithms that allow for rigorous exploration of model uncertainty and parameter trade-offs. I will discuss how applying these imaging approaches to subduction systems in the central Mediterranean and Western USA yields new insights into the geometry of mantle flow, the nature of seismic velocity heterogeneity, and trade-offs between isotropic and anisotropic features. At smaller scales, I will highlight how new anisotropic tomography reveals the structure of the magmatic plumbing system beneath Mt. Etna (Italy) and provides constraints on the geologic processes controlling crustal stresses.

How to cite: VanderBeek, B.: Improved Strategies for Seismically Imaging Earth's Anisotropic Interior with Applications to Subduction Zones and Volcanic Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17444, https://doi.org/10.5194/egusphere-egu25-17444, 2025.

The tectonic framework of Bhutan Himalaya documents significant along-strike variability in crustal structure and deformation. To visualize this spatial and depth variability, we compile an extensive dataset of surface-wave phase velocities derived from seismic ambient noise and teleseismic earthquakes recorded by the temporary GANSSER network (2013-2014) in Bhutan, aiming to produce Rayleigh phase-velocity maps over the period range of 4 to 50 seconds. We translate the phase-velocity maps into a 3-D shear-wave velocity model stretching from the surface to a depth of 42 kilometres. The employed methodologies enable imaging of the upper to mid-crustal and lower crustal velocity anomalies with a lateral resolution of approximately 25 km. The obtained tomographic model fills a void in the prior established shear-wave velocity structure of Bhutan, encompassing depths from upper-crustal to lowermost crust. Our findings indicate notable mid-crustal to lower-crustal high phase velocity anomalies in central Bhutan (around 90.5). The presence of this significant anomaly within the mid- to lower crustal layer may indicate localized stress accumulation along the Main Himalayan Thrust (MHT) resulting from the interaction of the dipping and sub-horizontal Moho. This area might act as a stress concentration zone, resulting in increased deformation and enhanced shear-wave velocity in the crust. Minor fluctuations in velocity across latitude may result from variations in the local geometry of MHT (dip or ramp-flat transition). Localised high shear velocity in western Bhutan may indicate a zone of crustal thickening. Northeastern Bhutan exhibits modest shear velocity, possibly because of a flat Moho and the partial creeping behaviour of the MHT.

How to cite: Kumar, G. and Tiwari, A. K.: Multiscale Surface Wave Tomography of the Bhutan Himalayas using Ambient Seismic Noise and Teleseismic Earthquake Data , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1021, https://doi.org/10.5194/egusphere-egu25-1021, 2025.

Southeastern Tibet, a segment of the eastern Himalayan Syntaxis, is a significantly deformed area resulting from multistage subduction and the ongoing collision of the Indian and Asian tectonic plates. The region has a clockwise material movement around the indenting corner of the Indian plate, evident on the surface as strike-slip faults aligned with the Himalayan Arc. Numerous scientific studies have focused on the east-west extension and tectonic history of southeastern Tibet; however, the scientific enquiries regarding the depth constraints of the crustal flow process—specifically, whether it is confined to the middle crust or extends to the lower crust beneath southeastern Tibet—remain unresolved. This study employs ambient noise tomography to  examine a 3-D high-resolution crustal velocity model for the region, which is crucial for unravelling the mechanisms that regulate crustal deformation and evolution in active orogenic systems. To do this, we examined ambient noise data from 48 seismic stations of the XE network, operational from 2003 to 2004. We obtained Rayleigh wave phase velocities ranging from 4 to 60 seconds and subsequently inverted them to develop a 3-D shear wave velocity model of the region extending to depths of 50 km. Our results reveal persistent low shear wave velocity zones at depths of 15–25 km (within the mid-crust), notably observed between the Indus Tsangpo suture and the Bangong-Nujiang Suture. We contend that the detected low-velocity zones are only linked to mid-crustal channel flow, a mechanism presumably essential for comprehending crustal deformation. Our findings provide significant constraints on the depth localisation of crustal channel flow and the interaction of tectonic forces in southern Tibet, enhancing the overall comprehension of Eastern Syntaxial tectonics.

How to cite: Mandi, A., Kumar, G., Bishoyi, N., and Tiwari, A. K.: 3-D Crustal Shear Wave Velocity Tomography Using Seismic Ambient Noise Data in Southeast Tibet, Close to Namcha Barwa Mountain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1178, https://doi.org/10.5194/egusphere-egu25-1178, 2025.

With the continuous development of deep learning technologies, fault prediction techniques based on various neural networks have been evolving. The deep learning modules based on U-Net residual networks have shown significant advantages in both learning efficiency and effectiveness. In this paper, we propose a deep learning model that integrates a 3D U-Net residual architecture, Convolutional Block Attention Module (CBAM), and Multi-scale Enhanced Global Attention (MEGA) module for automatic seismic fault detection and segmentation. This model can effectively handle complex 3D seismic data, fully exploiting both spatial and channel information, significantly improving the prediction accuracy for small faults, while only slightly increasing the computational cost.

Firstly, the model uses the 3D U-Net as the backbone framework, where the residual blocks (BasicRes) extract features through multiple convolution layers. The CBAM module is incorporated to apply attention weighting, enhancing the model's ability to focus on critical information. The CBAM module combines channel attention and spatial attention, effectively adjusting the importance of feature maps from different dimensions, enabling the model to identify potential fault features in complex seismic data.

Secondly, the MEGA module is introduced into the model, which further improves the model's feature representation ability by fusing multi-scale features and applying a global attention mechanism. By weighting global information, the MEGA module helps the model better capture key seismic fault features during feature fusion. This design allows the model to focus not only on local details but also to fully utilize the global contextual information in 3D data, thereby enhancing the accuracy of fault detection.

After validation, the model achieved promising results in seismic fault detection tasks, automatically identifying and segmenting fault structures in seismic data. The accuracy was improved from 80% with the original 3D U-Net residual network to 85%-87%. This provides strong support for applications such as seismic exploration and subsurface imaging.

Keywords: Seismic Fault Detection, 3D U-Net, Convolutional Block Attention Module (CBAM), Multi-scale Enhanced Global Attention (MEGA), Deep Learning

How to cite: wang, Y.: Application of Optimized 3D U-Net Residual Network with CBAM and MEGA Modules in Seismic Fault Detection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1772, https://doi.org/10.5194/egusphere-egu25-1772, 2025.

This article mainly studies the characteristics of the earthquake sequence and the post - earthquake trend of the Ms6.4 earthquake in Yangbi, Yunnan，China on May 21, 2021. The research area is located in Yangbi Yi Autonomous County in the western part of Yunnan Province. The earthquake caused severe disasters such as housing destruction, traffic interruption, water conservancy facilities damage and power supply interruption. Through the analysis of the basic parameters of the earthquake, the tectonic stress environment and the seismogenic structure, it is determined that the earthquake is a right - lateral strike - slip rupture, with a focal depth of 8 kilometers, consistent with the direction of the Weixi - Qiaohou and Honghe fault zones. The earthquake sequence type is determined as the main shock - aftershock type (including the foreshock - main shock - aftershock type). Spatially, the source rupture expands unilaterally from the northwest to the southeast, mostly occurring in the upper crust high - speed zone or the high - low speed transition zone. Based on the G - R relationship and other analyses, the earthquake activity cycle in this area has active and quiet periods, and there are certain abnormal change laws before strong aftershocks, such as strain accumulation, calmness or enhancement of earthquakes above magnitude 3.5, and abnormal frequency of earthquakes above magnitude 2. The conclusion is that the earthquake sequence is normal, and the post - earthquake trend shows the characteristics of long - term calmness - breaking calmness - becoming calm again - signal earthquake (main shock). In the next few years, the strain accumulation may reach the peak and release. It is predicted that there may be a larger earthquake accompanied by strong aftershocks in 2025, or enter an active period with a strong aftershock magnitude exceeding 5.9 and lasting for more than half a year. Finally, the earthquake prevention and disaster reduction countermeasures are proposed.

How to cite: Wu, B.: The determination of the seismic sequence characteristics and post - earthquake trend of the Ms6.4 earthquake in Yangbi, Yunnan, China on May 21, 2021, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2519, https://doi.org/10.5194/egusphere-egu25-2519, 2025.

Seismic inversion in geophysics is a method that uses certain prior information, such as known geological laws and well logging and drilling data, to infer the physical parameters of underground media, such as wave impedance, velocity, and density, from seismic observation data, and thereby obtain the spatial structure and physical properties of underground strata. Seismic inversion is a highly complex problem with multiple solutions, and with the advancement of collection equipment, the volume of geophysical observation data is increasing at an astonishing rate. This presents new challenges for the accuracy and speed of seismic data inversion methods. There is an urgent need to develop intelligent and efficient inversion technologies for seismic inversion.

Deep learning networks have powerful nonlinear fitting capabilities and can be used to solve complex nonlinear problems, such as seismic inversion. However, the predictive ability of deep learning networks largely depends on the quantity of training data. In the early stages of oil and gas exploration and development, the amount of well logging label data available for training is very limited, which poses a challenge for the application of deep learning in seismic inversion. Semi-supervised learning seismic inversion methods consider both data mismatch issues and well logging data mismatch issues, and can better adapt to inversion problems in real-world scenarios. Unlike supervised learning approaches, semi-supervised learning does not require a large amount of labeled data, thus it can better handle situations of data scarcity or mismatch.

This paper utilizes a semi-supervised learning workflow to perform inversion on post-stack seismic data and has conducted experimental validation on the Marmousi 2 model. The experimental results show that, compared to supervised learning networks, the semi-supervised learning network still exhibits good predictive performance with a limited amount of data, demonstrating better stability in the presence of noise and geological variations, and effectively learns the mapping relationship between seismic data and artificial intelligence. Furthermore, as the amount of training data increases, the performance of the network also improves, confirming the importance of data quantity for training deep learning networks. The application results of the network on actual data indicate that the network has broad application prospects and feasibility. However, since the network is based on a channel-by-channel inversion method, there is still a lack of representation in terms of lateral continuity, which requires further exploration and improvement in subsequent research.

How to cite: zou, C., zhang, J., tang, B., and huang, Z.: Post stack inversion of seismic data based on Semi-supervised learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2973, https://doi.org/10.5194/egusphere-egu25-2973, 2025.

Seismic attribute analysis technology has been widely used in the prediction of fluvial reservoir sand body, but the traditional seismic attribute fusion technology based on linear model has low prediction accuracy and limited application range. This study focused on the non-linear fitting between seismic attributes and reservoir thickness, and used a variety of machine learning technologies to predict the fluvidal reservoir in Chengdao area of Dongying Sag (China).The channel sand body in Chengdao area is deep buried, thin in thickness, fast in velocity and affected by gray matter, so it is difficult to predict, which greatly restricts the oil and gas exploration in this area. In this study, on the basis of fine well earthquake calibration, several seismic attributes such as amplitude, frequency, phase, waveform and correlation are extracted and correlation analysis is done to remove redundant attributes. Then model training and parameter set optimization are carried out, thickness prediction is carried out with verification set, and vertical resolution is improved by logging reconstruction and waveform indication inversion. The results show that compared with the conventional support vector machine and back propagation neural network, the prediction accuracy of echo state network optimized by Sparrow algorithm is greatly improved. Based on the comprehensive prediction method of fluvial reservoir, three large channels developed in the lower part of Chengdao area and several small channels developed in the upper part of Chengdao area are effectively described. The research method can be used for reference to the similar complicated river facies prediction.

How to cite: Huang, Z. and Zhang, J.: Study and Case Application of Fluvial Reservoir Prediction Based on the Fusion of Seismic Attribute Analysis and Machine Learning Technologies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3391, https://doi.org/10.5194/egusphere-egu25-3391, 2025.

A strong earthquake with a magnitude of Mw 7.8 and a nearby epicenter in the city of Pedernales, Ecuador, occurred on April 16, 2016. This seismic event severely affected several cities in Ecuador, including Chone and Portoviejo, both in the Manabí province, located some 85 km and 150 km away from the hypocenter, respectively. In Chone, a total of 662 homes were damaged, while 2,678 collapsed dwellings were registered in Portoviejo, where 137 fatalities were reported. These, like most cities in the Manabí province, were built in narrow valleys over colluvial and alluvial soils.  The thickness of these sediments in contact with the rock is between 40 and 70 meters, which corresponds to both ancient and contemporary alluvial plains that are supported by alluvial-colluvial and alluvial valley-fill deposits. After the 2016 interplate subduction earthquake, the main co-seismic geological effects were reported for constructions built on these soils. Landslides were primarily documented in the colluvial soils, while soil liquefaction effects were reported in soft and loose soils. In this research, the influence of the presence of paleochannels in both cities, Chone and Portoviejo, on the liquefaction effects reported during the seismic event is analyzed.

The Chone River flows through Chone city from east to west, while its western part was modified after 1975, leaving an abandoned meander where the river channel was between 7 and 22 meters wide. The soil profile in this area demonstrates a low percentage of fines, ranging from 15 to 52%, with a relative density of about 50%, making it susceptible to liquefaction. After the 2016 earthquake, evidence of liquefaction effects was concentrated along the old meander. The Portoviejo River, which flows through the city of Portoviejo, has changed from a pronounced meandering shape in 1911 to its current form. This change spans about 4.5 km with a low slope between 0.1 and 0.2%. The width of the river has also been reduced, from 12 to 19 meters. The analysis of the liquefaction evidence indicates that the damage was very severe, especially in the constructions along the river.

The damage inventories performed in both cities have evidenced that paleochannels exhibited several signs of soil liquefaction. The geological and geotechnical conditions of these soils, such as size distribution, shallow groundwater table and recent-age deposits, may be considered as factors potentially increasing the probability of liquefaction. Therefore, a geomorphological study of the cities can help identify areas with a higher liquefaction potential.

How to cite: Pastor, J. L., Ortiz-Hernández, E., Toulkeridis, T., and Chunga, K.: Influence of paleochannels on liquefaction effects in the cities of Chone and Portoviejo (Ecuador) following the strong Pedernales earthquake in 2016, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3782, https://doi.org/10.5194/egusphere-egu25-3782, 2025.

This paper proposes a deep learning model based on 3D Convolutional Neural Networks (CNN) and a custom attention mechanism (ESSAttn) for seismic fault interpretation from 3D seismic data. The model combines the advantages of self-attention mechanisms and convolutional neural networks to enhance the ability to capture and represent features in three-dimensional seismic data. The core innovation of the model lies in the introduction of the ESSAttn layer, which applies a non-traditional normalization process to the input feature queries, keys, and values, thereby strengthening the relationships between features, especially in high-dimensional seismic data. Unlike traditional attention mechanisms, the ESSAttn layer normalizes feature vectors by squaring them and integrates features across depth, width, height, and channel dimensions, significantly improving the effectiveness of attention computation.

The model's role in seismic fault interpretation is reflected in several aspects. First, the 3D convolutional layers automatically extract spatial features from seismic data, accurately capturing the location and shape of faults. Second, the ESSAttn layer enhances critical region features and focuses attention on important areas such as fault zones, reducing the interference from background noise and significantly improving fault detection accuracy. Finally, by using a weighted binary cross-entropy loss function, the model can prioritize fault regions when handling imbalanced data, improving sensitivity to weak fault signals.

The network architecture consists of three main parts: encoding, attention enhancement, and decoding. Initially, two 3D convolutional layers and max-pooling layers are used for feature extraction and down-sampling, followed by the ESSAttn layer to enhance the extracted features. The decoding part restores spatial resolution through upsampling and convolution layers, ultimately outputting the fault prediction results. The model is trained using the Adam optimizer, with a learning rate set to 1e-4.

Experimental results show that the model performs well in seismic fault interpretation tasks, effectively extracting and enhancing fault-related features. It is particularly suitable for automatic fault identification and localization in complex geological environments. The model's automation of feature extraction and enhancement reduces manual intervention, increases analysis efficiency, and demonstrates strong adaptability to large-scale 3D seismic datasets. Furthermore, the model architecture was visualized and saved using visualization tools for easier analysis and presentation.

Keywords: 3D Convolutional Neural Networks, ESSAttn, Attention Mechanism, Fault Interpretation, Weighted Cross-Entropy, 3D Seismic Data, Deep Learning

How to cite: zhang, Y.: "Deep Learning Application for Seismic Fault Interpretation Based on 3D Convolutional Neural Networks and ESSAttn Attention Mechanism", EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4961, https://doi.org/10.5194/egusphere-egu25-4961, 2025.

How to cite: Motti, A.: Active and capable faults (FAC) and buildings in Norcia, interventions carried out and possibile technicolor and regulatory actions., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5076, https://doi.org/10.5194/egusphere-egu25-5076, 2025.

Coal seam floor water hazards, caused by stress changes resulting from coal mining, are a common type of mine water disaster, and their monitoring and prevention are critical for mine safety. The mine resistivity method, a geophysical exploration technique, is widely used for monitoring and detecting such water hazards due to its high sensitivity to water-bearing structures. In practical monitoring, it is necessary to rapidly and accurately invert apparent resistivity data. However, traditional linear inversion methods are prone to local optima, leading to biased results. In contrast, deep learning-based inversion methods utilize data mining to train networks, avoiding reliance on initial models and enabling fast computation of global optimal solutions.

This study constructs a multi-layer convolutional and skip-connected U-Net model to capture resistivity features at different scales. The model is trained and validated using synthetic data to evaluate its inversion accuracy and efficiency in monitoring coal seam floor water hazards. The results show that the U-Net-based inversion method can accurately identify low-resistivity anomalies associated with water hazards in the coal seam floor and quickly achieve the global optimal solution.

The method is further applied to the inversion of resistivity models with complex boundaries to simulate the impact of stress changes caused by coal mining on the formation of floor water hazards. The results demonstrate that this method is several times faster than traditional linear inversion methods, while maintaining high consistency with the actual model. Therefore, this inversion method provides an efficient new tool for monitoring coal seam floor water hazards and holds great promise for advancing technologies in mine water disaster prevention and geological exploration.

How to cite: Wang, H. and Hu, Y.: Research on mine electrical resistivity inversion method based on Deep Learning Method, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5528, https://doi.org/10.5194/egusphere-egu25-5528, 2025.

The Himalayan region, shaped by the ongoing collision of the Indian and Eurasian tectonic plates, is one of Earth’s most seismically active and geologically complex areas. The Indian plate moves northeastward at a rate of approximately 5 cm per year, driving tectonic activity in this region. Understanding earthquake source mechanisms in this region is crucial for seismic hazard assessment and geodynamic studies. Moment tensor (MT) inversion, a widely used technique for analysing earthquake faulting mechanisms, matches synthetic waveforms to observed data by minimising the misfit. However, conventional 1D velocity models often fail to capture the region’s complex lateral heterogeneities, leading to inaccuracies in source characterisation. Synthetic waveforms, generated via Green’s functions using frequency waveform (FK) methods and 1D velocity models, are critical for MT solutions, with time shifts playing a pivotal role in achieving optimal waveform correlations.

This study employs a 3D velocity model to improve MT inversion for a Mw 3.5 earthquake on 9 January 2021 (30.76°N, 78.54°E). Green’s functions were generated using the spectral element method for six simulations. Each simulation resulted in three-component waveforms, with a total of 18 synthetics per station. Observed data from 24 broadband stations were analysed, and results were compared to those obtained using 1D models. Slight variations in strike, dip, and rake values underscore the limitations of 1D models in capturing Earth’s heterogeneities.

The study reveals that 3D velocity models significantly enhance MT solution accuracy, particularly in determining focal depths, faulting mechanisms, and seismic moment magnitudes. A probabilistic approach was also applied to quantify the uncertainty associated with MT estimates, providing confidence measures. Extending this approach, MT inversion was performed for another earthquake in the Uttarakhand Himalaya using the same 3D velocity model, further demonstrating the advantages of 3D wavefield simulations in seismically active regions.

Keywords: Himalayas, Moment Tensor, Green’s Function, Spectral element method.

How to cite: Maurya, S., Silwal, V., Mahanta, R., and Yadav, R.: Earthquake Moment Tensor Inversion Using 3D Velocity Model in the Himalayas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6545, https://doi.org/10.5194/egusphere-egu25-6545, 2025.

The effect of heterogeneity (dissimilar materials) and geometry constituting an interface is an important problem in earthquake source mechanics. These two parameters in the fault interface are responsible for complex rupture propagation and instabilities compared to the homogeneous planar interface. Here, a boundary integral spectral method (BISM) is proposed to capture the in-plane rupture propagation in the non-planar bi-material interface. The conventional traction BISM suffers from the disadvantages of hyper singularity and regularisation is needed (Sato et al., 2020; Romanet et al., 2020; Tada and Yamashita, 1997). So, we are utilising the representation equation arising from the displacement formulation devised by Kostrov (1966). It uses the elastodynamic space-time convolution of Green’s function and traction component at the interface. These displacement boundary integral equations (BIEs) are the inverse equivalent of traction BIEs. When applied to an interface between heterogeneous planar elastic half-spaces, these displacement BIEs have yielded simple and closed-form convolution kernels (Ranjith 2015; Ranjith 2022). Displacement BIEs of this kind have not been utilised to analyse fracture simulation for non-planar bi-material interfaces until now. We assume the small slope assumption (Romanet et al., 2024) in our formulation to get the required displacement BIEs. Also, we expand the displacement BIEs of a non-planar bi-material interface to the leading order to obtain the non-planarity effects. Finally, we present a general spectral boundary integral formulation for a non-planar bi-material interface independent of specific geometry and traction distribution in a small fault slope regime.

How to cite: Kumar, S. and Kunnath, R.: Boundary integral spectral formulation for in-plane rupture propagation at non-planar bi-material interfaces, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8250, https://doi.org/10.5194/egusphere-egu25-8250, 2025.

In this study, we used the P-wave receiver functions (PRFs) to investigate the crustal structure of northern Morocco, located at the westernmost edge of the Mediterranean, near to the boundary between the African and Eurasian tectonic plates. This region is an integral part of the complex crustal deformation and tectonic system associated with the Alpine orogeny, characterized by concurrent compressional and extensional processes. These dynamics have led to the development of various structural and tectonic models aimed at explaining the area‘s geological evolution. The significant tectonic activity, evident in frequent seismic events, and complex lithospheric deformation, makes it an ideal location for studying crustal variations, lithospheric interactions, and mineralogical contrasts.

To achieve these objectives, we utilized high-quality seismic broadband data from the TopoIberia and Picasso seismic experiments, provided by the Scientific Institute, as well as from the broadband seismic stations operated by the National Center for Scientific and Technical Research (CNRST). The PRFs were extracted by decomposing teleseismic P-waves to isolate the effects of the local crustal structure. The dataset covers a wide range of regional stations, and the RFs provide detailed insights into crustal thickness, density and velocity contrasts, as well as deep discontinuities. Our preliminary results reveal significant variations in Moho depth, ranging from approximately 22.7 km in the eastern part of the region to 51.7 km in the western part. These variations correlate with changes in Vp/Vs and Poisson’s ratios, indicating mineralogical heterogeneity, with compositions spanning from mafic to felsic. These findings provide new constraints for tectonic models and enhance our understanding of the geodynamic processes involved, particularly the interactions between the crust and the upper mantle. This study not only improves our understanding of active tectonics and crustal composition in northern Morocco but also offers valuable insights for refining evolutionary models of the Western Mediterranean within its complex geodynamic context.

Keywords: Teleseismic event, P-wave, Receiver functions, Seismic Network, Vp/Vs ratio, Poisson ratio, Crustal structure, Mineralogical composition, Seismotectonics, Northern Morocco.

How to cite: Zakarya, H., El Moudnib, L., Badrane, S., Zeckra, M., and Lharti, S.: Continental Crustal Structure Beneath Northern Morocco Deduced from Teleseismic Receiver Function: Constraints into structure variation and compositional properties., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9078, https://doi.org/10.5194/egusphere-egu25-9078, 2025.

Here, we disentangle a complex disturbance deposit sequence attributed to the ~M 7 1873 CE Brookings earthquake from lower Acorn Woman Lake, Oregon, USA, using sedimentological techniques, computed tomography, and micro-X-ray fluorescence. The lower portion of the sequence is derived from schist bedrock and has characteristics similar to a local landslide deposit, but is present in all cores, suggesting that it is the result of high frequency (>5 Hz) ground motions from a crustal earthquake triggered the landslide. In contrast, the upper portion of the sequence is similar to a deposit attributed to the 1700 CE Cascadia subduction earthquake (two-sigma range of 1680-1780 CE): the base has a higher concentration of light-colored, watershed-sourced silt derived from the delta front followed by a long (2-5 cm) organic tail. The soft lake sediments are more likely to amplify the sustained lower frequency accelerations (<5 Hz) of subduction earthquakes, resulting in subaquatic slope failures of the delta front. The upper portion of the 1873 CE deposit, however, has an even higher concentration of watershed-sourced silt as compared to the 1700 CE deposit, which is suspected to be the result of shaking-induced liquefaction of the lake’s large subaerial delta. The tail of both the 1873 CE and 1700 CE deposits is explained as the result of flocculation that occurred during sustained shaking. A preliminary literature search suggests that flocculation may occur during low frequency (<4-5 Hz) water motion that is sustained for an extended period of time (~minutes). The subduction interpretation of the upper portion of the 1873 CE deposit is supported by the observation of a small local tsunami offshore and the presence of a possible seismogenic turbidite attributed to the 1873 CE Brookings earthquake in southern Oregon sediment cores.

These results are important to regional seismic hazards for several reasons. Southern Cascadia crustal earthquakes, not previously recognized as a threat in southern Oregon, have the potential to cause damage to infrastructure, including the Applegate dam and buildings and other structures at Oregon Caves National Monument. They also identify a previously unrecognized recent southern Cascadia subduction earthquake. Finally, the close temporal relationship between these two types of earthquakes, not observed elsewhere in the downcore record, may be early evidence of the transition of the Walker Lane belt into a transform fault as predicted.

How to cite: Morey, A. E., Shapley, M. D., Gavin, D. G., Goldfinger, C., and Nelson, A. R.: A complex deposit sequence from a small, southern Cascadia lake suggests a previously unrecognized subduction earthquake immediately followed a crustal earthquake in 1873 CE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11849, https://doi.org/10.5194/egusphere-egu25-11849, 2025.

Local heterogeneities on a steadily propagating crack front create persistent disturbance along the crack front. These propagating modes are termed as crack front waves. There have been numerous investigations in the literature of the crack front wave associated with a Mode I crack (for e.g., Ramanathan and Fisher, 1997, Morrissey and Rice, 1998, Norris and Abrahams, 2007, Kolvin and Adda-Bedia, 2024). It has been shown that the Mode I crack front wave travels with a speed slightly less than the Rayleigh wave. However, similar investigation of the Mode II rupture has got minimal attention. Although, Willis (2004) demonstrated that for a Poisson solid, Mode II crack front waves do not exist for crack speeds less than 0.715, explicit results on the speed of the crack front waves, when they exist, have not been reported in the literature. The focus of the present work is on a numerical investigation using a recently developed spectral boundary integral equation method (Gupta and Ranjith, 2024) to obtain the speed of the Mode II crack front waves. Further, the perturbation formulae for Mode II crack, developed by Movchan and Willis (1995) are exploited to validate the numerical results on the crack front wave speeds.

How to cite: Mouli, Y. S. C. and Kunnath, R.: Crack front waves under Mode II rupture dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14737, https://doi.org/10.5194/egusphere-egu25-14737, 2025.

In Greece, almost all accelerometer stations provided accelerometer recordings, more than 400 in total, are characterized by inferred Vs30 values based on combination of surface geology and slope proxy (Stewart et al. 2014). However, only about 15% of them have been characterized by in-situ geophysical and geotechnical methods (invasive or/and non-invasive) were performed at a distance less than 100m from the station. In addition, regarding reference rock stations where shear wave velocity Vs30 is equal or greater than 800m/sec (engineering bedrock), only five (5) of them have been characterized todate, with respective values ranging between 800Vs301183m/s. It is evident that measured site characterization parameters of accelerometer stations in Greece is far from a desired goal, especially regarding those on rock reference sites. In this study multiple/combined non-invasive passive and active seismic techniques are applied in six (6) accelerometer stations throughout Greece, to improve earthquake site characterization metadat of the national accelerometer network, focusing on stations placed on geologic rock conditions. The Vsz (S-wave) and Vpz (P-wave) profiles and thereby Vs30 site class according to the Eurocode-8 are determined. In addition, to form a holistic picture of the site’s characterization, surface geology and topographic properties are provided for the investigated stations. Results of this study aim at contributing on improving site characterization parameters estimated by the Generalized Inversion Technique (source, path, site), as well as in defining Ground Motion Models for rock site conditions.

How to cite: Theodoulidis, N., Hollender, F., Rischette, P., Buscetti, M., Douste-bacque, I., Grendas, I., and Roumelioti, Z.: Characterization of selected “rock” reference stations of the Hellenic Accelerometer Network (HAN), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16647, https://doi.org/10.5194/egusphere-egu25-16647, 2025.

The sedimentary architectures of paleo-river channels and their associated floodplains play a crucial role in shaping alluvial aquifers. Meandering point bars, known for their high permeability, enhance groundwater recharge, while floodplains serve as natural filters, regulating both the vertical and lateral movement of groundwater. Geophysical methods, particularly Ground Penetrating Radar (GPR), facilitate high-resolution imaging of subsurface features, allowing for detailed mapping of sedimentary structures and hydrogeological characteristics. This study focuses on a paleo-meandering point bar and its adjacent floodplain deposits within a heterogeneous alluvial aquifer in North 24 Parganas, West Bengal. Four GPR survey sites were analyzed, three along the meandering channel axis and one on the adjacent floodplain, using 200 MHz and 80 MHz antennas to capture subsurface features up to a depth of 20 meters. Six radar facies (RF) and three types of radar bounding surfaces (RS) including chute channels, lateral accretion surfaces, and erosional surfaces were identified, corresponding to various sedimentary lithofacies. Towards the meandering apex, the paleochannels displayed well-defined, continuous, and horizontal subparallel RF indicative of top silty clay deposits that increase in thickness. In contrast, wavy, inclined, sub-horizontal RF indicates channel sand deposits, which exhibit a decrease in thickness toward the meander apex. The GPR profiles of the floodplain revealed sub-horizontal laminated RF, typical of finer silt and clay deposits at greater depths. The comparison of RF and RS at different scales highlights distinct depositional patterns between meandering channel deposits and floodplain sediments. This study emphasizes the importance of integrating multi-frequency GPR data to interpret sedimentary processes in fluvial-sedimentary environments, providing valuable insights into the sedimentary architecture and hydrogeological properties of the paleo-meandering system.

How to cite: Dutta, A. D., Gogoi, H., Bose, O., Bhowmik, T., Sengupta, P., and Mukherjee, A.: Characterizing Sedimentary Facies of Meandering Paleochannel and Floodplain Deposits using Multi-Frequency Ground Penetrating Radar: A Case Study from the Western Part of Bengal Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18491, https://doi.org/10.5194/egusphere-egu25-18491, 2025.

Ambient noise surface wave imaging has become a powerful tool for mapping subsurface velocity structures. Recent advancements in seismology, including the widespread deployment of high-density arrays such as nodal seismometers and Distributed Acoustic Sensing (DAS) systems, have facilitated the use of subarray-based methods for surface wave dispersion data extraction, such as phase-shift, F-K, and F-J methods. Alternatively, dispersion data can also be derived from two-station approaches, such as the FTAN method. However, integrating dispersion data extracted from subarrays and two-station methods remains challenging. In this study, we propose a joint inversion framework that combines these two types of surface wave dispersion data to achieve improved constraints on subsurface structures. We demonstrate its accuracy and practical applicability by conducting numerical experiments and applying the method to field data. The proposed approach introduces intrinsic spatial smoothing constraints. It effectively integrates subarray and two-station dispersion measurements, resulting in better imaging of subsurface shear-wave velocity structures compared to using either dataset alone. The versatility and potential of this method highlight its promising applications in a wide range of geophysical scenarios.

How to cite: Luo, S.: Joint inversion of surface wave dispersion data derived from subarrays and two-station methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20181, https://doi.org/10.5194/egusphere-egu25-20181, 2025.

The Sargur Schist Belt (SSB), the oldest supracrustal greenstone belt, present in the south-eastern part of the Western Dharwar Craton (WDC), is a ~ 320 km long N-S to NNE-SSW trending discontinuous belts that occurs as patches and pockets within the granitic‒gneissic complex. The SSB is mainly composed of metamafic, metaultramafic, metapelite, banded magnetite quartzite, micaceous quartzite, pyroxene granulite, amphibolite, hornblende-biotite schist/gneiss, etc. The schistose belt has undergone at least five deformations in which the last three are very prominent. The N-S trending high strain zones with S₄ mylonitic foliation were produced during the EDC-WDC accretion (D₄ deformation). The D₅ deformation (developed due to the accretion of the WDC to Southern Granulite Terrane (SGT) along the Moyar/Bavali Shear Zone (BSZ)) developed broad open folds/warps in the N-S trend of the SSB (as well as WDC) with E-W trending axial planes. On a regional scale, the D₃ fold axes curve into the WNW-striking BSZ (D₅ deformation), a steeply dipping transpressional shear zone with dextral kinematics.

The estimated metamorphic P-T conditions of 440-585 °C and 6.0-9.5 kbar in metapelites from north to south and 640-770 °C and 7-10 kbar in granulites present in south only. The grade of metamorphism varies from greenschist facies in the north to upper amphibolite to granulite facies in the south. The metapelite and pyroxene granulite shows a loading and slow cooling path. The top to the north movement along the BSZ thrusted the high-grade metapelites, mafic-ultramafic rocks and granulite facies rocks over the WDC lithologies. The higher grade of metamorphism along the southern part as compared to the rest of the WDC is due to its location close to the WDC-SGT accretion zone. The zircons from the metapelitic schist provided older age population ranging between 3.3-3.2, 3.1-3.0 Ga followed by 2.9-2.7 Ga and 2.55-2.4 Ga, whereas the granulites (2.5 and 2.4 Ga) and foliated granites (2.6 Ga) yielded only the younger age populations. However, the monazites in schistose rocks located along the northern part recorded the oldest ages up to 2.7 Ga followed by 2.4 and 2.2-2.1 Ga ages. The monazites from foliated granites, irrespective of their location, provided ages of 2.53, 2.36 and 2.24 Ga. However, the monazites in schists and granulites from the southern part provided younger ages of 0.77, 0.67, 0.53 Ga. The prominent 0.84, 0.76 and 0.62 Ga monazite ages obtained from the metapelites close to the BSZ suggests that the accretion along the BSZ initiated in Mid-Neoproterozoic and continued till Early-Paleozoic. 

How to cite: Swain, M. and Rekha, S.: Structure, metamorphism and geochronology of Archean Sargur Schist Belt, southern India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-941, https://doi.org/10.5194/egusphere-egu25-941, 2025.

This study analyses shear wave splitting measurements for core-refracted SKS and SKKS phases using data from nine strategically positioned seismic stations operated between 2023 to 2024 in the Central Indian Tectonic Zone (CITZ). The CITZ was formed during the mesoproterozoic orogeny in central India, resulting from the collision of the northern Bundelkhand Craton with a jumble of South Indian cratons (Dharwar, Bastar and Singhbhum Cratons). Understanding seismic anisotropy in this region is essential for elucidating mantle deformation patterns, which provides vital insights into geodynamic processes, lithospheric interactions, and ongoing tectonic activities shaping the CITZ. We employed both rotation-correlation and transverse energy minimisation techniques to determine the shear wave splitting parameters, namely the fast polarization directions (FPDs) and splitting delay times (δt). A total of 104 high-quality splitting measurements and 37 null measurements were obtained from 85 earthquakes (M ≥ 5.5) within epicentral distances of 84°-145° for SKS phases and 84°-180° for SKKS phases. The averaged δts at each seismic station ranges from 0.8 to 1.3 seconds, demonstrating significant anisotropy and heterogeneity in the upper mantle under the studied region. Our observations predominantly reveal NE-SW FPDs throughout the majority of stations, which correlate with the Absolute Plate Motion (APM) of the Indian plate. The discrepancies between FPDs and APM direction at some stations suggest the presence of fossilised anisotropic fabrics resulting from prior subduction events during mesoproterozoic. The smaller δt (0.8 sec) at the seismic station in the Pachmarhi region may be attributed to the significant magmatism during the cretaceous period. Null measurements, in conjunction with splitting measurements, suggest that the stations may be located in a region characterized by multi-layered or complex anisotropy. Our observations indicate that the mantle flow beneath the CITZ is influenced by the contemporary APM direction of the Indian plate as well as lithospheric frozen anisotropy.

How to cite: Bishoyi, N., Tiwari, A. K., and Dubey, A. K.: Mantle Deformation Pattern Beneath Central Indian Tectonic Zone: A Seismic Anisotropy Study in Satpura Gondwana Basin and Surrounding Areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-964, https://doi.org/10.5194/egusphere-egu25-964, 2025.

A mineralogical study of early Cretaceous lamproite sill intrusions from the Raniganj Gondwana sedimentary basin in eastern India shows that apatite occurs as both phenocrystic and groundmass phase. Based on texture and compositional zoning patterns of apatite in lamproites from the Rajpura and Ramnagore collieries, three paragenetic stages of apatite are identified. Early-magmatic apatite (Ap-I), which forms the core of zoned grains, is Sr-rich–LREE-poor fluorapatite. This apatite underwent resorption prior to the growth of a second generation of magmatic fluorapatite (Ap-II). In Rajpura, Ap-II overgrowth rim is richer in Sr and LREE compared to Ap-I core. The increase in LREE is explained by the substitutions: (Na,K)⁺ + ∑LREE³⁺ = 2Ca²⁺, and [2∑LREE³⁺ + ₶ = 3Ca²⁺]. Ramnagore Ap-II overgrowth rim is oscillatory-zoned with fluctuations in Sr and LREE, which likely resulted from slow rate of diffusion of these elements relative to fast growth of crystals. Apatite of the third generation (Ap-III) forms the outermost rim of zoned grains and is marked by enrichment in Na, K and Ba. The substitutional schemes which explain the increase in Na and K from Ap-II to Ap-III are: (Na,K)⁺ + CO₃^2– = Sr²⁺ + PO₄^3– and [(Na,K)⁺ + (F,OH)^– = ₶ + ₶]. The role of carbonate in the former substitution is supported by high content of stoichiometrically calculated carbon (0.21–0.30 apfu) in Ap-III. The formation of Ap-III is attributed to metasomatic alteration of Ap-II by CO₂-bearing hydrothermal fluid and is associated with sodic metasomatism. Microporous texture has developed in Rajpura Ap-III which suggests a dissolution–reprecipitation mechanism for its development. This study demonstrates that compositional variations among different generations of apatite provide a meaningful record of melt evolution from early magmatic to magmatic-hydrothermal stages.

How to cite: Saini, J., Patel, S. C., and Kaur, G.: Apatite compositional constraints on the magmatic to hydrothermal evolution of lamproites from Raniganj Basin, eastern India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1988, https://doi.org/10.5194/egusphere-egu25-1988, 2025.

The Neoproterozoic western margin of the Yangtze Block in South China records significant continental crust-forming and modification processes through two distinct magmatic episodes. Using integrated geochemical and petrological approaches, we demonstrate that the 811-802 Ma Yuanmou Complex comprises alkaline high-Nb mafic rocks characterized by high Nb (15.7-41.9 ppm), TiO₂ (2.13-3.39 wt%) contents and positive ε_Nd(t) (+4.8 to +6.9), coupled with adakitic granodiorites showing high Sr/Y (17.4-49.0), (La/Yb)_N (16.3-52.6) and consistent bulk rock ε_Nd(t) (-0.5 to -1.5) and zircon ε_Hf(t) (0.0 to +2.3). The younger 750 Ma Jinping I-type granites exhibit high SiO₂ (71.2-73.5 wt%) and alkalis contents, enriched LREE patterns and depleted isotopic signatures (ε_Nd(t): -0.4 to +1.3; zircon ε_Hf(t): +4.83 to +8.37). Thermodynamic modeling reveals how crustal water content-controlled magma generation at different depths - low water-fluxed melting (2.0-3.5 wt% H₂O) produced I-type granites at medium pressure (6-9 kbar), while deeper settings with higher water content generated adakitic melts. The high-Nb mafic rocks in the Yuanmou Complex, derived from metasomatized mantle wedge, provide evidence for crustal-mantle interaction during back-arc extension. These coupled magmatic processes demonstrate how water content variations with depth influenced continental crust formation and evolution in arc settings.

How to cite: Huang, B., Wang, W., Zhao, J., Khattak, N. U., Li, R., Huang, S.-F., Lu, G.-M., Sun, L., Xue, E.-K., Zhang, Y., and Cai, X.-Y.: Water-fluxed melting and back-arc extension in the continental arc: Evidence from I-type granites, adakitic rocks and high-Nb mafic rocks at the western margin of the Yangtze Block, South China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7536, https://doi.org/10.5194/egusphere-egu25-7536, 2025.

A petrological and geochemical study was performed on 5 selected samples of peridotites from two different sites (Finero and Balmuccia) outcropping in the Ivrea Verbano Zone, with the aim to investigate the processes occurring in the deep lithosphere and the possible interaction with the lower crust.

The peridotites from Finero area fall in the harzuburgite (FIN1, FIN3, FIN4) field whereas those from Balmuccia are lherzolithes (BALM1) and werlhites (BALM4), highlighting respectively the presence of a more fertile and primordial mantle for two sites.

The rocks from Finero are featured by higher MgO (42-45.7 wt%) and lower Al₂O₃ (0.6-2.4 wt%), CaO (0.42-2.09 wt%) content with respect to Balmuccia (MgO: 39.6 wt%, Al₂O₃: 2.9 wt%; CaO: 2.8 wt%) as a consequence of their harzburgitic nature. They display an enrichment in large-ion lithophile elements (LILE), light rare earth elements (LREE, La_N/Yb_N:13.6) and depletion in high field strength elements (HFSE) differently from the Balmuccia peridotites, which are featured by a light depletion in LREE (La_N/Yb_N:0.4-0.8) and nearly flat HREE pattern. The LILE and LREE enrichment measured in the Finero peridotites could suggest that a portion of the mantle below Ivrea Verbano area was influenced by metasomatic fluids/melts. The BALM4 sample is characterized by anomalously low values of MgO (16.05 wt%) and high values of Al₂O₃ (16.3 wt%) and CaO (14.5 wt%), reflecting the high modal proportion of spinel.

Even the higher Sr (⁸⁶Sr/⁸⁷Sr= 0.70736-0.72571) and lower Nd (¹⁴³Nd/¹⁴⁴Nd=0.51236) isotopic values measured in selected mineral phases from Finero with respect to Balmuccia (⁸⁶Sr/⁸⁷Sr= 0.70268-0.70644; ¹⁴³Nd/¹⁴⁴Nd=0.51334) allow to speculate a relation with crustal fluids in the Finero mantle.

The composition of fluid inclusions entrapped in olivine and pyroxene crystals from Finero peridotites evidenced CH₄ and CH₄-N₂ associated with antigorite and magnesite whereas prevalent CH₄ associated with antigorite, magnesite and graphite was measured in the rocks from Balmuccia area. The origin of CH₄ could be related to synthesis via reduction of CO₂ by H₂ from internal/external serpentine to minerals or re-speciation of initial CO₂-H₂O fluids associated to graphite precipitation during cooling by obduction after orogeny; differently, the CH₄-N₂ fluids could be introduced by past subduction-related processes.

The isotopic helium (³He/⁴He ratio) varies between 0.08 and 0.17 Ra in the Finero peridotites and among 0.18 and 0.48 Ra in the Balmuccia ones, evidencing an isotopic difference between the two sites that cannot be explained by ⁴He radiogenic production. Differently, the Finero-Balmuccia variability could reflect the helium signature recorded in deep by subduction events and confirm the previous petrologic and geochemical evidences in favour of a metasomatised mantle by crustal fluids in the Finero area with respect to a more primordial in the Balmuccia one.

How to cite: Correale, A., Romano, P., Arienzo, I., Caracausi, A., Carnevale, G., Fazio, E., Mormone, A., Paonita, A., Piochi, M., Rotolo, S. G., and Zucali, M.: Two different mantle types as evidenced from a geochemical and petrological study of peridotites from the Ivrea-Verbano Zone , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8654, https://doi.org/10.5194/egusphere-egu25-8654, 2025.

The viscosity of silicate melts is one of the most important physical parameter governing natural processes such as volcanic eruptions, as well as manufacturing processes in the ceramic and glass industries. The traditional techniques for measuring viscosity are commonly time- and energy-consuming, they require equilibrium conditions, and are mostly limited to reduced viscosity intervals. Reducing testing time is a critical target for both academic and productive purposes. In order to calibrate an efficient tool capable of both reducing testing time and expand the range of viscosity determination, we used the hot stage microscope (HSM) technique. Specimens (pressed powders) of natural samples, previously measured employing a combination of concentric cylinder and the micropenetration dilatometric techniques, were heated at a rate of 10°C/min until melting. Characteristic shapes (Start sintering, End sintering, Softening, Sphere, Hemisphere, and Melting) were observed at characteristic temperatures (CT); then their viscosities were calculated from their known viscosity-temperature (Vogel-Fulcher-Tammann, VFT) relationships. The observed shapes result from a combined effect of viscosity and surface tension, allowing viscosity values at each CT to linearly scale with surface tension. Viscosity was calibrated by introducing correction factors based on glass chemistry. This approach provides two independent data sets – CT (from HSM) and the corresponding characteristic viscosity (from glass composition) – which can be used to calculate the VFT parameters. The comparison between calculated and experimental viscosity shows good correspondence, which significantly improved previous attempts using only HSM data. These results also highlight the potential of this non-contact technique for evaluating the effects of crystalline particles and porosity on the rheological properties of alumosilicate melts.

Contribution of PNRR M4C2 - PRIN 2022PXHTXM - STONE project, funded from EU within the Next generation EU program. CUP: D53D23004840006

How to cite: Giordano, D., Molinari, C., Dondi, M., Conte, S., and Zanelli, C.: A Method for Measuring Viscosity of Silicate Melts Using Hot Stage Microscopy (HSM), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8768, https://doi.org/10.5194/egusphere-egu25-8768, 2025.

Four multicomponent metaluminous glasses were designed to investigate the evolution of residual glass-ceramics comprising glass and crystals. Samples were obtained from melting of quartz-feldspars mixes (with varying Na/K ratio and silica content) further fast sintered at temperatures of 1200-1260°C. Using an integrated approach combining high- and low-frequency Raman spectroscopy and Differential Scanning Calorimetry (DSC), we characterized the viscous and elastic response of the residual glass and its role in the mechanical properties of the corresponding ceramic products.

High-frequency Raman spectroscopy allows for the analysis of Qn species, which represent the polymerization state of the glass network. Q⁰, Q¹, Q², Q³, and Q⁴ correspond to isolated tetrahedra, short chains, branched structures, and fully polymerized networks, respectively. This provides insights into how chemical composition affects the microscopic structure of the residual glass. Simultaneously, low-frequency Raman spectroscopy probes the boson peak, a signature of collective vibrational modes in the glass, which is directly linked to its elastic properties. By coupling the boson peak analysis with the elastic medium scaling law, we determine the vibrational density of states and shear modulus, key parameters for understanding the mechanical behavior of the system.

DSC measurements further enable the determination of critical thermal transitions of the glass, including the glass transition temperature, crystallization, and relaxation processes, which are essential for characterizing the viscous behavior of the residual glass. The integration of these techniques provides a comprehensive understanding of the role of residual glass in stress transfer and mechanical properties control within multicomponent ceramics.

This is a first insight on the characteristics of technologically relevant glasses for the production of porcelain and vitrified ceramic tiles. The approach here followed actually allows appreciating the effect of variations in the Na/K ratio and silica content that mirror what can occur in the industrial production. This paves the way for application in more complex materials and real industrial conditions.

Contribution of PNRR M4C2 - PRIN 2022PXHTXM - STONE project, funded from EU within the Next generation EU program. CUP: D53D23004840006

How to cite: Giordano, D., Cassetta, M., Conte, S., Zanelli, C., Molinari, C., Dondi, M., and La Felice, S.: Characterization of Residual Glass Evolution from Vitrified  Ceramics: Insights from Raman Spectroscopy and DSC into Viscous and Elastic Properties, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8978, https://doi.org/10.5194/egusphere-egu25-8978, 2025.

Investigating the main geochemical characteristics of the lower continental crust is essential to understand its formation and evolution, identifying crustal differentiation processes and possible crust-mantle interactions. We performed bulk rock (major and trace elements), noble gases isotopes (He, Ne, Ar), and fluid inclusions (Raman spectroscopy) analyses on metamorphic rocks from Ivrea-Verbano Zone (Southern Italian Alps). Specifically, we studied various lithologies (metapelite, metagabbro, mafic and felsic granulite, amphibolite, and gneiss) to analyse the continuous metamorphic gradient from amphibolite- to granulite-facies.

Bulk rock analyses confirm the mafic nature of the protoliths for metagabbros (MgO = 5.36-10.25 wt.%), mafic granulites (MgO = 8.32-25.80 wt.%) and amphibolite (MgO = 7.98 wt.%) plotting in the metabasite field of the ACF chemographic diagram. Felsic granulite and sillimanite-gneiss fall within metamorphosed quartz-feldspar rocks, except for metapelite, which approaches the metacarbonate field, due to the presence of secondary carbonates. Metagabbros, mafic granulites and amphibolite show low REE concentrations (∑REE between 3 and 25 ppm) and high Cr and Ni contents (up to 1865 and 265 ppm respectively in mafic granulite), reflecting the mafic/ultramafic nature of the protoliths, whereas felsic granulite, sillimanite-gneiss and metapelite show higher REE contents (∑REE between 48 and 197 ppm).

³He/⁴He isotope ratios in metamorphosed quartz-feldspar rocks (0.06-0.30 Ra) and metabasites (0.15 and 0.45 Ra) are significantly radiogenic, although the metabasites show slightly higher values, corroborating a more primitive component in their source. Most samples plot near the air component in the ²⁰Ne/²²Ne vs ²¹Ne/²²Ne diagram, except for mafic granulites which show a crustal-air mixing trend. As regards the Ar isotope ratios, all samples appear rich in radiogenic component (⁴⁰Ar/³⁶Ar up to 2645 in metagabbros).

Raman spectroscopy analyses on fluid inclusions in orthopyroxene from mafic granulites show the coexistence of talc, graphite and magnesite with methane, providing direct evidence of a complex history in terms of post-metamorphic reactions and P-T-fO₂ conditions.

Our preliminary results show the compositional diversity and evolution of the lower continental crust, highlighting the interplay between mafic and sedimentary sources and the importance of fluid interactions and post-metamorphic processes.

How to cite: Carnevale, G., Caracausi, A., Correale, A., Fazio, E., Paonita, A., Romano, P., and Zucali, M.: Geochemical characterisation of Permian lower continental crust: case study from Ivrea-Verbano Zone (NW Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9306, https://doi.org/10.5194/egusphere-egu25-9306, 2025.

Experimental data on rock physical properties obtained through laboratory methods are enhanced by advanced techniques like X-ray microtomography (µCT) and image analysis. Lava rocks are important geological formations worldwide with varying textures, structures, and physical and mechanical behaviour. This research focuses on the heterogeneity analysis of vesicular lava rocks with intermediate composition from the Fogo Volcano (or Água de Pau Volcano, S. Miguel, Azores, Portugal). The effective porosity of six cubic samples is determined using the buoyancy technique. Ultrasonic wave velocities and capillarity absorption coefficient are obtained along three orthogonal directions using the through-transmission method and a European standard, respectively. Unconfined compressive strength (UCS) combined with µCT is determined in three cores from a single cube.

Results demonstrate that pore structure governs water uptake by capillarity and ultrasonic wave velocities. Regardless of the direction, the nonlinear water imbibition reflects a bimodal pore size distribution, confirmed through µCT imaging. The Sharp Front model describes this behaviour as the sum of two separate absorption processes related to larger (28.01-12.96 g/m²·s^0.5) and finer (0.45-1.73 g/m²·s^0.5) pores. Capillary-connected porosity (5.07%) is lower than connected porosity (18.5–20.1%) since gravitational fluid transport dominates for large pores (>1 mm). P-wave velocities (2802–3208 m/s) show minor dependence on pore shape, while Vp/Vs ratios (1.76 ± 0.25), dynamic Young’s modulus (16.78 ± 3.20 GPa), and Poisson’s ratio (0.23 ± 0.11) reflect vesicular textures.

µCT-based image analysis enables porosity quantification, revealing that effective porosity includes vesicles and pore-linking fractures. Permeability (0.7–6.6 mD) depends on tortuosity, which reduces fluid percolation despite higher connected porosity.

UCS (15.5-36 MPa) variations depend on pore size, orientation relative to the loading direction, and connected porosity, with minor influence from pore shape. µCT imaging reveals failure through tensile splitting, with fractures propagating from pore edges in all cores. The weakest specimen has more plagioclase phenocrysts, whose borders, intragranular cracks, and pores contribute to reduced strength.

These findings underscore the need to consider the heterogeneous pore structure of vesicular lavas when interpreting field measurements or improving volcano stability models. Advanced imaging and computational techniques clarify the role of vesicles and phenocrysts in strength and crack development patterns, providing important insights into the mechanics of lava rocks.

How to cite: Pereira, M. L., Cueto, N., Pappalardo, L., Buono, G., Falasconi, A., Moreira, M., Zanon, V., and Fernandes, I.: Characterisation of the heterogeneity of vesicular lava rocks from Fogo Volcano (Azores, Portugal) combining conventional laboratory methods with X-ray microtomography, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9505, https://doi.org/10.5194/egusphere-egu25-9505, 2025.

In the last decades, the volcanically active Aeolian Islands have been the focus of numerous geochemical investigations and monitoring activities, primarily focused on the islands of Vulcano, Stromboli and Panarea. However, relatively few studies have explored the geochemical characteristics of other islands, despite evidence of hydrothermal activity. Salina, for instance, hosts a shallow, cold, low-salinity aquifer that overlies a deeper warmer aquifer, with highly saline water. Additional noteworthy features include hydrothermal deposits on the seafloor and offshore submarine gas emissions. Similarly, Lipari hosts a thermal aquifer (e.g. Terme di San Calogero) and exhibits significant hydrothermal emissions along its western coast, particularly in areas of Valle del Fuardo and Caolino quarry. In this study we conducted detailed geochemical surveys on Lipari and Salina to investigate the origins of the fluids and their relationship with the geodynamic framework. The research is part of the Project CAVEAT (Central-southern Aeolian islands: Volcanism and tEArIng in the Tyrrhenian subduction system), which aims to provide a comprehensive understanding of the current geodynamics in the southern Tyrrhenian region, focusing on the interaction between volcanism and tectonic activity within the Tyrrhenian subduction system.

On Salina and Lipari islands, soil CO₂ flux measurement campaigns were conducted to examine the spatial distribution of soil CO₂ emissions. Thermal surveys using an Unmanned Aircraft System were conducted over fumarolic areas to detect thermal anomalies associated with zones of preferential fluid emissions. These measurements helped define preferential pathways for fluid migration and identify active tectonic structures associated with areas of elevated soil CO₂ emissions. At selected sites, isotopic composition of gas was analyzed to infer the gas origins. On Lipari, soil CO₂ emission anomalies revealed a NNW-SSE alignment consistent with the area’s primary tectonic structures. Isotopic analysis confirmed a contribution of deep-origin fluids to these emissions. Thermal (up to 45.8 °C) and cold waters from Salina and Lipari were sampled and analyzed for their chemical and isotopic composition, as well as for dissolved gases. The isotopic composition of the water clearly indicates that the sampled groundwater originates from a mix of meteoric water and seawater, with varying degrees of mixing at each site. Gases dissolved in water exhibit an atmospheric component with a high content of CO₂ in the most brackish samples. At Salina, the isotopic composition of dissolved helium reflects a mantle contribution. Collectively, the findings emphasize the significant influence of mantle and deep-origin origin fluids in shaping the geochemistry of both islands. They further highlight the critical role of geodynamic and tectonic processes in governing fluid emissions across the two islands.

How to cite: Camarda, M., De Gregorio, S., Liotta, M., Di Martino, R. M. R., Oliveri, Y., Palano, M., Pisciotta, A., Riolo, G. M., and Romano, P.: Unveiling the geochemistry of fluids in the Central Aeolian Islands (Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10890, https://doi.org/10.5194/egusphere-egu25-10890, 2025.

During active-source 2D marine ocean bottom seismic exploration, significant deviations of shot lines from the designed survey lines can introduce errors in 2D structural models, particularly in areas with rough bathymetry, such as mid-ocean ridges. By employing 3D tomography, it is possible to construct a three-dimensional model of the survey area that incorporates the actual shot locations and Ocean Bottom Seismometer (OBS) positions, leading to more accurate velocity structure models.

In 2021, the Joint Arctic Scientific Mid-Ocean Ridge Insight Expedition (JASMInE) acquired high-quality OBS data from the Gakkel Ridge in the Arctic Ocean. However, due to the presence of dense floating ice, significant offsets occurred between the shot lines and the OBS station profiles. Consequently, applying a 3D tomography-based modeling approach is essential for imaging the velocity structure in this region.

This study utilized the JIVE3D software to develop a 3D P-wave velocity model along a profile perpendicular to the 85°E spreading axis of the Gakkel Ridge, based on high-resolution multibeam bathymetry data. Compared to the velocity structure derived from 2D modeling, the P-wave velocities beneath the spreading axis are found to be lower in the 3D model, while lateral velocity variations in the upper oceanic crust are more pronounced away from the spreading axis. Despite these differences, the overall velocity structure and crustal thickness trends are consistent, indirectly validating the reliability of the 2D structural model.

Based on this 2D P-wave model, with data of 1257 S-wave arrival times picked from 9 OBS stations along the profile perpendicular to the mid-ocean ridge, using a forward modeling trial-and-error approach, a preliminary Poisson’s ratio structure beneath the profile was obtained. The Poisson’s ratio in Layer 2 of the oceanic crust ranges from 0.36 to 0.40, with relatively lower values beneath the spreading axis. In Layer 3, the Poisson’s ratio varies from 0.28 to 0.38. The relatively higher Poisson’s ratio values may indicate the presence of abundant fractures or fluids within the oceanic crust in this region.

How to cite: Niu, X., Li, J., Yang, W., Yu, J., Ding, W., and Zhang, T.: Poisson’s ratio structure and three-dimensional P wave velocity structure beneath the profile across the Gakkel ridge 85°E axis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11736, https://doi.org/10.5194/egusphere-egu25-11736, 2025.

Natural CO₂ emissions play a crucial role in understanding global CO₂ budget estimates. Consequently, numerous studies have focused on CO₂ emissions across various regions worldwide. However, the majority of these investigations have concentrated on terrestrial CO₂ emissions, with relatively fewer studies exploring submarine CO₂ emissions. Moreover, almost all the studies have focused on areas with significant hydrothermal activity, particularly those along Mid-Oceanic Ridges, while shallow-water hydrothermal vents have received comparatively little attention. Furthermore, diffuse submarine gas emissions, lacking or with little visible surface evidence, remain largely unexplored. This study investigates the CO₂ emissions in the shallow submarine environment around the coast of the Island of Vulcano (Aeolian Islands, Italy) by measuring dissolved CO₂ concentrations. Vulcano, has been characterized by an intense hydrothermal activity since its last eruption from La Fossa cone (1888-­1890). Vulcano features several fumarole fields, including one on the northern crater rim of La Fossa cone and another near the sea in the northeastern sector. Additionally, significant soil CO₂ degassing occurs across the volcanic edifice. In the Vulcano Porto area, numerous thermal wells discharge fluids with temperatures reaching up to 80 °C. Submarine emission areas are visible, at shallow depths, close to the beaches in the southern and northeastern sectors. Measurements of dissolved CO₂ concentrations were conducted along seashores and rocky coastlines and in sites encompassing both visible and non-visible emissions. In the northeastern sector, measurements focused on the area between the Vulcanello peninsula and the northern slopes of the volcanic cone. The northernmost section of this area, extending to the Faraglione cone, is widely recognized in the literature as Baia di Levante (BL), a well-documented site of significant CO₂-dominant hydrothermal fluids discharge, trough submarine vents placed on the seafloor, at shallow depth, near the shoreline. In this area, we performed measurements along the beach at depth of about 50 cm below sea surface. The measured values remain elevated throughout the entire profile, consistently surpassing those of seawater in equilibrium with the atmosphere (ASSW). Concentrations peaked near visible bubbling zones, with concentration values ​​that exceeded the 20%. Moving southward, between the port dock and the crater slopes, measurements were conducted both close to the coastline and approximately 30 meters off the coast. In this area, sporadic bubble emissions from the seafloor were observed and the concentration of dissolved CO₂ decreases significantly compared to the BL area. However, the dissolved CO₂ concentration remain elevated, above those expected for ASSW. Along the eastern coast, measurements were performed in two selected sites along the rocky coastlines. Anomalous dissolved CO₂ concentrations, reaching up to 1400 ppm, were recorded also in these areas. In the southern sector, measurements were taken along Gelso beach. CO₂ concentrations were consistently high along the entire beach profile. The results indicate that submarine CO₂ emissions are not confined to areas with visible surface evidence, but also occur in areas with minimal or no-visible hydrothermal activity.

How to cite: De Gregorio, S., Camarda, M., Cappuzzo, S., Francofonte, V., and Pisciotta, A.: Investigations on the shallow submarine CO2 emissions around the Island of Vulcano (Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11968, https://doi.org/10.5194/egusphere-egu25-11968, 2025.

Correlating surface hotspot volcanism with sharply defined edges of Large Low Shear Velocity Provinces (LLSVPs) is a common yet potentially oversimplified approach in mantle geodynamics. Such direct radial projections ignore the lateral displacement of plume conduits observed in seismic tomographic imaging, which suggests that purely vertical transport through the mantle is not guaranteed. Furthermore, many studies merge the African and Pacific LLSVPs, despite evidence that their correlation with hotspots differs significantly. These oversimplifications can lead to misinterpretations of plume-lithosphere interactions, the interaction between mantle plumes and the ambient ”mantle wind”, and mantle flow dynamics in general. Here, we systematically investigate how varied criteria can alter the inferred hotspot– LLSVP edge relationship. We separately analyze African and Pacific LLSVPs using: multiple tomography models, horizontal-gradient based definitions of edges, different vote-map methodologies, and distinct plume geometry assumptions–from perfectly vertical “spokes” to randomly deflected trajectories. We also apply the Back-and-Forth Nudging (BFN) method applied to time-reversed thermal convection, initialized with a present-day seismic–geodynamic–mineral physics model (Glisovic & Forte, 2016), to provide a geodynamically consistent assessment of the relationship between present-day hotspot locations and their source regions in the deep lower mantle. This independent geodynamic assessment clarifies how arbitrary choices concerning the interpretation of hotspots and LLSVP edges may lead to biased or skewed deep-plume reconstructions. Our results reveal that adjustments in hotspot catalogs, or the decision to combine the two main LLSVPs rather than regard each as dynamically distinct, can yield important differences in the significance attributed to sharply defined LLSVP edges. These findings underscore that commonly cited correlations between hotspot locations and LLSVP boundaries hinge on assumptions that vary considerably across the literature. Recognizing and rigorously defining input parameters–particularly the separate treatment of the African and Pacific LLSVPs and the inclusion of realistic lateral plume deflection–proves essential for robust interpretations of deep Earth structure. This highlights the need for standardized methodologies and careful parameter choices to avoid overstating the importance of LLSVP edges in shaping plume pathways.

How to cite: Johnston, G., Liu, S., Forte, A., and Glisovic, P.: Playing with Edges: The Influence of Arbitrary Definitions on Hotspot–LLSVP Correlations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14815, https://doi.org/10.5194/egusphere-egu25-14815, 2025.

The 2023-2024 eruptions at Sundhnúksgígar in Iceland produced tholeiitic basaltic lavas that traveled at high velocities, affecting vast areas. In this context, disequilibrium crystallization can play a fundamental role in modulating the lava flow dynamic and inundation capacity. To investigate this phenomenon, we performed a comprehensive rheological characterization of the Sundhnúksgígar basaltic liquid and crystal-bearing suspension under both disequilibrium and near-equilibrium conditions. Compared to other basalts erupted worldwide, our results reveal unique features of the Sundhnúksgígar melt: i) exceptionally low solidification rates and ii) the ability to crystallize even at the highest cooling rates applied during the experiments. These characteristics enhance the efficiency of external crust formation, minimizing heat loss from the inner portion of the lava flow, which consequently experiences slower cooling rates. As a result, the lava is able to flow for longer times and travel greater distances than other basaltic flows. Our findings underscore the critical influence of disequilibrium crystallization on the rheological evolution and emplacement behavior of basaltic lavas, with implications for hazard assessment and risk mitigation during effusive eruptions.

How to cite: Di Fiore, F., Vona, A., Di Genova, D., Caracciolo, A., Pontesilli, A., Calabro', L., Giuliani, G., Mollo, S., Bondar, D., Nazzari, M., Romano, C., and Scarlato, P.: Impact of Cooling Rate on Rheology and Emplacement Dynamics of Basaltic Lava Flows: Insights from the 2023-2024 Sundhnúksgígar Eruption (Iceland), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17683, https://doi.org/10.5194/egusphere-egu25-17683, 2025.

This study focuses on characterizing seismogenic zones by establishing a interrelationship between electrical and elastic properties using Effective Medium Theories (EMTs). The seismogenic zones exhibit complex geological and geophysical signatures that can be explored through joint analysis of electrical resistivity and elastic moduli. The research applies EMTs such as Self-Consistent Approximation (SCA), Generalized Effective Medium (GEM), and Differential Effective Medium (DEM) to model the physical properties of rocks under varying conditions of pressure, porosity, and fluid saturation.

The study compares theoretical predictions with observed data to understand how resistivity, influenced by fluid connectivity and composition, correlates with elastic properties, which are sensitive to stress and fracture networks. The study can reveal critical insights into the mechanical and fluid characteristics of seismogenic zones. By integrating theoretical models with available geophysical data, this work provides a framework for analyzing the interdependence of electrical and elastic properties in seismogenic regions. The findings contribute to advancing the understanding of fluid dynamics, and rock deformation in seismogenic zones, offering a valuable tool for seismic hazard assessment and monitoring.

How to cite: Raju, K. and Siniscalchi, A.: Interrelationship between the electrical and elastic properties using effective medium theories, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17921, https://doi.org/10.5194/egusphere-egu25-17921, 2025.

The Istituto Nazionale di Geofisica e Vulcanologia - Osservatorio Etneo (INGV-OE) is in charge to monitor Mt. Etna (Catania, Italy), one of the most active volcanoes in Europe. Its activity is characterised by mild strombolian to powerful lava fountains. Monitoring active volcanoes is fundamental to reduce the volcanic hazard, in particular in dense populated areas as it is the case for the Mt. Etna [1]. The combination of different remote sensing systems can improve the analysis of Etna volcanic activity and give a more reliable quantification of volcanic source parameters as the Cloud Height, Mass Eruption Rate, Fine ash Mass and Particle Size. Volcanic source parameters are used as input parameters by volcanic ash transport and dispersal model. A more accurate estimate of these parameters reduces the uncertainty of numerical dispersal model simulations. The data used for this study come from different sources: The VIVOTEK IP8172P is a visible camera located in Catania. The second is a Thermal-Infrared camera located in Nicolosi that collects images (320 x 240 pixels) at few meters resolution [2] [3]. The third instrument is a X-band (9.6 GHz) polarimetric weather radar located nearby the International Airport Vincenzo Bellini (Catania). The fourth is the Spinning Enhanced Visible and Infrared Imager onboard the Meteosat Second Generation Geostationary Satellite [4]. Through the use of complementary remote sensing systems, we aim at improving our understating of explosive phenomena at Etna volcano.

[1] Bonadonna, C., Folch, A., Loughlin, S., & Puempel, H. (2012). Future developments in modelling and monitoring of volcanic ash clouds: outcomes from the first iavcei-wmo workshop on ash dispersal forecast and civil aviation. Bulletin of volcanology, 74 , 1–10.

[2] S. Scollo, M. Prestifilippo, E. Pecora, S. Corradini, L. Merucci, G. Spata, et al., "Eruption column height estimation of the 2011–2013 Etna lava fountains", Ann. Geophys., pp. 57, 2014.

 [3] S. Calvari, G.G. Salerno, L. Spampinato, M. Gouhier, A. La Spina, E. Pecora, et al., "An unloading foam model to constrain Etna’s 11–13 January 2011 lava fountaining episode", J. Geophys. Res. Solid Earth, vol. 116, pp. B11207, 2011.

[4] S. Scollo, M. Prestifilippo, C. Bonadonna, R. Cioni, S. Corradini, W. Degruyter, et al., "Near-Real-Time Tephra Fallout Assessment at Mt. Etna Italy", Remote Sens., vol. 11, pp. 2987, 2019.

How to cite: Romeo, F., Mereu, L., Prestifilippo, M., and Scollo, S.: Etna volcano monitoring by remote sensing systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20022, https://doi.org/10.5194/egusphere-egu25-20022, 2025.

Deception Island, the most active volcanic system in the South Shetland Islands (Antarctica), has recorded over 20 explosive monogenetic eruptions in the past two centuries. The island’s most recent eruption in 1970 was one of its most violent, with a Volcanic Explosivity Index (VEI) of 3. This event generated a column height of up to 10 km and produced an estimated bulk eruptive volume exceeding 0.1 km³, with tephra fallout recorded over 150 km away on King George Island. To investigate the magmatic processes leading up to this significant eruption, we conducted detailed geochemical and textural analyses of near-vent pyroclastic deposits and distal tephra fall-out layers preserved in Livingston Island’s glaciers. Near-vent deposits include dilute pyroclastic density currents (PDCs) and lithic-rich breccias. Olivine crystals in these deposits exhibit two distinct populations: low-forsterite (Fo_65–70 mol.%) and high-forsterite (Fo_80–85 mol.%), with similar CaO contents (0.1–0.5 wt.%) but varying NiO concentrations (0–0.4 wt.% in low Fo; 0.02–0.10 wt.% in high Fo). Pyroxene microanalyses also reveal two distinct populations: i) augite-diopside (En_45–50, Fs_5–25, Wo_38–50) and ii) enstatite (En₉₀, Fs₁₀, Wo₀). Augite-diopside crystals can be further subdivided based on their Mg# (Mg# = Mg/(Mg+Fe) x 100) and TiO₂ contents. The first group shows Mg# values between 80–85 mol.% and TiO₂ ranging from 0.5 to 3.0 wt.%, while the second group displays Mg# values of 55–70 mol.% and narrower TiO₂ concentrations (0.5–1.25 wt.%). Notably, the enstatite population was not found in distal tephra layers. Plagioclase crystals range in composition from Bytownite to Andesine (An_85–40 mol.%). Comparative analyses with distal tephra layers confirm the presence of both olivine populations and overlapping augite-diopside compositions but lack enstatite. Plagioclase compositions show consistency between near-vent and distal deposits. These findings align the 1970 eruption deposits with compositional trends observed in other post-caldera collapse eruptions, shedding light on the island's eruptive history and magmatic evolution.

This work has been partially financed by the grant PID2023-151693NA-I00 funded by MCIN/AEI/10.13039/501100011033.This work is part of the CSIC Interdisciplinary Thematic Platform (PTI) Polar zone Observatory (PTIPOLARCSIC) activities. This research was partially funded by the MINECO VOLCLIMA (CGL2015-72629-EXP) and HYDROCAL (PID2020-114876GB-I00) MICIU/AEI/10.13039/501100011033 research project. Sampling was founded by CICYT (ANT91-1270, ANT93-0852 and ANT96-0734) and MICINN grant CTM2011-13578-E.

How to cite: Albert, H., Ruiz, J. L., Hopfenblatt, J., Pedrazzi, D., Geyer, A., Aulinas, M., Polo-Sánchez, A., Álvarez-Valero, A. M., and Vilanova, O.: Magmatic processes driving the 1970 eruption on Deception Island, (Antarctica), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20055, https://doi.org/10.5194/egusphere-egu25-20055, 2025.

Vulcano Island in Aeolian Archipelago last erupted in 1888-1890 and since then it is affected by an intense fumarolic activity from both the summit crater area of La Fossa volcano and by the hydrothermal system of Baia di Levante located very near to the main settlement of Vulcano Porto.  Ordinary solfataric activity is periodically interrupted by unrest crisis at La Fossa crater associated with increase in fumarole temperature and output, anomalous seismicity, ground deformation and accompanied by an increase in diffuse soil CO₂ degassing at Vulcano Porto. In Autumn 2021 a new major unrest crisis began exposing to a high gas hazard Vulcano Porto settlement due to contemporary dispersion of crater fumarolic plume and diffuse soil CO₂ degassing; Starting from February 2022, with apex in May, a huge increase in gas output of the geothermal system of Levante Bay was observed. The Baia di Levante area is characterized by the presence of a low-temperature fumarolic field (<100°C) either onshore and offshore and fed by a shallow hydrothermal aquifer heated by magmatic gases. A wide diffuse soil CO₂ degassing area extends all over the main beach. The chemical composition of bubbling gases is CO₂-dominant, associated with a 1-3 vol.% of H₂S and minor CH₄ and H₂. The Bay is one of the main sites of attraction for the thousands of tourists who visit the island and given the increased risk for gas emissions and possible phreatic eruptions (due to overpressuration of the geothermal aquifer) we carried out some extraordinary geochemical surveys. These consisted of (i) estimation of diffuse soil CO₂ flux over a target area (154 points over 16,750 m²) established since 2004; (ii) estimation of the convective CO₂ and H₂S flux (the main hazardous gases) from the onshore (50 points in the Mud Pool and surrounding areas) and offshore gas vents (2 main large vents and 60 small vents); (iii) Repeated measurements of the chemico-physical parameters (temperature, pH, Eh, conductivity and dissolved O₂) in the Baia di Levante sea water (107 profiles; water depth from 50cm to 12m). In particular we investigated the areas characterized by the presence of whitish waters, trains of gas bubbles, emissive vents. Results shown significantly increased values ​​compared to the past of the total CO₂ and H₂S output (diffuse and convective) measured on land and at sea surface. The sea water shows the presence of a wide anomalous pH in the near-shore sector between Faraglione and Vent 1 and to a lesser extent to the N of the bay. A wide anomaly of negative Eh values ​​persist at all depths in almost all of the bay. A huge emissions of acid gases from the increased submarine fumaroles alter the chemical-physical parameters of the sea water along the bay. Considering the increased gas hazard the adoption of risk prevention measures was suggested to authorities.

How to cite: Ranaldi, M., Carapezza, M. L., Tarchini, L., Pagliuca, N. M., Pruiti, L., and Sortino, F.: Gas hazard assessment at the hydrothermal system of Baia di Levante at Vulcano Island during the 2021-23 unrest of La Fossa crater (Aeolian Islands, Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20387, https://doi.org/10.5194/egusphere-egu25-20387, 2025.

Natural microseismicity serves as a potent tool for exploring smaller-scale hydrothermal and tectonic phenomena. Investigating seismic activities within the hydrothermal fields of mid-ocean ridges(MORs) offers profound insights into earth's internal dynamics. However, studies on natural earthquakes at ultra-slow spreading ridges, especially the Southwest Indian Ridge (SWIR), remain relatively scarce. To investigate the microseismic distribution, heat flow pathways, and tectonic characteristics of the Longqi hydrothermal field, a typical representative of SWIR, this paper processed one month of passive source OBS data from the DY43 cruise through microearthquake detection and relocation, obtaining a catalog of over 3000 earthquakes, significantly expanding the earthquake database for the Longqi field and improving the magnitude completeness. And the b-value calculation and imaging of the earthquake catalog were carried out using the maximum likelihood method and grid search method, respectively. The research results indicate that: ① The overall b-value of the SWIR Longqi field is 0.989; ② The b-value at the center of the Longqi hydrothermal vent is approximately 0.8, while the b-value around the vent is around 1.2; ③ High and low b-value areas alternate at a depth of 10km along the ridge axis; ④ There is an anomalously low b-value area of around 0.5 at depths of 12-16 km to the north across the ridge axis. Combining previous research results on b-values at MORs, this paper suggests that the background b-value of less than 1 in the Longqi field is consistent with its tectonic-type hydrothermal origin. The detachment fault beneath the Longqi hydrothermal vent leads to high stress and a low b-value, while the microseismic activity around the vent originates from rock fracturing caused by the combined effects of cold seawater and hydrothermal fluids. The uneven distribution of high and low b-values in the deep part of the hydrothermal field may reflect the uneven distribution of subsurface magma. The low b-value area in the north is speculated to be due to high stress resulting from torsional compression at the bottom of the detachment fault. In summary, it can be anticipated that the spatial distribution of b-values will serve as an indicator and reference factor for stress, fault structure, and magmatic-hydrothermal activity in MOR hydrothermal field in the future.

How to cite: wang, K.: Spatial distribution of b-values for microseismicity in the SWIR Longqi hydrothermal field and magmatic-tectonic interpretation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20441, https://doi.org/10.5194/egusphere-egu25-20441, 2025.

Transform faults are often considered to be geometrically simple, nearly linear, vertical structures that localize crustal deformation within a narrow zone surrounding the fault. The deformation kinematics are typically purely strike-slip, parallel to far-field plate motion, with seismic slip above the brittle-ductile transition, near the 600 °C isotherm, which is well predicted by thermal models. Although deviations from these simplified features have been described, much remains to be learned about the seismogenic behavior of transform faults, for example, why they release much less seismic moment than predicted by plate motion models, or why they so rarely produce earthquakes of magnitudes as large as would be expected given their geometric segmentation (>M7). 

The Oriente Transform Fault (OTF) along the southern margin of eastern Cuba, at the boundary between the Caribbean and North American plates, is a particularly relevant example to inform on the seismogenic behavior of transform faults for at least 5 reasons: (1) the OTF geometry changes from a nearly continuous trace along the Cayman Ridge to a highly segmented one westward along eastern Cuba, (2) the geometrically continuous segment was the location of a magnitude 7.8 supershear earthquake in January 2020, (3) GNSS-derived strain measurements indicate that this segmentation variation corresponds to a transition from very shallow (<5 km) mechanical coupling —perhaps creep— of the fault, to complete coupling across the entire crustal thickness (20 km), (4) earthquake hypocenters offshore eastern Cuba locally reach subcrustal depths, (5) earthquake focal mechanisms and offshore geological observations show fault-normal compressional deformation along this purely strike-slip segment.

Here we revisit the offshore trace and seismotectonics of the OTF in light of recent data. We benefit from several oceanographic campaigns in the northern Caribbean, in particular the recent Haiti-TWIST campaign of the Pourquoi Pas? R/V, during which new high-resolution bathymetric and seismic reflection data were acquired, filling several important gaps. We also benefit from recent deformation results from GNSS measurements in Cuba, as well as a new compilation of earthquake moment tensor solutions.

How to cite: Calais, E., Leroy, S., Poort, J., Lebrun, J.-F., Mercier de Lépinay, B., Gonzalez, O., Moreno, B., Granja-Bruna, J.-L., Roest, W., Marcaillou, B., Aiken, C., and Klingelhoefer, F.: Seismotectonics of the Oriente Transform Fault revisited, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20933, https://doi.org/10.5194/egusphere-egu25-20933, 2025.

Subduction zones represent more than half of the total plate boundaries length (38,000 over 64,000km) and cause fast geographic changes by a range of geological processes occurring at local to regional scales such as crustal deformation, volcanism, or dynamic topography. The associated transient changes in land-sea distributions influence the migration, genetic drift, adaptation, speciation, and endemism of the terrestrial biosphere that conquered emerged landmasses. Today, archipelagos located along subduction zones hostone-third of the biodiversity hotspots in the world (Myers et al., 2000). In this context, SUBUTTEC research team aim at combining geological and biological data to unravel the links between the subduction dynamics and terrestrial life in subduction zones based on the case study of the Antilles hotspot. This short and dynamic subduction zone, bounding the east of the Caribbean plate, is ideally circumscribed by two giant continents and two equally giant oceans that provide rather static boundary conditions. To unravel the role of the southern Lesser Antilles in the dynamics of Caribbean biodiversity, we will perform paleogeographic reconstructions over the last 20 Myrs, focused on the unknown role of the southern Lesser Antilles, will be done by integrating tectonics, paleomagnetism, (bio-)stratigraphy and geochronology. We will match these paleogeographic reconstructions with the assemblage distribution and phylogenetic records of extant endemic species, which will allow us to test for alternative scenarios of the temporal dispersion and evolution of life in this highly dynamic hotspot region for both biodiversity and tectonic activity. The implementation of comparative biogeographical methods provides here a powerful tool to reveal natural classification of biogeographic areas i.e. bioregionalization and identification of vicariant events. The joint analysis of the geological and biological records will provide a macro-ecological framework of the biosphere/biodiversity dynamics over subduction zones.

How to cite: Philippon, M., Roncal, J., Cornée, J. J., Quillevere, F., Arcay, D., Cerpa, N., Husson, L., Boucharat, Y., Rousteau, A., Ung, V., Bezault, E., Lorcery, M., Bernet, M., Sarr, A.-C., Riel, N., Kaus, B., Pubellier, M., Thivaiou, D., Montheil, L., and Noury, M. and the SUBUTTEC Team: SUBUTTEC Project: SUBdUcTion triggered Terrestrial Evolution in the Caribbean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20984, https://doi.org/10.5194/egusphere-egu25-20984, 2025.

The Guaymas Basin is a large marginal rift basin in the Gulf of California with ongoing seafloor spreading and strong hydrothermalism centered around two axial troughs. Extremely high concentrations of methane are discharged from diffuse hydrothermal flow, black smokers, and cold seeps. A thick sediment layer across the basin allows for thermocatalytic production of methane in the hot subsurface, resulting in the discharge of hydrothermal fluids from powerful black smokers with temperatures exceeding 300°C. The cooler surface sediments additionally support methanogenesis, providing a complex interplay between the biogenic and abiogenic systems. The dynamism of the Guaymas Basin means that the flux and distribution of hydrothermal vents in this region can change rapidly, impacting the wider oceanography of the region.

We present here results from a 2024 study of hydrothermalism in the Guaymas basin using a new optical sensor, developed at the Woods Hole Oceanographic Institution. SAGE – the Sensor for Aqueous Gases in the Environment – utilizes laser absorption spectroscopy through a hollow core optic fiber to quantify the partial pressure of dissolved methane extracted from the deep sea. This in situ sensor, deployed during a cruise on the R/V Atlantis allows continuous measurement of methane concentrations with high spatiotemporal resolution, with a sampling rate of 1Hz and a stable response time of 1-5 minutes. This new sensing technique facilitated analysis of the relationships between microbial communities and hydrothermalism and guided dives towards hydrothermal vents based on the real-time methane concentration. It also allowed the comprehensive in situ analysis of a rapidly evolving black smoker vent site in the northern axial trough, allowing the methane export to the water column to be characterized with high spatiotemporal resolution. The low detection limit of SAGE – down to ~10 ppm – allows the analysis of the broader impact of these dynamic methane-based systems into the wider oceanography of the region.

How to cite: Michel, A., Burkitt-Gray, M., Marquardt, S., Youngs, S., Remar, J., Joye, S., and Kapit, J.: Chemical mapping of methane in the Northern Guaymas Basin hydrothermal field, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21421, https://doi.org/10.5194/egusphere-egu25-21421, 2025.

The continent-derived nature of the western Philippines (Palawan-Mindoro Microcontinental Block; PCB) contrasts with the island arc-dominated eastern Philippines (Philippine Mobile Belt; PMB). Petrological investigation on the P-T-D history of the metamorphosed rocks in between these two terranes and how they relate to the broader tectonic events are however lacking. In this study, we examined rock units related to the arc-continent collision events in two areas: the Romblon Island Group and the central Zamboanga Peninsula.

In central Philippines, the Romblon Metamorphic Complex (RMC) represents the PCB-derived materials. The RMC consists of metapelitic and metapsammitic paraschists in Tablas, Romblon, and Sibuyan with minor orthoschists and marbles. Using two-feldspar geothermometery, and Raman Spectrometry of Carbonaceous Material, the temperature variations revealed a low P/TStage 1 metamorphism of all RMC units with peak T and P values of about 450-540°C at <5 kbars. Based on tensional structures (e.g. boudins) and preserved metapelitic-metapsammitic interlayering, we attribute this Stage 1 to the PMB continental rifting and subsequent shallowing of the paleogeothermal gradient. The RMC paraschists which are adjacent to the Sibuyan Ophiolite  Complex (SOC) meanwhile register significantly higher T at the same low P conditions (= 570-630 °C). This suggests a second stage of higher T deformation and metamorphism directly linked with the juxtaposition of the continental RMC and the island arc SOC. This is consistent with the subsolidus shearing and metamorphism of the isotropic gabbro units of the SOC with preserved P-T conditions of about 500-800°C.

The southern extension of the PCB-PMB collision is even less understood although earlier works extend the arc-continent suture zone in Mindanao Island, southern Philippines. The purported boundary of the continent-derived Zamboanga Peninsula and the island arc Eastern Mindanao is the northwest-southeast trending Siayan-Sindangan Suture Zone. Our field mapping in central Zamboanga Peninsula however revealed a distinct northeast-southwest trending suture zone of an apparent arc-continent collision zone. Across this NE-SW suture zone, the lithologies progress from the paraschists of the Gutalac Metamorphic Complex (GMC) in the northeast to the amphibolites of the Dansalan Metamorphic Complex (DMC). Further southeast, the residual peridotites and pillow lavas with intercalated chert, deep marine turbidites and limestones of the Polanco Ophiolite Complex (POC) are exposed. Such progression hints at a NE-SW convergence of an ancient arc (POC) with its metamorphic sole (DMC) against the continent-derived GMC following the consumption of an ancient oceanic basin.

How to cite: Valera, G. T., Badillo, J. K., Tabilog, A. E. S., Balanial, N. M., Alcancia, M. L., and Payot, B. D.: Understanding the arc-continent collision zones in western Philippines: Novel insights from the Romblon Island Group and the Central Zamboanga Peninsula, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21462, https://doi.org/10.5194/egusphere-egu25-21462, 2025.

The eastern Liaoning-southern Jilin tectonic zone (also referred to as the Liao-Ji tectonic zone), a potential zone for rare-metal and REE mineralizations in China, hosts over 10 rare-metal and REE deposits and ore occurrences with varying scales and mineralization characteristics, which establish this zone as an ideal target for research on the metallogenic regularities of rare-metal and REE mineralizations.The study area resides in the northern part of the East Asian continental margin, lying on the overlapping part of the North China and the Western Pacific Plates, is located in the northeastern North China Plate, consisting of the North China Craton and the north margin orogen of the North China Plate. This area serves as a critical large-scale copper-gold and polymetallic mineral resource base in China, also providing favorable geologic conditions for the enrichment and mineralization of rare metals and REEs. So far, many rare-metal and REE deposits and ore occurrences have been discovered in the Liao-Ji tectonic zone, including two large Nb-Be-Zr-REE deposits (i.e., Lijiapuzi and Pianshishan), two medium-sized Rb-Be-Nb-Ta-REE deposits (i.e., Saima and Gangshan), one small Nb-Ta-REE deposit (i.e., Shijia), and over 10 rare metal-REE ore occurrences (e.g., Xiaolizi, and Baiqi), suggesting considerable mineralization potential. Most of the deposits in the Liao-Ji tectonic zone are closely associated with alkaline rocks.

Extensive field surveys and geochemical studies of the above deposits reveal that the ore-forming rock masses of the Pianshishan, Gangshan, and Lijiapuzi deposits include alkaline granites and pegmatites and those of the Shijia and Saima deposits are quartz syenites and aegirine nepheline syenites, respectively. The Pianshishan (67±2.2 Ma) and Gangshan (110±1.2 Ma) deposits were formed during the Yanshanian, the Shijia (226.3±2.4 Ma) and Saima (224.4±6.1 Ma) deposits originated from the Late Indosinian magmatism, while the formation of the Lijiapuzi deposit (2501±11 Ma) was associated with the Lvliang Movement. Therefore, the study area underwent three stages of regional rare-metal and REE mineralizations: the Late Yanshanian (Mesozoic), Late Indosinian (Mesozoic), and Proterozoic Lvliangian mineralizations. The petrogeochemical analysis indicates that the ore-forming rock masses of several typical deposits all belong to the metaluminous, alkaline - calc-alkaline, and tholeiitic basalt series, sharing similarities with the elemental geochemical characteristics of intraplate rift rock series and rocks in an extensional environment under plate subduction. The rare-metal and REE mineralizations in the study area were primarily governed by the evolution and crystallization differentiation of alkaline magmas. Given that the alkaline magmatic rocks were all formed by crust-mantle contamination, this study posits that the enrichment and mineralization processes of rare metals and REEs in the Liao-Ji tectonic zone are intimately associated with the highly evolved alkaline magmas. Under the action of water and volatile constituents, magmas underwent intense fractional crystallization, leading to the migration and accumulation of ore-forming elements. With changes in ore-forming conditions such as temperature and pressure, ore-bearing fluids became enriched and mineralized in the late stage of magmatism with the crystallization of primary rock-forming minerals.

How to cite: Ju, N., Yang, G., Zhang, P., Li, J., Wu, Y., Lu, S., Liu, B., Yang, X., Liu, X., and Feng, Y.: Rare-metal and rare earth element mineralizations in the eastern Liaoning-southern Jilin tectonic zone in Northeast China: A review, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2404, https://doi.org/10.5194/egusphere-egu25-2404, 2025.

The second member of the Fengcheng Formation in the early Permian of the Mahu Depression has a rock series with interbedded alkali mineral layers and tuffaceous layers. The dark layer contains a large amount of associated metal minerals, which are closely related to the volcanic hydrothermal material at the Fengnan fault nose. However, due to the presence of detrital rock deposits on the west side of the Mahu Depression, this area is jointly controlled by volcanoes and terrestrial sources to form alkali mineralization. There are also a large number of dark hydrocarbon source rocks developed in the region, which are also one of the reasons for the mineralization of alkali minerals and associated metal minerals. Therefore, a mineralization model is established.

How to cite: Liu, X. Y., Longwei, Q., and Yongqiang, Y.: Enrichment Factors of Alkali and Key Metal Mineral Resources in Fengcheng Formation of Mahu Sag, the Junggar Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2960, https://doi.org/10.5194/egusphere-egu25-2960, 2025.

The Global Navigation Satellite Systems - Interferometric Reflectometry (GNSS-IR) method has been utilized for nearly fifteen years as an alternative and cost-effective approach to determine hydrological parameters such as sea level, snow depth, and soil moisture through the analysis of signal-to-noise ratio (SNR) data. Most GNSS-IR studies to date rely on archived data and post-processed results. However, the potential for near real-time GNSS-IR analysis is increasingly being explored. In this study, high-rate GNSS archive data, sampled at 1-second intervals and stored in 15-minute files, were processed in a simulated near real-time workflow. Every 15 minutes, new data were added to the analysis, focusing exclusively on the most recent 60 minutes of observations. A novel approach for detecting outliers in near real-time GNSS-IR estimates was also proposed. The median-based robust outlier detection (ROD) method, previously validated for post-processed GNSS-IR snow depth results, was adapted and applied to near real-time GNSS-IR data. A 30-day dataset of multi-GNSS, multi-frequency SNR observations from the Portland (PTLD) GNSS station in Australia, collected in November 2024, was analyzed. The near real-time GNSS-IR results were validated using sea level measurements from the PORL tide gauge station. The results demonstrate that the modified ROD approach can be used to identify outliers in near real-time GNSS-IR sea level retrievals.

How to cite: Altuntas, C., Erdogan, B., Tunalioglu, N., and Williams, S.: Improving near real-time GNSS-IR sea level retrievals with robust outlier detection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3157, https://doi.org/10.5194/egusphere-egu25-3157, 2025.

The bauxitic region of Sumbi and its surroundings in Kongo Central (DR Congo) is located in an area corresponding to “bands” of basic rocks made up of microdolerites, basalts and andesites. The problem of this study is linked to the similarity of the phenomena that generated the depositional process of these ferruginous and aluminous formations. The aim of this article is to carry out a chemical and petrographic study of samples of bauxitic materials from the Mayedo and Kinzoki regions, with a view to their possible recovery. To this end, the chemical and petrographic analysis of the weathering formations outcropping in the study area was carried out using X-ray fluorescence and thin section methods. The latter revealed that two lithologies were detected in the healthy rocks: basalts with a mineralogical assemblage of plagioclase crystals, pyroxene microcrystals and oxide opaques; and dolerites represented by plagioclase crystals, pyroxenes and a few quartz crystals. X-ray fluorescence revealed high levels of Al₂O₃ (32.69%) in the Mayedo zone (MHb1). This visibly gibbsite-rich level corresponds to the zone of friable, homogeneous bauxite with a massive, blood-red texture, with an estimated gibbsite percentage of 55.50. The percentage of Fe₂O₃ is high in these zones at 42.77%, hence the dark red colour, reflecting a strong zone of ferruginasation. This horizon contains a high concentration of hematite and goethite minerals. Highly variable SiO₂ contents ranging from 13.48% to 40.82%. These variations are essentially due to the dissolution of silica by leaching and resilification.

How to cite: Mwanakangu, E. and Ungu, D.: Petrographic and Geochemical Characterization of Mayedo and Kinzoki Ranges (Sumbi Bauxite Region, Kongo Central/DR Congo), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3874, https://doi.org/10.5194/egusphere-egu25-3874, 2025.

High-precision GNSS coordinate time series modeling and prediction provide a critical reference for applications such as crustal deformation, structural safety monitoring, and regional or global reference frame maintenance. A Deep neural network framework based on Transformer was applied to 22 GNSS stations, each with 1000 days, in which data is preprocessed using a synchronization sliding window. The overall fitting and prediction trends exhibit a high degree of consistency with the original time series. The average fitting RMSE and MAE are 3.40 mm and 2.64 mm, respectively, while the corresponding average prediction RMSE and MAE are 3.54 mm and 2.77 mm. In comparison to the LSTM model, the proposed method achieved a redu78ction in RMSE and MAE by 20.7% and 19.6%, respectively. Furthermore, when benchmarked against the traditional least squares approach, the improvements were even more pronounced, with RMSE and MAE decreasing by 35.7% and 37.8%, respectively. The approach demonstrates robustness and effectiveness under conditions of discontinuous data. Therefore, it could be used as a convenient alternative to predict GNSS coordinate time series and will be of wide practical value in the fields of reference frame maintenance and deformation early warning.

How to cite: Wang, J., Li, Z., and Jiang, W.: Deep Neural Networks for GNSS Coordinate Time Series Modeling and Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4633, https://doi.org/10.5194/egusphere-egu25-4633, 2025.

The Vemula-Velpula hydrothermal barite deposit is hosted by mafic dykes (ca. 1850 Ma. [1]) intruding into the uppermost part of about 1900 m thick carbonate strata of the Vempalle Formation (ca. 2000 Ma. [2]) in Cuddapah basin and occurs as fracture-fill and breccia-fill veins. The veins dominantly consist of barite with minor quartz. The host mafic rock has undergone various extents of hydrothermal alteration, due to which the primary calcic plagioclase-clinopyroxene assemblage is altered to albite and clinochlore, along with the introduction of secondary epidote, quartz, and calcite. The wide range in Ba concentration of mafic rock (68 to 3012 ppm) associated with the barite mineralization indicates that Ba was mobilized and subsequently leached from the mafic rock by the hydrothermal fluid during this alteration event. The δ³⁴S values of barite range from +16.19 to +23.24‰ which falls within the range of δ³⁴S value of +10 to +30‰ estimated for Proterozoic seawater [3]. At shallow crustal depth where this deposit was formed, direct participation of seawater is unlikely and therefore basinal brine is inferred to be the source of sulphate ion required for barite mineralization. Primary aqueous biphase fluid inclusions in barite have homogenization temperatures ranging from 180 to 300 °C, with most of them clustering in the range 220-250°C, and salinities ranging from 2.4 to 25.8 wt.% NaCl equivalent. The first ice melting temperature of these inclusions was measured between -55 and -37°C, broadly pointing towards an H₂O-NaCl-CaCl₂ fluid system. Petrography and microthermometric data of fluid inclusions indicate the involvement of two fluids of different salinities, which, upon mixing and cooling, led to barite precipitation.

This research work was funded by SERB, New Delhi (Scheme No. CRG/2019/001015).

References

[1] Chakraborty K. et al. (2016), Journal of the Geological Society of India 87, 631–660.

[2] Rai A.K. et al., (2015), Journal of the Geological Society of India 86, 131–136.

[3] Strauss H (1993) Precambrian Research 63(3–4), 225–246.

How to cite: Devanand Sreekala, D. and Muthusamy, S. P.: Insight into the genesis of barite deposit in Vempalle Formation, Cuddapah basin, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4666, https://doi.org/10.5194/egusphere-egu25-4666, 2025.

A high-precision Terrestrial Reference Frame (TRF) is essential for accurately monitoring geophysical activities. Currently, India lacks its own Regional Reference Frame (RRF) and relies on the global frame for local applications. With the recent installation of more than 1000 GNSS stations forming the so called CORS (Continuously Operating Reference Stations) network by the Survey of India (SoI), the need for a precise RRF for India has become evident.

The RRF can be realized using either a conventional secular Multi-Year Reference Frame (MRF) or a (geocentric) Epoch-based Reference Frame (ERF). An MRF is realized by aligning the regional network to a global frame such as the ITRF or IGS TRF. However, the accuracy of MRFs diminishes over time as coordinates are extrapolated beyond the observation period using linear velocities. Additionally, MRFs provide limited geophysical information and can’t be utilized with desired accuracy for quasi-instantaneous applications or after large earthquakes.

To address these limitations, this study aims to develop an ERF for the Indian CORS sub-network by adopting the methodology introduced by Kehm (2022) for creating a geocentric ERF in Latin America. The proposed ERF ensures that the frame's origin aligns with the Earth's instantaneous center of mass across all time scales within the observation period.

In this presentation, we will outline the approach to develop the Indian ERF, which involves combining weekly normal equations from global GNSS, SLR, and VLBI networks. Specifically, SLR determines the origin, SLR and VLBI jointly determine the scale, and a homogeneously distributed global GNSS network is used to realise the orientation of the frame. The results will be compared to those obtained using the conventional MRF realization approach. Furthermore, an independent validation strategy will be implemented to evaluate the accuracy of the developed ERF.

How to cite: Kushwaha, R., Bloßfeld, M., Kehm, A., Balasubramanian, N., and Dikshit, O.: Initial Findings on Epoch-Wise Realization of a Regional Reference Frame Using Indian CORS Sub-Network Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5046, https://doi.org/10.5194/egusphere-egu25-5046, 2025.

Direct ore-mineral U-Pb geochronology of scheelite (CaWO₄), cassiterite (SnO₂), and wolframite ([Fe,Mn]WO₄) using recently-developed reference materials led to new ore-genesis insights for multiple worldwide W-Sn/rare metal deposits. Scheelite from the Yellow Pine epithermal Au-W-Sb deposit in Idaho, USA was age dated using U-Pb via isotope dilution thermal ionization mass spectrometry (TIMS) and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). These analyses provided both the first age constraints on the tungsten mineralization (ca. 57 Ma) and a scheelite U-Pb reference material (NMNH-107667; 57.52 ± 0.22 Ma). The data reveal ore mineralization occurred in numerous discrete pulses during crustal uplift, which contrasts with the previous two-mineralization-event model.

The Yellow Pine scheelite reference material enabled U-Pb scheelite geochronology via LA-ICP-MS for multiple other deposits worldwide; namely, the polyphase stratabound scheelite-ferberite mineralization hosted within Fe-rich magnesite zones and marbles in two locations around Mount Mallnock, Austria. Two unexpected but geologically meaningful age dates (294 ± 8 Ma) for Mallnock West and (239 ± 3 Ma) for Mallnock North revealed for the first time that ore mineralization occurred during an extensional geodynamic setting as part of the breakup of Pangea, as opposed to the previous model invoking the older compressional tectonics of the Variscan orogeny.

Combining direct-ore geochronology methods for several ore minerals was particularly powerful for Sn- and W-bearing deposits in southeast Australia. A U-Pb cassiterite age date (435 ± 2 Ma) revealed the tin-bearing lithium pegmatites of the Dorchap Dyke Swarm are ca. 15 Ma older than some previous estimates suggesting mineralization was related to the earliest magmatic activity recorded in the Wagga-Omeo Metamorphic Belt. Additionally, a new U-Pb wolframite age date (395 ± 5 Ma) for the Womobi polymetallic (W-Mo-Bi) deposit is ca. 21 million years younger than the host Thologolong granite, suggesting the granite was a passive host that was mineralized by a concealed intrusion. Both instances revealed mineralization ages that were significantly different than previously accepted. More widespread application of these increasingly diverse, direct-ore geochronology methods stand to replace uncertain spatial or textural associations, thereby providing an opportunity to significantly improve ore genesis models.

How to cite: Wintzer, N., Holm-Denoma, C., Altenberger, F., and Waugh, S.: W-Sn Ore-Mineral Geochronology: New Ages Improve Genesis Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5245, https://doi.org/10.5194/egusphere-egu25-5245, 2025.

The relationship between the length of day (LOD) and the El Niño-Southern Oscillation (ENSO) has been extensively studied since the 1980s. LOD represents the negative time derivative of UT1-UTC, directly proportional to the Earth Rotation Angle (ERA), a key Earth Orientation Parameter (EOP).

ENSO is a climate phenomenon occurring in the tropical eastern Pacific Ocean that primarily impacts the tropics and subtropics. Extreme ENSO events can lead to severe weather conditions, such as flooding and droughts, across various regions worldwide. ENSO event undergoes a lengthy incubation period, during which the interannual variations in length-of-day (LOD) and atmospheric angular momentum (AAM) are rapidly influenced by the interactions between the ocean and the atmosphere.

The significant characteristics of climate change are the rise of global temperature and sea level, which are driven by ENSO. Interannual oscillations in global mean sea temperature (GMST) and global mean sea level (GMSL) might also impact changes in the Earth’s rotation velocity.

The goal of this study is to explain in more detail connections among the interannual (2-8 years) variations of the LOD, AAM, and different climate indices, like the Southern Oscillation Index SOI, Oceanic Niño Index ONI, GMSL, and GMST. The influence of climate signatures on LOD from January 1976 to December 2024 is assessed using semblance analysis based on continuous wavelet transform. This method evaluates the correlation between climate time series in the time and wavelength domains.

Studying the relationship between LOD, AAM, GMSL, GMST, and ENSO indices enhances our understanding of Earth's dynamic system, improves geophysical models, and increases the precision of applications dependent on accurate timekeeping and Earth rotation measurements.

How to cite: Wińska, M., Śliwińska-Bronowicz, J., Nastula, J., and Staniszewska, D.: Length of the Day changes and climate signatures- their relations in detected ENSO Events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8007, https://doi.org/10.5194/egusphere-egu25-8007, 2025.

Anelasticity is a type of rheology intermediate between elasticity and viscosity, responsible for rock’s transient creep behaviour. Whether to consider anelasticity in geodynamic processes operating outside the seismic frequency band which likely involve transient mantle creep is still under debate. Here, we focus on the geodynamic process of ocean tide loading (OTL), namely the deformational response of the solid Earth to the periodic ocean water-mass redistributions caused by astronomical tides. By analysing high-precision Global Positioning System (GPS) data from over 250 sites in western Europe and numerical OTL values from advanced three-dimensional Earth models, we unambiguously demonstrate anelastic OTL displacements in both the horizontal and vertical directions. This finding establishes the need to consider anelasticity in geodynamic processes operating at sub-seismic timescales such as OTL, post-seismic movement, and glacial isostatic adjustment (GIA) due to rapid ice melting. Consequently, to construct a uniform viscoelastic law for modelling Earth deformations across multiple timescales anelasticity must be incorporated. Our best-fitting anelastic models reveal the shear modulus in Earth’s upper mantle to be weaker at semi-diurnal tidal frequencies by up to 20% compared to the Preliminary Reference Earth Model (PREM) specified at 1 Hz, and constrain the time dependence of this weakening.

How to cite: Huang, P., Penna, N. T., Clarke, P. J., Klemann, V., Martinec, Z., and Tanaka, Y.: Signature of mantle anelasticity detected by GPS ocean tide loading observations , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9320, https://doi.org/10.5194/egusphere-egu25-9320, 2025.

This study investigates the present-day seismotectonic framework of the High Atlas Mountains, Morocco, with a specific focus on the area affected by the devastating Mw 6.8 Al Haouz earthquake of September 8, 2023. Leveraging a high-resolution seismic dataset encompassing over twenty moderate earthquakes (M 3.5-6.8) recorded by regional networks between 2008 and 2024, the research aims to refine earthquake locations and characterize the regional stress field. Initially located using P-wave arrival times, earthquake hypocenters were subsequently relocated using the double-difference method, which yielded more precise locations by minimizing travel-time residuals between pairs of events recorded at common stations. The high degree of agreement between the initial and relocated solutions validates the robustness of the location estimates. Notably, the observed seismicity is confined to shallow crustal depths, consistently shallower than 30 km, corroborating the shallow rupture observed for the Al Haouz earthquake, which occurred at a depth of approximately 31 km. This shallow seismicity suggests a shallow deformation style within the High Atlas.

To determine the state of the present-day tectonic and stress regimes across the western and central segments of the High Atlas, the study uses two complementary approaches: regional seismic moment tensor inversion and P-wave first motion focal mechanism analysis. Fault plane solutions were calculated using P-wave first motion polarities and further constrained through regional moment tensor inversion. The majority of analyzed earthquakes exhibit reverse faulting mechanisms, often with a significant strike-slip component, indicating a complex deformation pattern. Analysis of the principal stress axes (P, B, and T) derived from the focal mechanisms reveals average orientations of 16/189, 39/036, and 08/104 (plunge/azimuth), respectively. Subsequently, tectonic stress tensor properties were derived through inversion of the focal mechanism parameters. The results of this stress inversion indicate a predominantly N-S oriented maximum horizontal stress (σ1) in the Western High Atlas, closely aligned with the faulting style of the Al Haouz earthquake. In contrast, the stress field in the Central High Atlas exhibits a transition to a NW-SE to NNW-NNE orientation of σ1. These spatially varying stress orientations are consistent with independently derived GPS velocities and available neotectonics data, which document ongoing shortening across the High Atlas. This integrated analysis provides a comprehensive understanding of the active tectonic deformation within the High Atlas, shedding light on the complex interplay of faulting styles and stress orientations, and providing crucial insights into the source mechanism and broader tectonic context of the Al Haouz earthquake within the Western High Atlas region.

How to cite: Oujane, B., El Moudnib, L., Zeckra, M., Badrane, S., and Nouayti, A.: Seismotectonics of the Intracontinental High Atlas Mountains, Morocco, Derived from Regional Seismic Moment Tensor Analysis: Insights into tectonics and stress regimes., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9503, https://doi.org/10.5194/egusphere-egu25-9503, 2025.

North East India (NEI) is situated between the Himalayan collision arc to the north and the Indo-Burmese Ranges (IBR) to the east. The tectonic unit of the NEI, Shillong Plateau (SP), is one of the most active seismotectonic zones of the Indian subcontinent, as demonstrated by its seismicity. It is crucial to identify active faults in populated areas for human safety and the sustainable development of society. The gravity method is one of the convenient methods to delineate the shallow to deeper subsurface discontinuities, i.e., it is useful to detect active faults in the subsurface compared to other geophysical methods (e.g., Electrical, and Electromagnetic methods). In this study, detailed multilayer horizontal tectonics stress (HTS) was calculated using the approach of multi-scale decomposition of gravity anomalies data. HTS can be helpful in demarcating shallow to deep-seated tectonic structures. The tectonic features exhibit a strong correlation with the distribution of HTS at different depths. Major faults and earthquake epicentre align with areas of high stress, while stable zones correspond to regions of low stress. It means that HTS is employed to deduce the distribution and stability of faults. The high value of HTS is increased from shallow to deep depths for SP, Mikir Hills, IBR and Eastern Himalaya in the NEI region, and it varies as ~ 0.2-0.53 MPa, ~ 0.24-0.61 MPa, ~ 0.3-0.84 MPa, ~ 0.4-1.2 MPa, ~ 0.57 1.86 MPa, ~ 0.8-2.4 MPa, ~ 0.84-3.0 MPa at 4, 8, 12, 20, 40, 50, and 60 km depths, respectively. While the Brahmaputra Valley and the Surma Basin show relatively less stress, where HTS varies between ~ 0.1-0.33 MPa for 4, 8, 12, 20, 40, 50, and 60 km depths. It can be interpreted that the populated SP and Mikir Hills are highly unstable or earthquake-prone regions due to high stress.

How to cite: Pathak, P. and Kumar Mohanty, W.: Horizontal tectonic stresses and its implications in the Shillong Plateau and its adjoining using gravity data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9618, https://doi.org/10.5194/egusphere-egu25-9618, 2025.

The Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) aimed to evaluate and compare various methods of Earth Orientation Parameters (EOP) predictions. One of the goals of the 2nd EOP PCC was to prepare a combination of the predictions to obtain one robust and accurate solution for forecasts of individual parameters. This presentation focuses on identifying the most reliable and accurate combination of predictions for polar motion (PMx, PMy), universal time variations (UT1-UTC), and length of day (LOD) among the methods tested during the 2nd EOP PCC.

Two types of experiments were designed for this study: "operational" combinations tailored to real-time comparisons and practical application and "final" combinations designed for comprehensive analysis. Boths approaches incorporated six methods for handling outlier predictions, ranging from no filtration to progressively stricter criteria using the σ+β method (with α values ranging from 5 to 1). All experiments cover the period of 2nd EOP PCC (from September 1, 2021, to December 31, 2022), and each approach includes 70 10-day predictions.

The results show that combining various submissions generally enhances stability and accuracy of EOP forecasts. The σ+β criterion with α = 1 achieved the smallest Mean Absolute Prediction Error, indicating high accuracy of prediction. However, this method of eliminating outliers forecasts is the most restrictive, as it excludes a significant number of predictions. In contrast, operational combinations without filtering proved more practical for real-time applications, albeit with slightly higher errors.

The findings underscore the importance of tailoring combination strategies to specific goals—whether prioritizing maximum accuracy or practical applicability. This research highlights the benefits of prediction combination methods in improving EOP forecasts, offering a foundation for further development of operational strategies and expanding their use in geophysical and astronomical applications.

How to cite: Michalczak, M., Śliwińska-Bronowicz, J., Wińska, M., Partyka, A., Ligas, M., and Nastula, J.: Exploring various approaches to combine Earth Orientation Parameter (EOP) predictions gathered during the Second EOP Prediction Comparison Campaign (2nd EOP PCC), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9658, https://doi.org/10.5194/egusphere-egu25-9658, 2025.

Precise estimation of Zenith Tropospheric Delay (ZTD) is crucial for improving the accuracy of data from Continuously Operating Reference Stations (CORS), particularly in applications requiring high-precision GNSS positioning. This study focuses on evaluating various ZTD models to identify the most accurate approach for mitigating atmospheric delays in CORS data. The research compares ZTD values derived from Automatic Weather Stations (AWS), GNSS meteorological sensors, and temperature-pressure-humidity-based models calculated using the GAMIT software with reference values obtained from co-located weather stations and global atmospheric models.

The methodology involves processing GNSS observations from selected CORS sites using multiple ZTD estimation models, including empirical approaches. The accuracy of these models is assessed using key performance metrics such as root mean square error (RMSE), mean bias, and correlation with actual weather conditions.

Preliminary results indicate that empirical models show better consistency in stable atmospheric conditions. Additionally, comparisons between GAMIT-derived ZTD values and those from AWS and GNSS met sensors reveal insights into the reliability and precision of each data source under different atmospheric conditions.

The study highlights that precise ZTD estimation is essential for reducing atmospheric errors in CORS data, thereby enhancing GNSS-based applications such as geodesy, surveying, and real-time positioning. The research concludes that combining inputs from various meteorological data sources offers the best accuracy across diverse CORS networks, particularly in regions with varying climatic conditions and atmospheric dynamics.

How to cite: Agarwal, D., Mahato, S., Gandugade, P. B., Nagarajan, B., and Dikshit, O.: Comparisons between GAMIT-derived Zenith Tropospheric Delay (ZTD) values from AWS and GNSS met sensor values, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10677, https://doi.org/10.5194/egusphere-egu25-10677, 2025.

This study introduces a methodology designed to enhance the accuracy of Celestial Pole Offset (dX, dY) prediction, with a focus on a short-term forecast horizon (up to 30-days). IERS EOP final data as well as those published by JPL are used as input for the  prediction algorithms. The prediction procedure is consistent, in the sense that, it does not rely on any external data to fill any latency gaps in the final IERS product. This is handled within the prediction routine itself by enlarging the forecast horizon to the gap filling horizon and proper forecast horizon. In this way, the presented methodology is ready to use under operational settings what makes it well suited for real time applications. Such an approach enables also to asses prediction capabilities of the methods in offline experiments whilst maintaining the operational settings. JPL CPO data serves as supplementary series for prediction and adjusting using Deming regression to align it  with IERS CPO values (attempt to assess fixed and proportional biases between series). The prediction strategy applies also the Whittaker-Henderson smoother to IERS CPO series, which after smoothing is treated as an additional source of information in the prediction process. Separate predictions based on JPL, IERS and smoothed IERS series are also averaged in different combinations giving rise to ensemble data-based prediction model. In this way we show that the overpredictive and underpredictive characteristics of specific input data, even with the application of a single prediction method, can result in a more precise and accurate final forecast. The presented approach was tested against the results obtained within the course of the 2^nd EOPPCC, as well as other contemporary studies. This presentation includes also a comparison of performance of the method in reference to different series, i.e., IERS EOP 14 C04 and IERS EOP 20 C04.

How to cite: Ligas, M., Michalczak, M., Belda, S., Ferrándiz, J. M., Karbon, M., and Modiri, S.: Enhanced Celestial Pole Offset forecast via combination of different data sources, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11708, https://doi.org/10.5194/egusphere-egu25-11708, 2025.

Geodetic VLBI (Very Long Baseline Interferometry) currently consists of two observing networks (legacy S/X and broadband VGOS). Heretofore, the two networks have run rather independently, which is non-ideal. There have been several attempts to combine observations from both networks at sites with co-located antennas using either conventional local-tie surveys or VLBI tie-sessions between S/X and VGOS, or both. Unfortunately, the number of sites with co-located VLBI antennas is rather limited, which hampers progress. To overcome this problem, we proposed an approach, the so-called mixed-mode VLBI tie session, that does not require to have co-located VLBI antennas. Instead, mixed-mode sessions have the S/X and VGOS networks observed simultaneously as a single geodetic VLBI technique to thus obtain global ties between the two networks. Two of the sessions observed in 2020 were already included in the ITRF2020 combination. We hypothesize that the global-tie approach helps preserve the geometry of the networks when aligning with the state-of-art ITRF2020 frame. In this presentation, we will describe the observed mixed-mode sessions, detailing scheduling strategies, correlation techniques, and geodetic processing methods used. We will also demonstrate how mixed-mode sessions can help realize a stable global geodetic reference frame such as the ITRF.

How to cite: Mondal, D. R., Elosegui, P., Ruszczyk, C., Lemoine, F., and Behrend, D.: Global VLBI ties using mixed-mode sessions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12533, https://doi.org/10.5194/egusphere-egu25-12533, 2025.

The progressive development of navigation technology has significantly improved real-time positioning accuracy, addressing the needs of modern applications. GNSS (Global Navigation Satellite System) is the primary system used for precise positioning across various platforms. However, GNSS is susceptible to errors, particularly interference, which degrades signal quality and compromises accuracy. Auxiliary systems such as INS, gyroscopes, and map-matching algorithms enhance reliability during interference but depend on GNSS for initialization. Signal detection algorithms, often employing CRPA (Controlled Reception Pattern Antennas) and advanced computational techniques, are essential for mitigating the impact of interferences and ensuring reliable navigation. This study compares the performance of two CRPA systems with different GNSS modules and algorithms, subjected to spoofing-jamming interference during experiments. The first CRPA, integrated with the u-blox ZED-F9P module, supports GPS, BeiDou, and Galileo satellites, employing an adaptive notch filter and pulse blanking. The second CRPA, featuring the Unicore UM980 module, supports GPS, BeiDou, and GLONASS satellites, utilizing a space-time algorithm alongside the JamShield adaptive mechanism for interference mitigation. In this study, real-time measurements were conducted on a car-mounted device platform under normal operating conditions. The platform was tested stationary for 5 minutes, followed by 15-minute intervals at speeds of 60 km/h. During each interval, 5 minutes of jamming and 5 minutes of spoofing were applied, with independent spoofing signals introduced. Jamming signals reached up to 50 dB-Hz, and spoofing signals were applied at levels up to 32 dB-Hz using a specialized interference device. During constant-speed travel, the second CRPA tracked 28 satellites with an HDOP of 0.5, while the first CRPA tracked 23 satellites with an HDOP of 0.75. Under jamming conditions, The second antenna maintained consistent satellite visibility, whereas the first experienced a pronounced decline in the number of observable satellites. Similarly, spoofing had no adverse effect on the second antenna, but the first suffered reduced satellite counts and positional accuracy. Additionally, the first antenna consistently underestimated the vehicle’s speed by approximately 5 km/h and exhibited a speed fluctuation of 0.5 m/s under interference conditions. 

How to cite: Karlitepe, F., Sezen, S., Velibasa, B. E., and Kabalci, A.: Advancements in Navigation Technology and Robustness Against GNSS Interference: A Comparative Analysis of CRPA , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12972, https://doi.org/10.5194/egusphere-egu25-12972, 2025.

The need to improve Earth rotation theories and models in a consistent and accurate
manner is currently widely recognized. Several researchers and groups at different
institutions have been working on this problem using quite different approaches, either
from the theoretical or computational perspective.
A potential source of the loss of accuracy of celestial pole offsets can be due to the
mismodeling of the planetary component of the IAU2000 nutation series. In fact, as
recognized in Ferrándiz et al. (2018), this component is actually based on a rigid-Earth
solution and does not include the Oppolzer terms that are significantly affected by the
Earth non-rigidity.
Such hypothesis was showed to be realistic by adjusting directly the amplitudes of a
small number of nutation periods of strictly planetary origin that could be reasonably
well separated by analyzing the series of VLBI observations. The results provide
significant fittings and the WRMS was successfully decreased by amounts comparable
to those achieved with lunisolar amplitude rescaling. A further step in this direction
requires the consideration of theoretical developments for the amplitudes of the non-
rigid Earth planetary nutations.
In this contribution, we present preliminary results considering the analytical formulae
of such planetary amplitudes for a two-layer earth model including dissipation effects at
the core-mantle boundary and anelasticity, obtained from a Hamiltonian method. Their
performance is assessed using several series of VLBI observations, with satisfactory
results, and is placed in the general context of the improvement of the precession and
nutation models sought by the IAG and the IAU.
Acknowledgment. This research was supported partially by Spanish Projects PID2020-119383GB-I00 funded by
Ministerio de Ciencia e Innovación (MCIN/AEI/10.13039/501100011033); SEJIGENT/2021/001, funded by
Generalitat Valenciana; and the European Union—NextGenerationEU (ZAMBRANO 21-04).

How to cite: Zerifi, A. Z., Ferrándiz, J. M., Escapa, A., Baenas, T., Juárez, M. A., Belda, S., and Karbon, M.: Performance of a new set of analytical corrections to planetary nutations: preliminary results and outlook, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13099, https://doi.org/10.5194/egusphere-egu25-13099, 2025.

The rapidly changing climate is amplifying both the frequency and severity of extreme weather events in the Azores archipelago, Portugal. Understanding the underlying dynamics of these events is essential for effective mitigation. Atmospheric water vapor data derived from the Global Navigation Satellite System (GNSS) data and reanalysis outputs from an atmospheric general circulation model offer valuable tools for studying the behavior of weather fronts around the Atlantic Ocean environment of the Azores. This research aims to conduct a detailed comparison between GNSS-based measurements and atmospheric reanalysis data, such as those available from ERA/MERRA2, focusing on the detection of small-scale atmospheric structures with high temporal resolution. We utilize atmospheric reanalysis products to decode long-term trends in the frequency and severity of extreme weather events in the Azores. We then apply statistical methods to identify consistencies and differences between these two approaches in capturing atmospheric water vapor patterns. By combining water-vapor estimates from both GNSS data and atmospheric reanalysis, we are able to characterize the dynamics of atmospheric turbulence from small (few meters) to large (few tens of kilometers) scales. 

How to cite: Ramrajvel, N., Mondal, D., Elosegui, P., Paine, S., Mateus, P., and Mendes, V.: Decoding the signal of extreme weather events in the Azores archipelago using GNSS and atmospheric reanalysis products, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13415, https://doi.org/10.5194/egusphere-egu25-13415, 2025.

Abstract.

The Isiro and Ngayu belts in northeastern Democratic Republic of Congo (DRC) are part of the Congo Craton and among the most poorly known Archean terrains worldwide. These belts consist of metavolcanic and metasedimentary rocks surrounded or intruded by granitoid rocks. minimum age of deposition for the supracrustal formations is defined at ca 2633 Ma (e.g. Allibone et al., 2020), whereas the granitoids were dated between 3200 Ma and 2530 Ma (Allibone et al., 2020; Turnbull et al., 2021) and are strongly deformed with variable proportions of mafic enclaves at outcrop scale (Turnbull et al., 2021). Both Isiros and Ngayu belts host important gold deposits, but the genetic relationships between gold mineralization, deformation and the diverse host rocks remain ambiguous. In this context, the work we present here is part of a multidisciplinary approach, combining the processing of satellite images and field observations using GIS to map the structural lineament that may control gold mineralization in the region. The results show that the strains are large, marked by NW-SE lineaments at low angle to the belt strikes and combined with a secondary ENE-WSW brittle structure. The overall structural pattern, together with the existence of artisanal gold mining in the area, emphasizes that gold mineralization is largely controlled by structures localization along the greenstone belts.

Key words: Congo craton, gold mineralization, field observations, satellites images, structural lineaments.

Reference

Allibone, A., Vargas, C., Mwandale, E., Kwibisa, J., Jongens, R., Quick, S., Komarnisky, N., Fanning, M., Bird, P., MacKenzie, D., Turnbull, R., Holliday, J., 2020. Chapter 9: Orogenic Gold Deposits of the Kibali District, Neoarchean Moto Belt, Northeastern Democratic Republic of Congo, in: Sillitoe, R.H., Goldfarb, R.J., Robert, F., Simmons, S.F. (Eds.), Geology of the World’s Major Gold Deposits and Provinces. Society of Economic Geologists, p. 0. https://doi.org/10.5382/SP.23.09

Turnbull, R.E., Allibone, A.H., Matheys, F., Fanning, C.M., Kasereka, E., Kabete, J., McNaughton, N.J., Mwandale, E., Holliday, J., 2021. Geology and geochronology of the Archean plutonic rocks in the northeast Democratic Republic of Congo. Precambrian Research 358, 106133. https://doi.org/10.1016/j.precamres.2021.106133

How to cite: Birimwiragi Namogo, D., Martial Akame, J., Mbuluyo, M., Debaille, V., Mango Itulamya, A. L., and Hubert-Ferrari, A.: Geology of the Isiro-Ngayu gold-bearing region, western belts of the Kibali granite-greenstone superterrane in the northeastern Congolese craton, Democratic Republic of Congo, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13796, https://doi.org/10.5194/egusphere-egu25-13796, 2025.

The Crustal Dynamics Data Information System (CDDIS) provides essential support for the Global Geodetic Observing System (GGOS) by operating a data and product archive for the main geodetic techniques.   As GGOS matures and grows, the CDDIS adopts the latest data practices to strengthen its support for the community and ensure quality products are available in a timely manner.  This poster explores the breadth of work done at the CDDIS and provides highlights of the latest developments including new data and product holdings, updates to provide clarity and usability for users, and updates on future works. Statistics on usage will also be provided.

How to cite: Woo, J.: The Crustal Dynamics Data Information System (CDDIS) Updates for 2025, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13924, https://doi.org/10.5194/egusphere-egu25-13924, 2025.

Abstract

Urban surface dust and soils serve as a primary source and reservoir of metals that substantially impact human health and urban ecosystems. This study investigates the impact of metal contamination on urban surface soils from diverse land-use locations and their potential risk to human health in Jammu City, India. A total of fifteen surface soil samples were collected to evaluate the total metal concentration (As, Cu, Fe, Mn, Ni and Zn), Contamination Factor (CF), Geo-accumulation Index (Igeo), Pollution Load Index (PLI), and Potential Ecological Risk Index (PERI). The research findings of this study revealed significant variation in metal concentration. In comparison to Upper Continental Crust (UCC, taken as background here), the average concentration of Fe and Mn is lower across all locations, whereas As, Ni, Cu, and Zn are significantly higher over all locations. Elevated levels of Fe and Mn were observed higher near samples collected from industrial zones while Ni, As, Cu and Zn showed wider distribution throughout the study area. Apart from all metals, high As content was observed at near-construction and high-traffic interactions. Higher CF (CF > 6) and PLI values in surface soil samples revealed high contamination of As, Cu, Ni and Zn due to intensive industrial and vehicular emissions in the study area. Igeo values in surface soil samples indicated severe contamination of As, Cu, Ni and ZN in the study area, while Fe and Mn showed no contamination. PERI assessment in surface soil samples revealed extremely high ecological risk for As and Cu in Jammu City. Risk index values indicated that 40% of surface soil samples carried a very high risk (RI > 600) of metal contamination in the study area. The overall findings advised that industrial, transportation, and construction activities need to be improved to protect the region's environment and public health.

Keywords: Heavy metals, geo-accumulation index (IGeo), risk assessment, roadside dust.

How to cite: Gorka, R. and Kumar, R.: Spatial Distribution and Contamination Levels of Heavy Metals (Fe, Mn, Ni, Cu, As, and Zn) in Urban Topsoils of Jammu City, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14122, https://doi.org/10.5194/egusphere-egu25-14122, 2025.

Low Earth Orbit (LEO) satellites have the advantages of high flight velocity and minimal influence from external environmental factors on onboard observation. Integrating LEO satellite observations with ground observations can improve the accuracy and convergence performance of GPS and LEO real-time orbit determination, which can simultaneously meet the prerequisites for real-time Positioning, Navigation, and Timing (PNT) services for both GPS and LEO systems. Therefore, this study employs the Square Root Information Filter (SRIF) for GPS and LEO satellites real-time joint orbit determination (RTJOD). Based on observations from eight existing scientific LEO satellites, a detailed study on RTJOD was conducted under two scenarios: one using observations from 100 global stations and the other using observations from 9 regional stations in Australia. The results show that, with 100 global stations, incorporating LEO observations can significantly improve the convergence performance and GPS satellite orbit accuracy. The convergence times in the Along-track, Cross-track, and Radial components are reduced from 3.5, 5.8, and 10.3 h to 0.9, 1.0, and 10.3 h, respectively. The accuracy improves from 5.8, 3.6, and 2.8 to 4.0 cm, 2.5 cm, and 2.5 cm. Additionally, the ambiguity resolution (AR) performance is significantly enhanced. The time required to achieve a 90% narrow-lane ambiguity fixing rate is reduced from 4.9 to 0.7 h. After AR, the orbit accuracy further improves to 3.1 cm, 2.3 cm, and 2.4 cm. In the case of the 9 regional stations in Australia, after incorporating LEO, the orbit accuracy of the float solution after convergence is comparable to that of the 100 global stations without LEO, with accuracies of 6.0, 4.8, and 2.9 cm in the three components. It is important to note that, due to insufficient observations in this case, AR does not result in any further improvement in accuracy. In addition, LEO can achieve orbit determination accuracy better than 5 cm within a short time in both station distribution scenarios. This ensures that RTJOD enables LEO and GPS to generate high-precision real-time orbits simultaneously. Finally, the processing time for each epoch in all scenarios is less than 5 seconds, ensuring that the GPS and LEO RTJOD can provide timely orbit updates.

How to cite: Lai, W., Huang, G., Wang, L., She, H., Xie, S., Xie, W., and Wang, Q.: Real-time high-precision joint orbit determination of GPS and LEO using SRIF, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15238, https://doi.org/10.5194/egusphere-egu25-15238, 2025.

Zenith Total Delay (ZTD) is a crucial parameter for understanding the effects of atmospheric conditions on satellite signals, constituting a fundamental aspect of precision positioning and atmospheric modeling applications. Traditional methods for ZTD estimation, including GNSS observations, numerical weather prediction models, and interpolation techniques, encounter critical limitations such as generalization constraints, sparse data availability, insufficient spatial coverage, high computational costs, and limited adaptability to dynamic atmospheric changes. Deep learning techniques provide substantial benefits, including processing large and complex datasets, enabling dynamic modeling, and delivering rapid and accurate estimations.

This study integrates real-time GNSS observations with high-resolution atmospheric reanalysis data from the ERA5 dataset to develop deep learning-based methods for ZTD estimation. GNSS data were sourced from 17 IGS tropospheric stations strategically selected to represent diverse geographic and climatic conditions. These stations supplied ZTD values and their temporal variations at 5-minute intervals, spanning February 2023 to January 2024. ERA5 data, offering hourly atmospheric parameters, necessitated the alignment of GNSS temporal resolution with ERA5 for spatial modeling. The spatial distribution of GNSS data was optimized using interpolation techniques to enhance the quality of inputs for deep-learning models.

The findings highlight the potential of deep learning techniques to enhance ZTD estimation processes. Future research will focus on integrating additional datasets, such as InSAR, to achieve higher spatial resolution and improved accuracy. Moreover, advanced deep learning architectures, including attention mechanisms, will be investigated to refine estimation methods and broaden their applications in atmospheric and geospatial studies.

How to cite: Tekin Ünlütürk, N. and Bak, M.: Deep Learning Approaches for Zenith Total Delay Estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15605, https://doi.org/10.5194/egusphere-egu25-15605, 2025.

A Global Geodetic Observing System (GGOS) affiliate is an organization or entity that collaborates with the Global Geodetic Observing System (GGOS) to enhance the global geodetic infrastructure and support the objectives of GGOS in a region.
With this goal, a GGOS affiliate was created to enhance geodetic infrastructure and scientific collaboration across the Iberian Peninsula and the Atlantic region. It is called GGOS IberAtlantic. This project focuses on improving the accuracy and reliability of geospatial data through the co-location and integration of geodetic space techniques to support various scientific and practical applications, including global reference frame maintenance, climate change monitoring, natural hazard assessment, in the perspective of a sustainable development. GGOS IberAtlantic aims to establish a robust network of geodetic stations, facilitate high-accuracy data collection, and promote international cooperation among geodetic institutions, contributing to a better understanding of Earth's dynamic processes. It is also focused on supporting decision-making in the area and bringing geodesy closer to society, specially to young scientists.
The upcoming presentation will outline the steps taken to establish the GGOS IberAtlantic group, as well as its future directions and objectives.

Acknowledgment. This presentation was supported partially by Spanish Project PID2020-119383GB-I00 funded by Ministerio de Ciencia e Innovación (MCIN/AEI/10.13039/501100011033)

How to cite: Azcue, E. and Ferrándiz Leal, J. M. and the GGOS IberAtlantic Governing Board: GGOS IberAtlantic Affiliate: Bringing Geodesy Closer to Society across the Iberian Peninsula and the Atlantic region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17154, https://doi.org/10.5194/egusphere-egu25-17154, 2025.

The neutral atmosphere that extending from the surface of earth to about 80 km overhead is the electrically neutral part (within a certain frequency band which GNSS signals fall) of the atmosphere. There is no doubt that neutral atmosphere has a delaying effect on transmitted radio waves. Spilker (1996) noted that the more precise term of this delaying effect is neutral atmosphere delay, even though this delaying effect has been traditionally referred to as just troposphere delay. At all events, the delaying effect has propagated into satellite observations, and we must deal with it appropriately in order to achieve precise satellite positioning results. There are many geodesists have been making their contributions to treatment of neutral atmosphere delay, and how to get satisfactory supports from numerical weather model data set is one of the efforts making to calibrate this delaying effect more precisely up-to-date. Currently, both Earth observation network and technology have great improvement, which results in wonderful increase of Earth observational data as well as the subsequent numerical weather model data set. Briefly speaking, numerical weather model data set which generally provided by different organizations and/or institutions is a global and/or regional gridded meteorological data set with specific temporal-spatial resolution. Generally, reanalysis data set and forecast data set are usually considered to be the two main data set representations, and they both provide two types of data level, i.e., three-dimensional pressure levels and two-dimensional surface level. The data set contains some usually used meteorological parameters, such as height, temperature, pressure, humidity. With these meteorological parameters, some main terms related to neutral atmosphere delay, such as hydrostatic/wet delay, gradient factors and mapping factors can all be calculated without any difficulty by using computing techniques like raytracing and interpolation. Undoubtedly, the performance of different types of data set that mentioned above in representing neutral atmosphere delay are not all the same. Definitely, some interesting and meaningful comparison results have found and widely propagated by many scholars. In this work, we put more emphasis on evaluation of the forecast data set from neutral atmosphere delay point of view, considering there is an objective fact that satellite positioning industry especially the (near) real-time positioning has vigorous development, in which the calibration of neutral atmosphere delay is required more and more accurate and timely-supported. Besides time-delayed reanalysis data set and time-advanced forecast data set, microwave radiometer data set and radiosonde data set are also employed. The first results show that empirical model such as UNB3 can only state the normal level of delaying effect and the obtained delay values are either larger or smaller; the pressure levels data set performs better than the surface level data set with very high proportion in time domain; even though reanalysis data set generally has good performance, forecast data set can work for the neutral atmosphere delay calibration with relatively satisfactory support in term of accuracy.

This work is supported by the National Natural Science Foundation of China (42304010), the Youth Foundation of Changzhou Institute of Technology (E3-6207-21-060, 31020222007).

How to cite: Wang, M.: First results about evaluation of forecasted numerical weather model data set in view of neutral atmosphere delay, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17506, https://doi.org/10.5194/egusphere-egu25-17506, 2025.

Tropospheric refraction is one of the major error sources in satellite-based positioning. The delay of radio signals caused by the troposphere ranges from 2m at the zenith to 20m at low elevation angles, depending on pressure, temperature and humidity along the path of the signal transmission. If the delay is not properly modeled, positioning accuracy can degrade significantly. Empirical tropospheric models, with or without meteorological observations, are used to correct these delays but they are limited in accuracy and spatial resolution resulting in up to a few decimeters error in positioning solutions. The present availability of ground-based GNSS networks and the state of the art of GNSS processing techniques enable precise estimation of Zenith Tropospheric Delays (ZTD) with different latency ranging from real time to post-processing.
We present a method for computing ZTD residual fields interpolating, through Ordinary Kriging, the residuals between GNSS-derived and model-computed ZTD at continuously operating GNSS stations. GNSS ZTD estimates, obtained in real time and in PPP mode, are augmented by a multi-prediction model based on a Graph Neural Network model trained using one year of Near Real Time ZTD observations and a model using a polynomial plus harmonic interpolation. A combination strategy is defined to merge GNSS ZTD estimates at sites with the predicted values, where predicted ZTD values act as hole fillers for stations missing from the GNSS network at the current epoch. The residual ZTD field, obtained from PPP/prediction model and ZTD empirical model, is modelled as a random process and for each epoch a variogram is estimated and fitted to characterize the spatial correlation of the process. At a known user location, ZTD value is obtained as the sum of site interpolated ZTD residual and modeled-ZTD value. The algorithm is validated with respect to GNSS ZTD estimates provided by an external provider at a selection of sites not included in the network used to fed the computation. Details about validation and possible improvements will be provided.

How to cite: Basoni, A., Pacione, R., Bagaglini, L., and Lanotte, R.: Real-Time ZTD correction grid based on augmented GNSS network for navigation services, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18280, https://doi.org/10.5194/egusphere-egu25-18280, 2025.

The University of Luxembourg (UL), in collaboration with the United Kingdom Met Office, continues to advance the provision of global and regional near real-time (NRT) Zenith Total Delays (ZTDs) from GNSS ground networks to support operational meteorological products within the EUMETNET EIG GNSS Water Vapour Programme (E-GVAP). E-GVAP facilitates coordination and uptake of NRT GNSS-based atmospheric monitoring, which is indispensable for assimilation in Numerical Weather Prediction (NWP) models across Europe, including at the Met Office, where high-temporal-resolution data enhance mesoscale weather forecasting. This study highlights the collaborative efforts of the Met Office and UL in delivering accurate, timely meteorological data from GNSS. The partnership has resulted in the development and enhancement of NRT processing systems using the state-of-the-art Bernese GNSS software version 5.4 (BSW5.4), generating ZTD products at both UL and the Met Office at 1-hour intervals globally and regionally, and at sub-hourly intervals regionally. Over the past year, UL has focused on developing hourly NRT ZTD solutions for global and regional networks, and more recently extending them to sub-hourly intervals (down to 15 minutes) for regional coverage, thereby refining the temporal resolution for E-GVAP users. In particular, we are now prepared to provide NRT products in the form of a global hourly product (ULGH), a regional hourly product (ULRH), and a regional sub-hourly product (ULRS) to E-GVAP. As part of the system's development, we validate our latest global, regional, and sub-hourly ZTD solutions against established NRT outputs from E-GVAP and benchmark post-processed Double-Difference Network (DDN) products, while also verifying Integrated Water Vapour (IWV) estimates against ECMWF Reanalysis v5 (ERA5). Finally, we highlight how higher-frequency updates can positively influence NWP assimilation in rapidly evolving weather situations, detailing data flow and latency management that ensure reliable NRT ZTD delivery to E-GVAP participants and the Met Office. By extending temporal coverage from hourly to sub-hourly in regional networks and continuing our global solutions, we advance the utility of GNSS-based atmospheric sensing for short-term weather forecasting, providing consistent, high-quality NRT GNSS products for meteorological operations in Europe and beyond. 

How to cite: Hunegnaw, A., Teferle, F., and Jones, J.: Extending Global and Regional Near Real-Time GNSS ZTD Solutions Using BSW5.4 at the University of Luxembourg: Contributions to E-GVAP , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18910, https://doi.org/10.5194/egusphere-egu25-18910, 2025.

The Geophysics Section of the Royal Institute and Observatory of the Navy (ROA) is structured into three main services: Seismology, Geomagnetism, and Space Geodesy, in addition to an auxiliary Meteorology service and participation in maritime scientific campaigns. Since its foundation, the ROA has played a pioneering role in Spain, being a member of the Spanish Commission of Geodesy and Geophysics and collaborating with international institutions across all its fields of activity, such as ILRS, IGS, INTERMAGNET, and GEOFON, as well as organizations like NASA and ESA, among others.

The Geomagnetism Service, established in 1879, studies the Earth's magnetic field and its variations to conduct scientific research. After several relocations due to electromagnetic interference, the current geomagnetic observatory is located at Cortijo de Garrapilos (Cádiz) and has been a member of INTERMAGNET since 2006. The Seismology Service dates back to 1898, when one of the 12 seismographs of the first global seismic network, promoted by geologist John Milne, was installed at the ROA. The current infrastructure is distributed across Spain and North Africa, including a short-period network for regional seismicity in the Gulf of Cádiz and the Alboran Sea, long-period stations for global seismicity, and the international Western Mediterranean network, in which prestigious institutions such as UCM and GFZ participate. The ROA has been involved in space geodesy with artificial satellites since the early days of the space era, starting just one year after the launch of the first SPUTNIK (1958) with the Baker-Nunn camera. This technique was followed by laser ranging (SLR) in 1975, when a station capable of tracking collaborative satellites was installed. By 1980, the station was exclusively operated by ROA personnel. Since then, the station has undergone constant upgrades to maintain a high level of operability. Today, it contributes to national and international tracking networks such as ILRS-EUROLAS and EU SST-S3T. Additionally, the ROA adopted GPS in the 1980s for geodetic studies and currently manages a GNSS network comprising 17 permanent stations spanning the southern Iberian Peninsula and North Africa. Maritime campaigns include studies in the Spanish Exclusive Economic Zone (EEZ), with objectives such as hydrographic surveys and geophysical exploration for seabed characterization. Since 1987, the ROA has also participated in Antarctic campaigns.

The Geophysics Section of the ROA combines tradition and advanced technology to contribute to the understanding of the Earth and space, consolidating its position as a national and international benchmark in the study of geophysical and geodetic processes. Evidence of this includes recent or ongoing scientific work over the past years: four doctoral theses (three of them in progress), various articles in high-impact journals, participation in numerous scientific projects, and extensive contributions to conferences. In this way, the ROA, through the Geophysics Section, fosters collaboration in geodesy through its active participation in international networks, addressing global scientific and societal challenges with cutting-edge technology and a multidisciplinary approach.

How to cite: Rodriguez Collantes, D., Sánchez Piedra, M. Á., Cabieces Díaz, R., and Fiz Barreda, J.: Scientific Legacy and Current Contributions of the Royal Institute and Observatory of the Spanish Navy: Impact on Geophysics, Geodesy, and other Scientific and Social Fields., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19802, https://doi.org/10.5194/egusphere-egu25-19802, 2025.

The accuracy and reliability of Earth Orientation Parameters (EOP) are significantly influenced by the geometric configuration of the Very Long Baseline Interferometry (VLBI) network. This astronomical technique employs a global network of radio telescopes to collect data. The distribution of VLBI antennas affects the triangulation process used to determine the positions of celestial sources, which is integral to the calculation of EOP. An optimal geometry yields more accurate and reliable EOP results, which are essential for many scientific applications.

This study examines the impact of different VLBI networks on EOP estimation, using data collected during several Continuous VLBI Campaigns (CONT) and designing alternative networks by removing various antennas and/or baselines from the original configuration. The results of this analysis aim to contribute to the refinement of EOP and the achievement of the stringent GGOS accuracy targets (i.e., a frame with accuracy at epoch of 1 mm or better and a stability of 0.1 mm/y).

How to cite: del Nido Herranz, L. D., Belda, S., Karbon, M., Ferrándiz, J. M., and Azcue Infanzón, E.: Influence of VLBI Network Geometry on the Estimation of Earth Orientation Parameters, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20077, https://doi.org/10.5194/egusphere-egu25-20077, 2025.

The Residual Terrain Modeling (RTM) technique is commonly used to recover short-wavelength gravity field signals. However, classical gravity forward modeling methods for RTM gravity field determination face challenges such as series divergence, inefficient computation, and errors induced by tree canopy in Digital Elevation Models (DEMs). In this study, deep learning methods are employed to enhance the quality of the computed RTM gravity field. Experiments are conducted at the Wudalianchi airborne gravity gradiometer test site, which provides a large volume of precise gravity measurements. The Random Forest method is used to estimate and correct tree canopy height errors in DEMs. A fully connected deep neural network (FC-DNN) is introduced to efficiently calculate the RTM gravity field. Additionally, to improve the network’s generalization capability, a novel terrain information fusion regularization method is applied to create an Improved FC-DNN with a refined loss function. The accuracy, computational efficiency, and generalization performance of the deep learning method are evaluated and compared in the Wudalianchi volcanic region. The results demonstrate a significant improvement in the accuracy of the RTM gravity field when based on tree canopy-corrected DEMs. The RTM gravity fields determined using both FC-DNN and Improved FC-DNN achieve mGal-level accuracy, with a remarkable 10,000-fold increase in computational efficiency compared to the classical Newtonian integration method. The Improved FC-DNN exhibits superior generalization, with accuracy enhancements ranging from 7% to 21% compared to the standard FC-DNN.

How to cite: Yang, M., Zhang, B., Wang, L., Feng, W., and Zhong, M.: Deep learning in RTM gravity field modeling: A case study over Wudalianchi area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20320, https://doi.org/10.5194/egusphere-egu25-20320, 2025.

Over the years, we have made significant updates to the Hungarian National Seismological Bulletin as velocity models, localization techniques, and station network configurations have evolved. A crucial aspect of our work involves addressing anthropogenic events since many recorded events initially classified as earthquakes are actually mining explosions, complicating geological interpretations. Since the Kövesligethy Radó Seismological Observatory has been collecting digital data since 1995, this is an ideal time for us to review the entire catalog using contemporary algorithms and velocity models.

By 2018, we had recalculated hypocenter parameters for 5,735 events (Bondár et al., 2018) using the 3D RSTT velocity model, which produced reliable results for Hungary. We have since expanded the dataset with an additional 6,578 events through December 2022, ensuring the inclusion of the highest-quality initial hypocenter parameters.

Our research has two main components: a comprehensive analysis of seismicity across the Pannonian Basin using the Bayesloc algorithm (1995-2022) and the precise relocation of specific event clusters using the double-difference method (e.g., Somogyszob, Szarvas, Móri-árok). Before conducting the Bayesloc analysis, we reviewed event types and identified hundreds as potentially anthropogenic. We also performed quality control, filtering events to retain those with favorable station geometry for accurate initial estimates.

For the Bayesloc analysis, we used GT2 events as reference points. Before finalizing results, we tested and documented the impacts of initial hypocenters, GT2 events, and a priori standard deviations on hypocenter parameters. This analysis included refining travel-time corrections, phase identification, and precision metrics to improve accuracy.

Our results showed reduced location errors and clear clustering, particularly for GT2 events, enabling more reliable geological interpretations. For local event clusters, the double-difference algorithm proved highly effective for small-scale studies, using differential times from waveform cross-correlation to achieve optimal relative positioning.

In this project, we made over 25 years of data compatible for simultaneous analysis, yielding the most reliable results for the Pannonian Basin to date and enabling improved seismic hazard assessments. Identifying and excluding anthropogenic events is crucial for accurate geological interpretation and seismic risk assessment. Our workflow also supports annual database updates and consistent processing of local event clusters.

How to cite: Czecze, B. and Bondár, I.: Review of Local and Regional Seismicity in the Carpathian Basin Using Multiple Event Location Algorithms with ground truth events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-37, https://doi.org/10.5194/egusphere-egu25-37, 2025.

Chile is one of the most seismically active countries in the world. In May 1960, two large earthquakes occurred along the subduction interface in southern Chile. The first took place on May 21st in Concepción, with a magnitude of Mw=8.1, and the second happened on May 22nd and corresponded to the world's most significant event recorded in instrumental history with a magnitude of Mw=9.5, popularly known as the Valdivia earthquake. Both events provoked considerable structural damage in some of the most important cities in the center and south of Chile, and the second one produced a transpacific tsunami, with casualties in Japan and Hawaii. Here, we investigate the interaction between the Concepción and Valdivia earthquakes, which occurred within a mere 33 hours of each other. According to previous studies, both earthquakes were initiated at similar locations and depths below the Arauco peninsula. We compute the Coulomb stress changes between these two seismic events to explore the increase of the regional stress produced by the Concepción earthquake into the Valdivia segment. We hypothesized that the Concepción earthquake promoted the rupture initiation of the second largest event, modifying their stress field and giving the conditions that lead to the mainshock nucleation. Our preliminary analysis indicates an interesting relationship between both earthquakes, which allows us to characterize and quantify these events' interactions at near distances and along the same subduction zone. Finally, a better understanding of the stress transference between these historical earthquakes gives us key information on the physical conditions for the nucleation of the largest earthquake ever recorded.

How to cite: Castro-Araya, F., Morales-Yañéz, C., González, J., and Ojeda, J.: Evaluation of the Coulomb stress changes between the 1960 Concepción and Valdivia earthquake in southern Chile., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-497, https://doi.org/10.5194/egusphere-egu25-497, 2025.

In anticipation to substitute the existing manual and semi-automated methods for classifying three categories of seismic events (quarry blasts, earthquakes, and noise), we have developed three different convolutional neural network (CNN) models. The first CNN model is based on extracting relevant features from seismograms (waveform), second is based on spectrograms (spectrum), and the third model uses a combination of these two respectively. The CNNs were trained using a labeled seismological waveform dataset recorded at a station SUR from GSNet (Gujarat) during the years 2007-2022. Generally, a common limitation in applying any deep learning techniques is the limited labelled dataset. Therefore, we utilised SeisAug, a Data augmentation (DA) python toolkit to address this challenge to significantly mitigate overfitting by increasing the volume of training data and introducing variability, thereby improving the model's performance on unseen data. A total of 3414 x 3 waveforms were extracted from the three categories of seismic events with a uniform data length of 180 s, considering factors such as coda length, which varies with magnitude and epicentral distance. From this dataset, 15% of the data belonging to each category was split for testing and remaining data was augmented using ‘SeisAug’ toolkit and used for training. The waveform model (WF), spectrogram model (SPEC), and combined model (COM) produced accuracies of 95.32%, 93.13%, and 93.96%, respectively. The robustness of the developed models is indicated by high F1-scores (WF > 0.91, SPEC > 0.92, COM > 0.97) and high area under the curve (AUC) values (WF > 0.98, SPEC > 0.93, COM > 0.98). The high F-scores indicate that these models are very well trained and the probability/possibility of false positives or false negatives is minimum. The high AUC indicates that the model performs well across a range of thresholds and can effectively distinguish between different seismic events. Further, these models produced accuracies of >90% and 100% when tested on completely new datasets from SCEDC and Palitana region (Gujarat) respectively. 

How to cite: Pragnath, D., Srijayanthi, G., Kumar, S., and Chopra, S.: Enhancing Seismic Event Classification in Gujarat Through SeisAug-DrivenData Augmentation for Deep Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-911, https://doi.org/10.5194/egusphere-egu25-911, 2025.

To understand the ubiquitous nature of the Precambrian shield, we investigated the structure of the mantle transition zone (MTZ) beneath the Canadian, Brazilian, Baltic, African, and Australian Shields. Receiver Functions were computed from data collected from various stations sampling these regions, and the topography of the MTZ boundaries was mapped using depth-migrated RF images generated with two 3D tomographic velocity models, LLNL_G3D_JPS and GyPSuM. The depth-migrated images from both models reveal a thinner-than-usual MTZ beneath all the Precambrian shields, with an average thickness of approximately 238 ± 8 km. The upper boundary of the MTZ (the 410 km discontinuity) shows distinct topography, while the lower boundary (the 660 km discontinuity) is found at shallower depths. This suggests that the main cause of MTZ thickness variation is the shallowing of the 660 km discontinuity, pointing to a post-spinel transition occurring at higher temperatures with a negative Clapeyron slope, supporting the theory of whole-mantle convection. These results suggest that mantle plumes have appreciable influence the MTZ beneath Precambrian shields, extending to its base, which also gains support (/may also be accounted by) from the global mantle warming observations at the 660 km discontinuity. Further numerical modeling/ simulation studies are needed to test this hypotheses.

How to cite: Srinu, U. and Rao B, P. R.: Is the mantle transition zone uniform beneath Precambrian shields?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-915, https://doi.org/10.5194/egusphere-egu25-915, 2025.

The quest for reliable earthquake prediction has led researchers to explore various unconventional methods, one of which involves the study of animal behaviour. Observations have shown that some animals exhibit unusual behaviours before seismic events, suggesting a potential link between animal activities and impending earthquakes. This abstract summarizes the emerging research on using animal behaviour as an indicator for earthquake prediction. Studies have documented phenomena such as increased agitation, abnormal movements, and changes in vocalizations among various species prior to seismic occurrences. These behaviours are hypothesized to be responses to subtle environmental changes, such as shifts in electromagnetic fields, ground vibrations, or gas emissions, which are imperceptible to humans. By systematically recording and analysing these behavioural changes in different animal species, researchers aim to develop predictive models that could serve as an early warning system. The integration of animal behaviour data with traditional seismological methods may enhance the accuracy of earthquake forecasts and contribute to disaster preparedness and risk reduction strategies. Despite promising preliminary findings, further research is needed to establish standardized protocols and validate the reliability of these biological indicators in various seismic contexts.

How to cite: Kumar, Y.: A Review: on the Animal abnormal behaviour during the Earthquake or Before Earthquake , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1345, https://doi.org/10.5194/egusphere-egu25-1345, 2025.

The Xiaojiang Fault Zone, situated in southeastern Tibet, is renowned for its intense seismic activity. Studies have identified areas of high slip rate and seismic gaps that could indicate future large earthquakes. In our latest research, we look at the detailed seismic structure of the northern Xiaojiang fault and its surroundings using data from 68 seismic stations of dense networks. By analyzing these data, we identify two types of fault zone head waves (FZHW) through a combination of automatic picking, manual selection, and horizontal particle motion analysis of seismic data of local events. The first type of FZHW is detected at four stations, indicating a 2%-4% P-wave velocity contrast across the fault. The second type of FZHW is recorded at eight stations from five clusters of earthquakes primarily in and around localized low-velocity zones.  To bolster our findings, we employed teleseismic P-wave arrival time delays between station pairs, which confirmed the FZHW analysis with a velocity contrast ranging from 1%-5%. A joint analysis of FZHWs and teleseismic P-wave arrival times reveals a sharp transition in velocity contrast across the fault near 26.7°N.To the north of this latitude, the west side of the fault exhibits lower velocities, suggesting the preferred rupture direction for a future earthquake would be from north to south. Conversely, to the south, the east side shows lower velocities, indicating a northward rupture direction. This structural variation suggests that an earthquake may be unable to rupture the entire NXJF, placing limits on the maximum size of potential earthquakes in the region. 

How to cite: Zheng, X., Zhao, C., Guo, W., and Niu, F.: Seismic Imaging of the North Xiaojiang Fault with Fault Zone Head Waves and Teleseismic P-Wave Arrivals, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2033, https://doi.org/10.5194/egusphere-egu25-2033, 2025.

The kinematic characteristics of an earthquake source offer insights into its physical properties, with macroscopic parameters such as seismic moment and radiated energy serving as key indicators for describing seismic hazards. In this study, we explored the characteristics of earthquake sources in the frequency domain. Specifically, we introduced a hybrid method using teleseismic P-wave data to derive source spectra for over 200 large (M_W > 7) shallow (depth < 70 km) earthquakes from 2000 to 2024. Our analysis reveals that in a simple ω^-n model, the power exponent n is consistently less than 2 when the corner frequency aligns with the earthquake's total duration. To further characterize earthquake source properties, we defined the centroid frequency (f_e) as the centroid of the energy spectral density which can represent the dimension of asperities on the fault plane. We also proposed the ratio between observed radiated energy and that predicted by the ω^-² decay model as a metric for quantifying rupture heterogeneity (R_H). Relationships of f_e and R_H with focal mechanism, magnitude, and centroid depth were analyzed within the conventional asperity model framework. Our source spectra dataset can also provide a robust foundation for future investigations into earthquake sources.

How to cite: Jiangcheng, Z. and Yong, Z.: Source Spectra for Global Shallow Large Earthquakes from 2000 to 2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2135, https://doi.org/10.5194/egusphere-egu25-2135, 2025.

Taiwan is not large in area, but it has a complex geological structure due to intense orogeny. From east to west, Taiwan can be divided into five major geological zones: the Coastal Range, the Central Range, the Hsuehshan Range, the Western Foothills, and the Coastal Plain. The southwestern region of Taiwan primarily comprises the Western Foothills and the Coastal Plain. The Western Foothills belong to the foreland of western Taiwan orogen. During the Oligocene, the rifting of the South China Sea led to the development of several east-west-oriented normal faults. Subsequently, in the late Quaternary, the northwestward compression of the Philippine Sea Plate caused a series of north-south-oriented, fold-thrust fault zones in the Western Foothills, and reactivating the normal faults formed during the extensional period. In recent years, most destructive earthquakes have occurred at the deformation front of the orogenic belt, making the study of the region's structural characteristics an important topic. This research utilizes data from a dense seismic array and employs nonlinear inversion of receiver functions and the double beamforming method to investigate the shallow subsurface structure of the Western Foothills. The goal is to provide a more detailed understanding of the structure, which can improve the precision of earthquake location and deepen our knowledge of the area's tectonics.

How to cite: Su, C.-M., Wen, S., and Wen, Y.-Y.: Investigation of Shallow Structure at the Western Front of Taiwan Orogen Applying Nonlinear Search Method, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3445, https://doi.org/10.5194/egusphere-egu25-3445, 2025.

    SeisComP (Seismological Communication Processor) is an open-source, free seismic monitoring software that features an automated seismic data processing workflow, flexible database integration, and data interface capabilities. This study integrates SeisComP with the scanloc module, ETHZ-SED SeisComP Earthquake Early Warning (EEW) system algorithms, and SeisBench to develop three distinct seismic monitoring systems, optimizing three key tasks for the Central Weather Administration (CWA): earthquake early warning, seismic activity analysis, and global earthquake data acquisition. During the M_L 7.2 Hualien earthquake on April 3, 2024, at 7:58 AM (UTC+8) CWA, in collaboration with ETHZ-SED, applied the EEW algorithms, including the Virtual Seismologist (VS) and Finite-Fault Rupture Detector (FinDer). Both algorithms, tested in parallel at the time, successfully generated complete results within 26 seconds of the earthquake’s origin. Based on these results, Public Warning System (PWS) alerts would have been issued for 17 out of 19 counties in Taiwan, thereby supporting CWA’s existing system. For the seismic activity analysis system, which integrates SeisComP, SeisBench, and the scanloc module, 3,789 automatic location results were produced within three days of the event. Compared to 604 official earthquake reports from CWA, the horizontal location error was approximately 4 km, the depth error 5 km, and the magnitude error 0.17. These results demonstrate the system’s ability to quickly assess seismic activity and estimate subsequent disaster risks. It also has the potential to automate earthquake catalog creation and reduce manual workload. In the global earthquake monitoring system, data is received from IRIS and GEOFON, currently generating results for earthquakes with magnitudes of 6.0 or larger and depths of 30 km or less in the Pacific region. In addition to providing valuable data for tsunami simulations, the system utilizes the global network to calculate Moment Magnitude Mw, which is derived from broadband P-wave amplitudes. For example, the system calculated a M_w of 7.4 for the 2024 Hualien event, which closely matched the magnitude result reported by the USGS. This helps avoid saturation issues with CWA’s M_L estimation, particularly for larger earthquakes, and provides a more accurate measurement of earthquake size and dynamics, ultimately enhancing the system’s ability to monitor and assess earthquake risk. This study successfully tested the use of SeisComP in the aforementioned tasks. Although discrepancies remain between automatic results and the official catalog, ongoing testing and parameter optimization are expected to significantly enhance Taiwan’s earthquake monitoring capabilities and integrate more seismic data, ultimately improving the quality of earthquake monitoring services.

Keywords: SeisComP, earthquake early warning, earthquake monitoring 

How to cite: Song, G.-Y., Chen, D.-Y., Wu, Y.-M., Böse, M., Clinton, J., and Massin, F.: Application of SeisComP at the Central Weather Administration (CWA) for Earthquake Monitoring and Early Warning: A Case Study of the 2024 ML 7.2 Hualien, Taiwan, Earthquake Sequence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5275, https://doi.org/10.5194/egusphere-egu25-5275, 2025.

In 2024, the Mw 7.4 eastern Taiwan earthquake highlights Taiwan’s status as one of the most seismically active areas, driven by the complex interaction between the European Plate (EP) and Philippine Sea Plate (PSP). This dense seismicity provides a unique opportunity to investigate the earthquake rupture properties. The corner frequency, a key parameter for understanding kinematic rupture properties, is challenging to measure due to the site effect and the trade-off between corner frequency and attenuation parameter t*. Jian and Kuo (2024) proposed the Cluster Event Method 2 (CEM2), which mitigates these challenges by incorporating joint datasets of spectra and spectral ratios. This study applied CEM2 to the eastern Taiwan earthquakes recorded by local broadband stations. Site-effect patterns were first retrieved during the initial inversion stage for each station, enabling corrected corner frequency estimates in the subsequent inversion. Using the relationship of stress drop, corner frequency and seismic velocity at the source area (Madariaga, 1976), we calculate the stress drop (SD) for individual events. To prevent overestimation caused by extreme values, we calculated the average SD in log-scale. The overall average SD for earthquakes shallower than 40 km is 50 MPa. Our results reveal significant spatial variations in SD across the Taiwan collision zone. In the southern collision zone, SD in the PSP are approximately twice as high as those in the EP. To the north, where the PSP subducts beneath the EP, SD increases with longitude and depth. However, west of the Longitudinal Valley Fault (LvF), the vertical variation of SD is reversed: shallow SD (<10 km) is about twice as high as deep SD (>10 km). Considering self-similarity model of faults with constant stress drop of earthquakes, further exploration of the parameters controlling tectonic-related stress drop variability are necessary. Overall, earthquakes within the PSP exhibit higher stress drops than those in the EP. These findings provide valuable insights for different earthquake rupture properties in the tectonically complex region.

How to cite: Jian, P.-R., Tseng, T.-L., and Kuo, B.-Y.: Variations in earthquake source properties across the Taiwan collision zone, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5398, https://doi.org/10.5194/egusphere-egu25-5398, 2025.

Taiwan is situated at a complex plate boundary, and numerous velocity models have been published, providing significant insights into large-scale structural features. However, detailed shallow structures remain inadequately resolved. Gravity data, with its sensitivity to lateral variations in shallow regions, offers an excellent opportunity to address these limitations. This study aims to integrate gravity and seismic data through sequential inversion to develop a more precise structural model.

Seismic data will include travel-time records from the Central Weather Administration (CWA) seismic network and temporary stations deployed in mountainous areas by National Central University over the past decade, ensuring comprehensive station coverage. The seismic inversion will employ the tomoDD method, significantly improving structural resolution in regions with dense seismicity and enhancing earthquake location accuracy. The results will yield models of Vp, Vs, and Vp/Vs.

Gravity inversion will utilize free-air gravity data to resolve velocity and density variations within terrains characterized by topographic relief. By integrating terrestrial, marine, and airborne gravity data, the study ensures robust constraints on both shallow and deep structures. Wavelength analysis of Bouguer gravity anomalies will further distinguish shallow and deep residual gravity effects, elucidating their relationship with tectonic structures.

Finally, a relationship between seismic velocity and density will link the two distinct physical observations, enabling a sequential inversion that incorporates both gravity and seismic data. This approach will yield a subsurface model that aligns with both datasets. By adopting this innovative inversion strategy, the study aims to produce an improved three-dimensional velocity and density model for Taiwan.

How to cite: Lo, Y.-T. and Yen, H.-Y.: Enhancing 3D Density and Velocity Models of Taiwan Using Sequential Inversion, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5436, https://doi.org/10.5194/egusphere-egu25-5436, 2025.

Taiwan lies at the convergent boundary between the Eurasian Plate and the Philippine Sea Plate. In northeastern Taiwan, at the Ryukyu subduction zone, the Philippine Sea Plate subducts northward beneath the Eurasian Plate. This tectonic interaction has formed a series of geological features, including the Ryukyu Trench, the Ryukyu Arc, and the extensional Okinawa Trough. This study area is significantly influenced by both the extension of the trough and the plates subduction. As a result of these tectonic processes, this region experiences notable igneous activity, such as the formation of Kueishan Island and the Tatun Volcano Group. Additionally, extensional activities related to plate rifting are observed beneath the Ilan Plain.

In this study, we apply the Sequential Inversion method, utilizing seismic data provided by the Central Weather Bureau. The analysis applies Double-Difference Tomography to construct a three-dimensional velocity model of the study region. The initial model is based on the 3-D velocity structure proposed by Su et al. (2019). To address the limited resolution of seismic waves in the shallow subsurface, gravity data are integrated to enhance the resolution of shallow structures. A velocity-density conversion formula is used to transform the velocity model into a corresponding density model for gravity inversion. The resulting density model is subsequently converted back into a velocity model, completing the first iteration of the sequential inversion process.

Through multiple iterations of sequential inversion incorporating both gravity and seismic datasets, final velocity and density models are obtained that align well with observed travel-time and gravity data. These results demonstrate a significant improvement in the resolution of shallow structures, with residuals for both velocity and density models exhibiting stable convergence. In the shallow subsurface, low P-wave velocity (Vp) values are associated with the extensional basin of the Ilan Plain, whereas higher Vp values further south correspond to the topographic relief of the northern Central Range. Furthermore, Vp/Vs ratios are utilized to infer rock properties, providing insights into magmatic intrusions in the Ilan region and the geothermal heat source of the Chingshui area.

How to cite: Chiu, P. H., Yen, H. Y., and Lo, Y. T.: The 3-D velocity and density structure of NE Taiwan inferred from seismic and gravity data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5520, https://doi.org/10.5194/egusphere-egu25-5520, 2025.

The Haichenghe fault zone (HFZ), renowned as the site of a successfully predicted earthquake, namely the 1975 M7.3 Haicheng earthquake, as well as the 1999 M5.4 Xiuyan earthquake, is one of the most seismically active zones in eastern China. Nevertheless, fault structures within the key areas of the HFZ remain inadequately characterized, which limits the understanding of the fault behavior and seismological mechanism of the region. Consequently, from August 2021 to September 2023, we deployed the densest array to date, consisting of 23 broadband seismic stations with an average distance interval of about 6 km. Based on the dense observation data, we constructed a high-precision catalog of the HFZ utilizing neural network-based phase picking, earthquake association, and relocation methods. Our results show that:

• The HFZ is characterized by a conjugate fault system composed of WNW-striking and NE-striking subvertical faults of different scales. The Haichenghe Fault (HF) appears as a WNW-trending en echelon fault, traversing the entire study area. The 30-kilometer-long main segment (MHF) in the northwest is responsible for the Haicheng 7.3 earthquake, while the 5-kilometer-long Xiuyan segment (XYF) in the southeast generated the Xiuyan M5.4 earthquake.
• The MHF is further segmented into eastern and western sections. A several-kilometer-wide step-over between two sections is connected by two NE/NNE trending faults. It can be deduced that the rupture of the Haicheng earthquake along the WNW direction was not continuous throughout, but terminated or transferred to these two NE/NEE trending faults at the step-over.
• At the intersection of the MHF and its main NE-trending conjugate fault, a horizontally asymmetric conjugate rupture area of the Haicheng M7.3 earthquake has been identified. Moreover, a vertically triangular seismic gap has been discovered, suggesting a strong heterogeneity of the subsurface medium in this region.

How to cite: Bai, L., Sun, Q., Jiao, M., Wang, L., Yang, S., Wang, J., Li, E., and Wu, Q.: New insights into fault structures of the Haichenghe Fault Zone through dense array observation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5539, https://doi.org/10.5194/egusphere-egu25-5539, 2025.

For eastern Himalayan segments the Shillong block is distinguished by clustered seismicity along the Kopili Fault Zone (KFZ) and the Dhubri-Chungthang Fault Zone (DCFZ). The foreland seismicity patterns of the KFZ and DCFZ seem to penetrate the higher Himalayan mountain belts. To understand the ability of the DCFZ in generating future great earthquakes and its role in segmentation of Himalaya which affects regional seismicity pattern, a network of 54 broadband seismic stations was installed in two phases (2018-2023, 2023-continuing) covering Sikkim and Foreland Basin along the DCFZ. Preliminary results suggest concentrated seismicity along the DCFZ. Earthquakes are of mid-crustal origin, akin to results from earlier experiments. Focal mechanisms are showing dominantly strike-slip nature of the fault zone. Crustal structure obtained using receiver functions have shown a very complex crust with prominent offsets and overlaps. The thick sedimentation in foredeep is revealed with large amplitude arrivals close to 1 s. Moho arrivals show a thick crust beneath Himalaya and its foredeep. Highly deformed middle crust show presence of dipping and anisotropic layers, evident in the backazimuthal stacks of receiver functions. The seismicity and crustal deformation with presence of dipping and anisotropic structures suggest presence of a sheared fault zone, though extent of this zone remains unclear in the preliminary results. Ongoing experiment with continuous monitoring of seismicity of DCFZ, will help to resolve critical issues, like depth extent, dominant fault mechanisms and penetration of the DCFZ within the Himalaya, its role in segmentation of the Himalayan arc and defining seismogenic boundaries and rupture zones of possible major earthquakes.

How to cite: Singh, A., Bala, S., Singh, C., Singh, P. K., Dubey, A. K., and Tiwari, A. K.: Seismogenesis of earthquakes in the Dhubri-Chungthang fault zone, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6039, https://doi.org/10.5194/egusphere-egu25-6039, 2025.

The seismic activity of the Hälsingland region on the northeast coast of Sweden, about 270 km north of Stockholm, presents an intriguing case study of intraplate seismicity in a low-seismicity setting. The activity consists of an elongated earthquake cluster, about 100 km in length, stretching from inland in the southwest to off-shore in the northeast, at approximately 45 degree angle to the coast line. In contrast to most of the main earthquake clusters in Sweden, this Hälsingland earthquake cluster has not yet been associated with any distinct geological feature, such as a postglacial fault or a major deformation zone. In 2021, a network consisting of thirteen temporary broadband seismic stations was installed in the Hälsingland area, as part of a joint study between Uppsala University and the Geological Survey of Sweden, with the aim of better understanding the drivers of the Hälsingland seismicity. The addition of the temporary network has increased detectability by almost a factor six compared with the permanent seismic network operated by the Swedish National Seismic Network. With the aid of an automatic processing system and an in-house, machine learning based classification system, we have extracted and manually analyzed a catalog of about 900 earthquakes, and 50 industrial blasts in the area, with origin times between September, 2021 and December, 2024. The analyzed earthquakes have local magnitudes ranging from -0.2 to 2.9. This presentation will focus on the preliminary seismic results from the study. We derive a new, improved, one-dimensional seismic velocity model for the Hälsingland area, using data from analyzed blasts, and re-locate the analyzed earthquakes in this improved velocity model. We identify the presence of a swarm of highly similar earthquakes, closely located in space, using waveform cross-correlation of analyzed earthquakes and a subsequent cross-correlation search through the continuous waveform data. Finally, we perform relative re-locations of the analyzed earthquakes, based on differential travel times, and calculate focal mechanisms to study potentially activated fault planes.

How to cite: Eggertsson, G., Lund, B., Gudmundsson, O., and Roth, M.: A study of the intraplate Hälsingland earthquake cluster in central Sweden, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6595, https://doi.org/10.5194/egusphere-egu25-6595, 2025.

Large earthquakes can trigger other earthquakes at great distances, even on a global scale, through the dynamic stresses imparted by their seismic waves. However, identifying such remote triggering is not always straightforward. It may occur not only instantaneously, during the passage of the seismic waves, but also with a delayed effect—sometimes days or even weeks after the initial event. Recognizing this phenomenon in historical earthquake catalogues, which are incomplete, is particularly challenging, yet it is essential for better understanding its occurrence following rare, large earthquakes and eventually assessing its impact on global, time-dependent seismic hazard.

In this study, we report what may be the oldest documented case of global earthquake triggering, dating back to the eighteenth century. We first integrated and revised global and regional earthquake catalogues from multiple continents, spanning a period of ten years before and after the main earthquake, in order to create a more comprehensive and reliable global dataset. Just after this main event, we observed a statistically significant increase in seismic activity, even at distances greater than 2000 km from the epicentre, followed by an Omori-like decay.

We then modelled the expected global seismic wavefield for the initial earthquake to test whether it could have triggered subsequent remote earthquakes occurred across several continents. For this modelling, we considered the likely magnitude and location of the main earthquake, as well as the focal mechanism of a large, instrumental earthquake most likely caused by the same fault, according to prior research. The subsequent earthquakes occurred precisely in the regions where the calculated dynamic stresses induced by the main earthquake were largest, strongly supporting the hypothesis that they were dynamically triggered.

How to cite: González, Á., Crespo, C., Heimann, S., and Corral, Á.: The quest for the oldest case of global earthquake triggering, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10047, https://doi.org/10.5194/egusphere-egu25-10047, 2025.

Geothermal energy offers great potential for supplying heat to the large district heating networks common in central Europe and is a viable substitute for the heat fed into the district heating by coal-fired power plants that will soon be decommissioned. In the Lower Rhine Embayment, efforts are ongoing to explore the region's geothermal potential. To facilitate any future drilling and testing activities, we contribute in a seismotectonic prescreening of the study area. However, because of various anthropogenic noise sources, including open pit mining, industrial activity and densely populated urban areas, the region has an elevated noise level, influencing the seismological data quality. Furthermore, soft sediments dipping towards the northeast cause local site amplification effects. The thickness of these sediments varies greatly from almost non-existent up to 600 m. Thus, noise levels and general data quality differ strongly across the region. To overcome these restrictions, we have gradually optimized and focused our seismological monitoring in the region over the last couple of years.

As an addon to existing, permanent monitoring stations from Earthquake Observatory Bensberg (FDSN-network code BQ), Royal Observatory of Belgium (BE), the Royal Netherlands Meteorological Institute (NL), and the Geological Survey of North Rhine-Westphalia (NH), we first deployed a dense network (ZB) operating from 2021 to 2022 and consisting of 48 broadband and short period stations to investigate background seismicity and the spatially resolved noise level using probabilistic power spectral densities (PPSD). The PPSDs show the correlation of the noise level with the sediment thickness and reveal high seismic noise, especially in the north-eastern region. The seismicity is moderate and concentrated in the western part.

Following the ZB network, we currently operate a research network (YV) of eight broadband stations. The station locations were optimized using the information gained from the ZB network to deploy the best quality stations with a focus on the area where most natural seismicity occurs. One broadband sensor was deployed in a 100 m deep well, about 30 meters below the softer sediments in the center of the region to remove the effect of site amplification and anthropogenic noise as much as possible.

To prepare upcoming drilling activities, additional five stations are deployed surrounding the potential drill site at the Weisweiler power plant. We interpolate I95 values to find potential station locations with a low noise level, based on the noise levels observed on the ZB network and taking into account the existing YV stations. Different network geometries resulting from the potential locations are investigated for optimum accuracy and magnitude of completeness up to the anticipated drilling depth of about 3 km.

The optimal model promises a magnitude of completeness of down to 0.5 in the target region at the Weisweiler power plant at a depth of 3 km, showing that all potential events with a magnitude > 0.5 will be detectable. We present the used workflow, show modeling results and the evolution of data quality over the last years.

How to cite: Dietl, M., Moutote, L., Kremers, S., Roth, M. P., Carrasco, S., Neugebauer, S., Fazlibašić, N., and Finger, C.: Seismic monitoring network design in the Lower Rhine Embayment using pre-existing dense deployments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10087, https://doi.org/10.5194/egusphere-egu25-10087, 2025.

The objective of this work is the estimation of the moment tensor, with particular attention to the analysis of the full moment tensor, to kinematically characterize the seismogenic sources in the shallow crust of Mt. Pollino area (Southern Italy). The moment tensor analysis is an indispensable tool for understanding the rupture process, determining the fault geometry, the characteristics of the medium in the seismogenic volume, and the acting stress field. In regions with a complex tectonic structure, such as the Mt. Pollino area, the double couple model may not be sufficient for characterizing seismogenic sources. The inversion of the full moment tensor is essential to analyze also the non-double couple components, which can give very important insights in case of complex sources. Furthermore, the full moment tensor solution is of fundamental importance for verifying and estimating possible volumetric variations in the focal volume.

In this study, the dataset consists of 65 earthquakes that occurred in the Pollino area between latitudes 39.70 and 40.10 and longitudes 15.80 and 16.30 from 2010 to 2024, characterized by magnitudes 2.5<M<5.0 and a maximum depth of 10 km. The inversion of the full moment tensor was performed using "Grond" (Heimann et al. 2018), a software based on the comparison between synthetic and observed signals through a Bayesian probabilistic approach. We used seismic data at local to regional distances; the data were deconvolved to remove the instrumental response and transformed into displacement. The inversion is based on the fitting of full waveforms, on the three components, in the 0.04 - 0.10 Hz frequency band. The inversion was performed under the hypothesis of point source by applying the L1 norm for the “double couple”, “deviatoric” and “full” moment tensor configurations, using three different crustal velocity models to check the stability of the results. We obtained a stable solution for 50 earthquakes, all of them characterized by normal kinematics with strike in the NW-SE direction, and a predominant positive isotropic component. Positive values up to 53% of the isotropic component indicate tensile opening processes, thus suggesting that the seismicity may be affected by fluid transfer.

How to cite: Ponte, M., Cesca, S., Büyükakpınar, P., Calderoni, G., and La Rocca, M.: Full Moment Tensor Inversion for the Characterization of Seismogenic Sources in the Pollino Area (Italy) , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10416, https://doi.org/10.5194/egusphere-egu25-10416, 2025.

The Western Foothills Belt of Taiwan, shaped by the collision between the Eurasian and Philippine Sea plates, exhibits high seismicity concentrated along its deformation front. For example, Chongpu region in southwestern, Taiwan, characterized by active thrust faulting and fold structures, represents a zone of elevated seismic hazard. The most significant historical event in the area was the 1941 Chongpu earthquake (ML 7.1). Since the 1999 Chi-Chi earthquake (Mw 7.6), however, seismicity has predominantly involved smaller earthquakes. Notably, two earthquake swarms near the 1941 epicenter in 2017 and 2018 have raised questions about their potential implications. Similar phenomena have been observed in the Noto Peninsula, Japan, where prolonged earthquake swarms preceded the 2024 Mw 7.5 Noto earthquake. Previous research highlights the highly fractured subsurface environment beneath the Chongpu region, suggesting the possibility of analogous swarm patterns. This study aims to investigate the spatiotemporal characteristics of recent earthquake cluster in the Chongpu area, examining their relationship to the 1941 event. By conducting seismic data inversion to derive subsurface imaging, integrating these results with structural analysis of fault-fold systems, and assessing stress distributions from geological surveys, this research seeks to elucidate the seismogenic processes at the deformation front. The findings are expected to enhance the understanding of earthquake clusters mechanisms, evaluate their potential as precursors to major seismic events, and contribute to improved earthquake hazard assessment and disaster mitigation in urban settings.

How to cite: Lai, C.-N., Wen, S., and Chen, Y.-N.: Tectonic features of the deformation front in western Taiwan and implications for recent earthquake clusters, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12368, https://doi.org/10.5194/egusphere-egu25-12368, 2025.

Constraining upward mantle flow is essential for understanding global mantle dynamics and linking Earth's interior with surface processes. The ERC-funded UPFLOW (Upward Mantle Flow from Novel Seismic Observations) project addresses the limited understanding of mantle upwellings connecting the deep mantle to the surface by utilising advanced seismic imaging methods and conducting extensive data collection.

Between June 2021 and September 2022, UPFLOW deployed 50 and successfully recovered 49 ocean bottom seismometers (OBSs) across a ~1,000×2,000 km² area in the Azores-Madeira-Canaries region, with an average station spacing of ~150-200 km. This multinational collaboration involved institutions from Portugal (IPMA, IDL, Univ. of Lisbon, ISEL), Ireland (DIAS), the UK (UCL), Spain (ROA), and Germany (Potsdam University, GFZ, GEOMAR, AWI). The deployment utilized three OBS frame designs equipped with three-component wideband seismic sensors and hydrophones. The dataset exhibits high-quality recordings, including teleseismic, local seismic events, and non-seismic signals (e.g., whales, ships, and the Tonga eruption), with significant noise reduction observed in vertical component long-period data (T > ~30 s).

Initial tomographic results feature a preliminary P-wave model derived from ~8,000 multi-frequency (T ~2.7-30 s) body-wave travel time cross-correlation measurements and over 120 teleseismic events. Integrating UPFLOW's OBS data with global seismic datasets from temporary and permanent stations expands the dataset to approximately 600,000 multifrequency measurements. This comprehensive approach enables the construction of a global P-wave model with enhanced resolution throughout the entire mantle beneath the Azores-Madeira-Canaries region. We compare our new model with existing global tomography models and discuss its geodynamical implications in terms of mantle upwelling processes and their surface expressions.

How to cite: Tsekhmistrenko, M., Ferreira, A., and Miranda, M.: Unveiling Deep Earth Mantle Structures Beneath the Azores-Madeira-Canaries with UPFLOW data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12389, https://doi.org/10.5194/egusphere-egu25-12389, 2025.

Reliable seismic source parameter estimates are essential for making effective seismic hazard assessments in earthquake prone regions. The Sea of Marmara, NW Türkiye, and its environs located on a major fault with a history of destructive earthquakes. The utilization of seismic coda waves has a long history in providing valuable insights into the source properties, energy release at the foci, and attenuation characteristics of the Earth's crust. In the study region where we have previously demonstrated efficiency of the automated coda wave analysis with robust seismic moment and energy estimates using Qopen approach that enables modeling coda wave envelopes via a non-empirical inversion procedure to provide earthquake source properties. Here we aim to extend the initial framework by integrating comparative insights with the Coda Calibration Tool (CCT), a well-established empirical coda wave technique, to further develop a local earthquake catalogue with relatively small events (M_w ≤ 2.5) following the coda calibration of events between 2018 and 2020 with magnitudes 2.5 ≤ M_L ≤ 5.7. The findings of this study will provide a basis for comparison between empirical and non-empirical coda approaches, which has not been done previously in this region.

How to cite: Özkan, B., Eken, T., Mayeda, K., Barno, J., and Taymaz, T.: Source Parameter Estimates and Energy Scaling of Small-to-Moderate Earthquakes in the Marmara Sea Region, NW Türkiye: A Comparative Study Using Coda Wave Analysis with CCT, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13720, https://doi.org/10.5194/egusphere-egu25-13720, 2025.

The Korean Peninsula has long been recognized as a stable intraplate region with relatively low seismic activity. However, moderate-to-large earthquakes, such as the 2016 Gyeongju earthquake and the 2017 Pohang earthquake, have highlighted the necessity for systematic studies on the seismic characteristics of the region. This study aims to understand the seismic characteristics of the Korean Peninsula by estimating key seismic source parameters (seismic moment, moment magnitude, stress drop, and corner frequency) for major earthquakes. 
Using the Brune source model (Brune, 1970) as a basis, spectral analysis was conducted to determine the source parameters of the 2016 Gyeongju earthquake, and the results were validated through comparisons with previous studies. After verifying the results, the established framework was applied to automatically determine the seismic source parameters for earthquakes of magnitude 3.0 or greater that occurred in the Korean Peninsula after 2010. Regional comparisons of stress drop values were also conducted based on the determined results.
The framework developed in this study aims to automatically determine seismic source parameters for future earthquakes of magnitude 3.0 or greater in the Korean Peninsula. This advancement is expected to enhance the quantitative understanding of seismic activity in the region and provide a crucial foundation for future studies on seismic activity and region-specific seismic hazard assessments.

How to cite: Byun, A.-H., Jo, E., Min, K., and Park, S.-C.: Study on the determination of seismic source parameters in the Korean Peninsula, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13752, https://doi.org/10.5194/egusphere-egu25-13752, 2025.

Resilience during and after large earthquakes depends heavily on the rapid characterisation of the events and their impacts. To achieve the best response possible, it is useful to estimate the dynamic rupture characteristics. The duration of rupture can help us assess tsunamigenic potential, by detecting the special class of “tsunami earthquakes” [Newman & Okal 1998], characteristically long-rupturing events that generate larger tsunamis than expected for their magnitude [Kanamori 1972]. Energy partitioning between high and low frequencies can guide analysis of potential damage to the built environment.

To better understand the links between the dynamic energy parameters and seismic impacts of earthquakes in the southwest Pacific, I have compiled a catalogue of historic and recent events, to use as a baseline for future near-real-time estimates and bring some more insight into seismic activities in this part of the Pacific.

We apply proven algorithms [Newman & Okal 1998, Newman & Convers 2013, Boatwright et al 2002], that rely on different type of waves and distance ranges, as well as the more recent updates to such estimations [Ebeling & Okal 2012, Saloor & Okal 2018], to select the approaches and input assumptions most fitting for this region. However, the remote location with sparse instrumental data brings limitations in gaining reliable estimations of radiated energy.

The technique from Newman & Okal [1998], relying on teleseismic P-waves, performs reliable estimates of Mw5.5+ events but needs some adjustment to reduce uncertainties for intermediate magnitude earthquakes in the region, due to large azimuthal gaps. Following a later approach from the author [Convers & Newman 2011], I design a network of consistent and reliable stations within the distance range and correct them for permanent deviations. Lastly, I reproduce the scheme from Ebeling and Okal [2012] in evaluating an empirical correction of the scaled radiated energy for stations closer than 35° epicentral distance, but on a regional scale instead. This approach makes use of stations within regional distances of the epicentre for faster results.

Our application of the S-wave approach of Boatwright et al. [2002], relies heavily on the densely sampled New Zealand broad-band seismic network. I use the existing velocity and attenuation models for New Zealand [Eberhart-Phillips et al. 2020] to test both 1D and 3D attenuation corrections, in terms of reliability of the results and computing time. We also refine our estimations of higher frequencies to better evaluate their contribution to the energy estimates in various subduction zone settings.

In this talk, I will present the current state of the radiated energy catalogue for the South-West Pacific and the automated energy analysis under the New Zealand RCET (Rapid Characterisation of Earthquakes and Tsunamis) program. The teleseismic estimates are in good agreement with previous evaluations for large earthquakes. The richer dataset largely correlates with what we know about regional tectonics and highlights some variations that could indicate large transitions in subduction-zone stress fields.

How to cite: Chanony, S., Fry, B., Gorman, A., and Stirling, M.: Estimating radiated seismic energy for New Zealand and the South-West pacific, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13981, https://doi.org/10.5194/egusphere-egu25-13981, 2025.

The distribution of focal depths can enhance the understanding of seismogenic characteristics in a region. However, determining accurate focal depths is highly challenging, and the reliability of such determination is often limited, requiring careful consideration during analysis and interpretation. This study investigates the depth distribution of earthquakes in the southern Korean Peninsula to better understand regional seismicity. To minimize focal depth errors, the criteria for reliable focal depth determination were examined. For the local crustal velocity model of Kim et al. (2011), P- and S-wave travel times were computed under conditions of arbitrary seismic networks with a 150 km aperture, consisting of 1,680 seismic stations and originating from focal depths of 5 to 25 km. From these conditions, 10 seismic stations were randomly selected to determine the hypocenter of an event, which was then compared with the original focal depth. This process was repeated 100,000 times for each focal depth without noise and an additional 100,000 times with random noise ranging from -2 to 2 seconds added to the travel times. Consequently, a total of 4.2 million sets of arrival times were generated. To account for epistemic uncertainty in the crustal structure, three local velocity models, including that of Kim et al. (2011), were used for earthquake location. Various metrics were evaluated to develop selection criteria for ground truth events with well-located hypocenters. The Gradient Boosting method identified the minimum distance to a station as the most important metric. In this study, a new metric was introduced by gridding the epicentral distance within 100 km and the azimuth while considering the distribution of seismic stations within each grid. Using this metric, along with considerations of epicentral distance and azimuthal gaps, criteria were established to ensure the reliability of input data for accurately determining focal depths. Seismic events from 2018 to 2022 were located after meticulously inspecting seismic phases and selected based on the criteria proposed in this study to analyze the focal depth distribution in the southern Korean Peninsula.

How to cite: Sheen, D.-H.: Analyzing Earthquake Focal Depth Distribution in the Southern Korean Peninsula, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14240, https://doi.org/10.5194/egusphere-egu25-14240, 2025.

Human activity induces ground motions that dominate >1 Hz seismic data recorded in semi-rural and urban areas. Whereas the seismic noise generated by industrial and traffic activities is relatively well characterized, weaker pedestrians-related noise remains less well understood. Here, I examine the spatiotemporal distribution of seismic amplitudes to uncover the effects of human crowds and derive semi-empirical attenuation relationships between seismic amplitudes and crowd sizes. I utilize recordings from the dense MesoNet seismic network, which consists of about 300 accelerometers, primarily located near schools in the Tokyo metropolitan area. These data exhibit strong temporal variations: maximum daytime amplitudes are recorded during school hours, and minimum amplitudes coincide with daily breaks in activity during lunch and afternoon teatime. Outside school hours, I observe a wide-spread amplitude peak at 3 to 5 am daily, likely due to truck traffic. The peak amplitudes correlate very well with the number of students in each school. Given that ambient traffic volumes differ substantially in the area covered by the MesoNet stations, this correlation suggests the amplitudes are primarily influenced by daytime pedestrian school activity. I supplement the MesoNet dataset with seismograms of well-recorded outdoor cultural activities with participant sizes ranging from 200 to about 100,000. After correcting for attenuation due to surface-wave geometrical spreading, the amplitudes are found to scale logarithmically with the crowd’s size. This finding indicates that seismic data can be effectively used to study trends in pedestrian mobility within urban environments.

How to cite: Inbal, A.: The People’s Magnitude: Characterizing Seismic Motions Generated by Human Crowds, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14902, https://doi.org/10.5194/egusphere-egu25-14902, 2025.

The earthquake that happened the 17^th of December 2024, located 30km West of Port Vila, the Vanuatu capital, is characterized by an unusual focal mechanism: a nearly-vertical fault with slip predominantly along-dip, and with strike perpendicular to the Vanuatu subduction. In addition to numerous aftershocks, the local seismic networks of Vanuatu and New Caledonia recorded seismic events preceding the mainshock. In this study, we relocate the main event using the local networks. This allows us to better constrain its depth with respect to the available global catalogs, and to determine if it happened in the slab or in the upper plate. A relative relocation of the whole seismic sequence (preshocks and aftershocks) will also allow us to better image the geometry of the fault that activated during this event. A detailed analysis of the preshock activity will give us hints on the mechanisms that led to this large unconventional earthquake.

How to cite: Tari, D., Aru, M., Laga, J., Durand, V., Kobayashi, K., Niroa, J., and Antfalo, L.: Detailed study of the seismic activity preceding and following the 2024 M7.3 Vanuatu earthquake, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16072, https://doi.org/10.5194/egusphere-egu25-16072, 2025.

Traditional research grade 3-component seismic sensors by their very design are sensitive to both translational ground movement as well as rotational (or tilt) motion. This is most prevalent in the horizontal components of sensors which are most sensitive to tilt of the ground. The outputs of traditional seismometers represent a sum of rotation and displacement information. Most applications processing the data make the assumption that the outputs are proportional to purely displacement although this is not strictly the case in commercial devices.

New technologies are now allowing for accurate and precise discrimination between the two components which make up the vast majority of seismic records.

Stratis is the world’s first integrated seismic sensor offering simultaneous output of both rotational and displacement data in all 3 axis. Stratis offers six concurrent outputs providing Z, N and E ground displacement channels proportional to velocity (Metres/second) and rotation channels in the Z, N and E planes proportional to velocity in rotation (Radians/Second). The provision of the measurement of the six degree of freedom now permits derivation of the Elasticity Tensor from a single sensor.

The Stratis displacement output removes these rotation effects and gives a ‘pure’ displacement measurement. This is unique in the seismic sensor marketplace, providing true displacement data that is uncontaminated by rotational signals. This will therefore allow for higher fidelity seismic measurements, improving our analysis and understanding of earthquake processes.

These six parameters are measured at a single point in the geometric centre of the sensor. Use of multiple separated sensors to derive rotation can only approximate true rotation at the same point as displacement. By integrating these measurements into a single instrument, the installation process is also greatly simplified thereby enabling wider access to rotational seismic data. Naturally, the separation of rotational information from the displacement outputs also gives a pure displacement sensor – something unique for the seismological community.

How to cite: Watkiss, N., Hill, P., Calver, J., Mohr, S., Kerkenyakova, A., Restelli, F., and Lindsey, J.: Guralp Stratis - a Commercial Six Degree of Freedom Seismometer for Academic and Research Applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16935, https://doi.org/10.5194/egusphere-egu25-16935, 2025.

The 17^th of December 2024, a deadly M7.3 earthquake struck Port Vila, the capital of Vanuatu. This earthquake, located 30 km West of Port Vila, was strongly felt and damaged heavily the city economical center. This earthquake shows an unusual focal mechanism: a nearly-vertical fault with slip predominantly along-dip, and with strike perpendicular to the Vanuatu subduction. Comparing this earthquake with regional focal mechanisms for 30 years shows the uniqueness of this event. A local seismic network recorded a precursory seismic sequence (we detail this in another presentation, EGU25-16072). In this study we analyze the position time series recorded by a continuous GNSS station located in Port Vila, putting it in relation with the local and regional seismotectonic context, with a focus on the weeks preceding the mainshock and on the coseismic displacement. Finally, using Coulomb stress modeling and analyzing the spatio-temporal evolution of the seismicity in the neighboring subduction, we examine the potential impact of this earthquake on the subduction behavior.

How to cite: Durand, V., Tari, D., Aru, M., Laga, J., Gualandi, A., Patriat, M., Niroa, J., and Antfalo, L.: The 2024 M7.3 Vanuatu earthquake: an overview, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16939, https://doi.org/10.5194/egusphere-egu25-16939, 2025.

Seismic anisotropy -- the directional dependence of seismic wave speeds -- provides a unique view into the past and present deformation of Earth's interior. However, constraining Earth's anisotropic heterogeneity remains a challenge primarily due to imperfect data coverage combined with the increased number of free parameters required to describe elastic anisotropy. And yet, exploring this more complex model space is critical for the interpretation of seismic velocity anomalies which may be significantly distorted if anisotropy is neglected. In this presentation, I will review new imaging strategies, developed by myself and colleagues, for constraining 3D anisotropic structures and their application to studying subduction zone dynamics and volcanic processes. Key developments include moving beyond simplified assumptions regarding the orientation of anisotropic fabrics (i.e. from azimuthal and radial parameterisations to tilted-transversely isotropic models), the integration of multiple and complementary seismic observables (P and S body wave arrivals, shear wave splitting measurements, and surface wave constraints), and the use of probabilistic inversion algorithms that allow for rigorous exploration of model uncertainty and parameter trade-offs. I will discuss how applying these imaging approaches to subduction systems in the central Mediterranean and Western USA yields new insights into the geometry of mantle flow, the nature of seismic velocity heterogeneity, and trade-offs between isotropic and anisotropic features. At smaller scales, I will highlight how new anisotropic tomography reveals the structure of the magmatic plumbing system beneath Mt. Etna (Italy) and provides constraints on the geologic processes controlling crustal stresses.

How to cite: VanderBeek, B.: Improved Strategies for Seismically Imaging Earth's Anisotropic Interior with Applications to Subduction Zones and Volcanic Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17444, https://doi.org/10.5194/egusphere-egu25-17444, 2025.

This work summarizes the procedure for constructing a dataset of accelerometric and velocimetric recordings of earthquakes in the subduction zones of the Mediterranean Basin. The aim is to improve the ground motion characterization of this type of events by calibrating a brand-new predictive model (GMMs) for the next generation of seismic hazard models in Europe. 

The database consists of three components ground-motion recordings from selected earthquakes in subduction zones, collected and processed uniformly through the ESM (Engineering Strong Motion database, https://esm-db.eu/#/home; (Luzi, et al., 2020)) infrastructure. The dataset also includes several supporting data (metadata) relative to source (magnitude estimates and focal mechanisms), path (different distance metrics), and site (VS,30 and soil classification according to the Eurocode 8). The earthquakes are located in the Calabrian Arc, Cyprus Arc, and Hellenic Arc, whose geometries are defined based on studies carried out in the framework of the European Fault-Source Model 2020 (Basili, et al., 2022). The preliminary dataset consists of 9910 records of 475 events with a magnitude range between 2.9 to 6.6. The events that occurred in the subduction zones are classified into interface and intra-slab events, according to different strategies, since their position relative to the slab can induce different near-source. A flag to identify stations in backarc or fore-arc position is also introduced since different rates, propagation characteristics, and characteristics of ground motion attenuation characterize them. The ground motion parameters will also be compared with available datasets for other worldwide subduction zones, such as the NGA-Sub database (Mazzoni, et al., 2021). The dataset will be finally disseminated through a flatfile (parametric table), formatted according to the ESM flatfile specification (Lanzano, et al., 2019).

How to cite: Shoaib, B., Lanzano, G., and Luzi, L.: Construction of a ground motion flatfile for subduction earthquakes in the Mediterranean area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19380, https://doi.org/10.5194/egusphere-egu25-19380, 2025.

Probabilistic seismic hazard assessment (PSHA) requires the definition of seismic source models (SSM) that describe the spatial variations of the seismic activity over the region of interest. They can include and combine active faults, seismotectonic area source polygons as well as zoneless, continuous descriptions of the seismicity. In this contribution, we present the different components of the source model we developed to update the probabilistic seismic hazard model for continental France that was published by Drouet et al. (2020).

Particular effort was dedicated to produce a new historical and instrumental catalogue, homogenized in moment magnitude, with location and magnitude uncertainty estimates. The three area source models used in the previous study were also revised in order to extend them up to 300 km away from the political borders so as to allow seismic hazard calculations at longer return periods. The recently published SSM of the ESHM20 was used to that end. We also explored new methodologies to build zoneless models with less arbitrary choices. Whenever possible, we involved Bayesian methodologies for the estimation of model parameters. Finally, a new feature for the updated seismic hazard map calculations is the introduction of active faults in the source model.

We present preliminary depth distributions, style of faulting, maximum magnitude and frequency magnitude estimates for the various elements of this source model. Location, but more importantly magnitude uncertainties are carefully taken into account and propagated at each stage to properly honor epistemic uncertainties in the subsequent seismic hazard calculations.

How to cite: Arroucau, P., Mazet-Roux, G., Daniel, G., Lefèvre, M., Bollinger, L., and Le Roux-Mallouf, R.: A seismic source model for continental France probabilistic seismic hazard assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20447, https://doi.org/10.5194/egusphere-egu25-20447, 2025.

Earthquakes are one of the most potent geological hazards. They may cause vital destruction, including casualties and property damage. Fluid plays an important role in the seismic cycle, along with tectonic deformations. Reservoir induced seismicity (RIS) linked to impoundment of artificial reservoirs and their water level changes, is usually characterized by higher magnitudes, compared to other types of natural and anthropogenic fluid-induced seismicity. However, there is still no comprehensive understanding of the RIS mechanisms despite previous high-resolution in-situ water level and seismic monitoring and their statistical analysis. This study suggests a fully coupled poroelastic model for fully dynamic RIS sequences simulations in a faulted reservoir. The model is thoroughly verified (e.g., on quasi-static Terzaghi and dynamic compressive poroelastic seismic wave propagation, and on other problems). Seismic sequence patterns simulated using a rate-dependent frictional contact under extension and with adaptive time stepping demonstrate proper characteristics applicable to tectonic earthquakes. These verifications and benchmarking demonstrate a convincing agreement with analytical predictions. The fluid flow within the rock and over the fault is modeled as well, being enhanced after the activation of a reservoir impoundment. The model allows further investigation of the RIS spatio-temporal characteristics and triggers. It also may allow for improving earthquake prediction and mitigation policy, especially in areas with substantial water level fluctuations.

How to cite: Zhou, X. and Katsman, R.: Fully Dynamic Model for Reservoir Induced Seismicity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-287, https://doi.org/10.5194/egusphere-egu25-287, 2025.

Moment magnitude (M_w) is widely accepted magnitude scale as a direct physical measure of the long-period seismic energy released at the foci and thus its reliable quantification is of great importance for accurate seismic hazard assessment studies (Onur et al., 2020). Yet, a robust estimation of M_w and radiated energy (e.g., apparent stress) over a wide range of magnitudes is difficult, mainly due to the existing strong lateral heterogeneous nature of the crust in various tectonic regimes. Additionally, the extrapolation or linking of short-period magnitudes like M_L to M_w can often lead to significant bias (e.g., Shelly et al., 2022). To address this issue, we employ a coda envelope-based source spectral method, which uses a regional empirical calibration approach by lowering the threshold for reliable M_w and radiated energy estimation. In order to achieve this objective, in this study we analyzed three-component digital waveform recordings of 51 moderate local and regional earthquakes (M_L ≥ 4.0) that occurred from 2013 to 2022 in and around the central North Anatolian Fault Zone (NAFZ), including the 18 April 2024 M_w 5.7 Sulusaray (Tokat) earthquake and the 23 November 2022 M_w 6.1 Gölyaka (Düzce) earthquake, both with two notable aftershocks. Data with 100 Hz sampling rate were collected from 51 broadband stations operated by the Kandilli Observatory and Earthquake Research Institute (KOERI) and the Disaster and Emergency Management Presidency (AFAD). Using the Java-based Coda Calibration Tool (CCT), which applies the empirical approach developed by Mayeda et al. (2003) and efficiently processes seismic coda envelopes (Barno, 2017) for further calibration, we successfully implemented the coda-derived source spectrum method to calculate apparent stress and moment magnitude (M_w) across different regions. Following the calibration with reference events (apparent stress and moment-tensor M_w’s), we plan to extend reliable magnitude estimation to smaller earthquakes (M_L < 4.0), confirming robustness in these predictions. Our results provide a more thorough catalog of seismic events in the NAFZ, thereby contributing to improvement of regional seismic hazard assessments. This approach may also serve as a framework for reliable small-to-moderate earthquake analysis in other tectonically active regions, thus supporting broader seismic risk management efforts.

How to cite: Tekiroğlu, G., Kaya Eken, T., Mayeda, K., Roman-Nieves, J., and Eken, T.: Source scaling of earthquakes in and around the North Anatolian Fault Zone based on coda-derived source spectra: Toward a more accurate and unbiased Mw catalog, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-540, https://doi.org/10.5194/egusphere-egu25-540, 2025.

The Korean Peninsula, generally tectonically stable, has experienced occasional seismic activity, with 19 earthquakes of magnitude Mw ≥ 4.0 since 2013. The largest inland earthquake, a magnitude ML 5.8 event, struck Gyeongju in 2016, causing significant damage and casualties. It was preceded by a foreshock and followed by numerous aftershocks. In 2017, an ML 5.4 earthquake in Pohang, potentially related to enhanced geothermal system exploration, caused major damage. In 2024, a 4.8 ML earthquake occurred near Buan-gun, indicating continued seismic activity in the region. This study simulates the ground motions of the recent inland earthquakes, with a focus on the 2016 Gyeongju and 2017 Pohang earthquakes, along with their associated foreshocks and aftershocks, utilizing existing source and velocity model data and rise time scaling relationships. We investigated whether we can consistently simulate the recent Korean earthquakes with existing models and data, with an overarching goal of improving earthquake simulation accuracy for future applications in the Korean peninsula. The simulations were performed using the Spectral Element Method via SPECFEM3D, an open-source software for high-accuracy seismic modeling. We found that simulated ground motions were overall consistent with Gyeongju mainshock observations when an existing risetime scaling relationship was assumed. The results also showed some dependence on the assumed risetime scaling relationship for the Gyeongju and Pohang mainshocks, meaning that a region-specific scaling relationship might improve the overall accuracy of the simulation. We also found that the simulations were less dependent on the risetime scaling for earthquakes with magnitudes less than 5. Simulation of the 2017 Pohang mainshock was significantly underpredicting the recorded motions, when the simulation assumptions was consistent with the Gyeongju event. 2017 Pohang earthquake records were showing very pronounced surface waves that the simulation failed to reproduce using the current model. Simulations of smaller earthquakes showed varied levels of consistency. We are currently investigating the causes of the inconsistency in the simulation of recent earthquakes by comparing them with recorded motions, and we hope that we will eventually find a way to consistently reproduce earthquake ground motions for future applications. 

How to cite: Layek, M. K. and Jeong, S.: Ground Motion Simulation of Recent Korean Earthquakes Using the Spectral Element Method, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2031, https://doi.org/10.5194/egusphere-egu25-2031, 2025.

During earthquakes, seismic ruptures propagate (V_r) along faults as mode II-III cracks, approaching the shear wave speed V_s (i.e., sub-Rayleigh or V_r ~ 0.9 V_s), or, in at least 15% cases, at V_r faster than V_s (i.e., supershear or V_r ~ √2V_s ) (Bao et al., 2022). Since ground shaking increases with rupture speed, the transition from sub-Rayleigh to supershear speeds is critical in seismic hazard studies. This transition may occur (1) directly, due to dynamic stress perturbations associated with stress and strength heterogeneities along the fault, presence of fault step-overs and damage zones, etc., or (2) in-directly, where the stress peak ahead of the main crack tip (mother crack) nucleates a secondary crack (daughter crack) once the local fault strength is exceeded (Burridge-Andrews model).

Here we employ a newly-conceived 2-dimensional hybrid Finite Element Method and Peridynamic (FEM/PD-2D) model to simulate crack propagation and investigate the transition from sub-Rayleigh to supershear in dry and fluid-saturated media, where the Finite Element Method is used to simulate fluid flow, while Peridynamics is used to describe solid deformation. The model also incorporates a novel bond failure criterion based on critical rotation deformation (i.e., deflection angle of the Peridynamic bonds before and after shear deformation) for mode II fracture propagation.

First, we validate the FEM/PD-2D model against previous results from (1) numerical simulations with ABAQUS by Yolum et al. (2021), and (2) physical experiments with PMMA by Svetlizky et al. (2015). In case (1), the FEM/PD-2D model accurately reproduces rupture propagation in a dry Homalite plate with a pre-notch subjected to impact shear loading. Supershear rupture is recognized by the emergence of shear Mach waves observed in the particle velocity magnitude contours. In agreement with Yolum et al. (2021), the stable supershear crack velocity lies between √2V_s and V_p (compressional wave speed). In case (2), the model reproduces the shear loading experiments of PMMA blocks with a frictional interface, and yields crack growth curves and supershear propagation consistent with the measurements of Svetlizky et al. (2015).

Subsequently, we apply the FEM/PD-2D model to explore rupture propagation along both dry and fully saturated media under shear loading. Supershear crack speeds and the emergence of shear Mach cones are observed in both the dry and fluid-saturated cases. Supershear rupture can be achieved through either the in-direct (Burridge-Andrews mechanism) or a direct transition. Notably, the presence of the fluid phase enhances the sub-Raileigh to supershear transition due to poroelastic effects at the rupture front. The findings from this model, beyond their implications for earthquake hazard assessment, may also explain the formation mechanisms of hundred-meter-thick rock damage zones adjacent to seismogenic faults.

How to cite: Shu, Y., Shen, Z., Ni, T., Faccenda, M., Galvanetto, U., Di Toro, G., and A. Schrefler, B.: Hybrid FEM-Peridynamic Modelling of Supershear Earthquake Ruptures, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2185, https://doi.org/10.5194/egusphere-egu25-2185, 2025.

The rise in frequency and magnitude of anthropogenic earthquakes has raised public concern and underscored the importance of understanding subsurface processes and mechanisms to assess induced seismic hazards and risks. While faults are ubiquitously rough, and the characteristics of fault roughness are well investigated and constrained by natural observations, the interplay between roughness and successive rupture events in induced seismicity remains poorly understood. Here, we simulate seismicity induced by fluid injection on a self-affine rough fault. The model assumes instantaneous weakening from static to dynamic friction, homogeneous friction coefficients, and instantaneous frictional healing after each earthquake. We investigate how pore pressure diffusion, initial stress state, and fault roughness influence the stress distribution and the seismicity front. We find that fault roughness significantly alters the statistical distribution of distance to failure (critical pressure), transitioning from an approximately normal distribution at low roughness to a highly skewed distribution at high roughness. Furthermore, models with similar initial stress distributions have comparable seismic fronts, highlighting the critical influence of pre-existing stress conditions. With additional simplifications, the seismicity front and back-front can be predicted reasonably well based on the initial stress distribution and the spatio-temporal evolution of pore pressure. This provides a basis for understanding additional factors such as stress interactions and spatial correlation that influence the seismicity front.

How to cite: Lin, H.-F., Candela, T., and Ampuero, J.-P.: Seismicity front induced by fluid injection on rough faults, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2294, https://doi.org/10.5194/egusphere-egu25-2294, 2025.

Stress-drop is the overall reduction of average stress due to energy release during an earthquake, and should reflect geometrical, rheological and dynamic properties of the seismic source. Stress-drop values, estimated using seismological data, vary over four orders of magnitudes making the stress-drop an enigmatic parameter. There have been many efforts to reduce the stress-drop scatter, and to perceive better understanding of the factors controlling its variability. These efforts focused mainly on observational aspects, in which source properties such as, corner-frequency and seismic moment, were measured, considering site, path and additional source properties. Standard cubic power-law relation between corner-frequency of radiated waves and stress-drop, with a constant coefficient K, is and additional reason to its significant scatter. We provide a new formulation, applying a strain-drop dependent K; by that leading to a significant reduction of the relation of stress-drop to corner-frequency, down to a power-law of 3/4. Results based on a wide range of theoretical, laboratory and observational measurements demonstrate that the new formulation significantly narrows the three to four orders of magnitude of scatter, to about one order of magnitude around a value of 10MPa. The more converged range of stress-drop values, obtained by the suggested new formulation, may be used to support those who argue for self-similarity of earthquakes. In summary, the impact of the uncertainties of the source properties, seismic moment, M₀, seismic potency, P₀, and corner frequency, f_C, on the value of stress-drop is not as dramatic as so many studies argued before. Furthermore, as we demonstrate, the reduction of scatter does not eliminate internal trends, controlled by geometrical, rheological and dynamical properties at the source.

How to cite: Kurzon, I., Lyakhovsky, V., and Sagy, A.: New formulation reduces the scatter of earthquake stress drop estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3345, https://doi.org/10.5194/egusphere-egu25-3345, 2025.

    Understanding the complexity of earthquake source parameters, including coseismic slip distribution and rupture dimensions, is essential for local-scale seismic and tsunami hazard assessments. One effective approach is to use earthquake source models generated from synthetic earthquake catalogues via physics-based generators like RSQSim. A key factor influencing the characteristics of a synthetic earthquake catalogue is the tectonic stressing rate, calculated from the slip-deficit rate using a back-slip loading method. The slip-deficit rate can be calculated by integrating the geodetically-inferred convergence rate from Euler Pole rotations with seismic coupling models. Unfortunately, some of the world’s subduction zones have insufficient geodetic data to significantly constrain coupling models. Such is the case with our focus area in the southwest Pacific. To overcome this challenge, we estimate coupling factors on subduction interfaces by adjusting them according to the seismicity rate ratios between the instrumental and synthetic earthquake catalogues of the baseline models. The subduction interfaces are divided into several segments for calculating the seismicity rate ratios along strike. To incorporate sufficient instrumental earthquakes for seismicity rate estimates and to avoid artificial segmentation, we test the segment window lengths and shifting distance. Our new method is applied to the Tonga and Vanuatu subduction zones, which exhibit the highest convergence rates among subduction zones worldwide of approximately 240 mm/year. The coupling factor in this area was poorly defined in previous studies, leading to debate about whether the coupling was weak or strong in each segment. The ideal coupling distribution occurs when adjusted by seismicity rate ratios calculated with a 500 km moving window shifted 50 km along the strike for the Tonga and Vanuatu subduction zones. The results show weak coupling at northern Tonga and strong coupling at northern Vanuatu interfaces. We use this model to develop a synthetic catalogue of finite fault earthquakes spanning ~60,000 years.

How to cite: Liao, Y.-W. M., Fry, B., Williams, C., Howell, A., and Nicol, A.: Earthquake source modelling for hazard assessment of the Tonga and Vanuatu subduction zones , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3979, https://doi.org/10.5194/egusphere-egu25-3979, 2025.

Earthquakes are commonly associated with brittle failure and frictional sliding in the uppermost 70 km of the Earth. Yet, a significant fraction of seismic events are detected at depths of up to 700 km (deep earthquakes). As these events are difficult to reconcile with our understanding of brittle failure, they are likely facilitated by a ductile weakening mechanism instead. Thermal runaway describes the positive feedback loop of shear heating, temperature-dependent viscosity and deformation. This mechanism has been proposed as a driver of deep earthquakes, and several one-dimensional (1D) studies support its viability. However, two-dimensional (2D) models that show the transient propagation of highly localized shear zones due to thermal runaway are still missing.

We present 2D thermomechanical models which employ a composite visco-elastic rheology, combining elasticity with diffusion creep, dislocation creep and low-temperature plasticity. The code is written in the Julia programming language, operates on Graphic Processing Units (GPU) and utilizes the pseudo-transient relaxation method. Our models capture the nucleation of ductile ruptures on small perturbations, and their transient propagation through a previously intact host rock. Slip velocities inside the ductile ruptures are initially on the order of the far-field deformation, but as the rupture self-localizes, velocities quickly increase by several orders of magnitudes and reach the range of earthquakes (> 1 mm s^-1). The ductile ruptures propagate parallel to the simple-shear background deformation without pre-existing faults or weak layers. If multiple perturbations are present, thermal runaway may nucleate in multiple locations and ruptures can bend to connect to each other.

The magnitude of maximum slip velocity strongly depends on the ratio of stored elastic energy to thermal energy when deformation transitions from low-temperature plasticity to diffusion or dislocation creep. This ratio is derived from one-dimensional models but retains its validity in 2D. If it is small (e.g., low stress, high temperature), shear zones are broad, and deformation is slow. For medium values, slip velocities are in the range of aseismic slow slip events (SSEs). For large energy ratios (e.g., high stress, low temperature), slip velocities reach the seismic window.

Such high-stress conditions are most likely to occur in the cold cores of subducting slabs when they approach the bottom of the mantle transition zone. The resistance of the lower mantle causes slabs to deform, and the large overburden pressure increases viscosity. Both effects increase stress levels. This depth also coincides with the highest occurrence rate of deep-focus earthquakes.

How to cite: Spang, A., Thielmann, M., de Montserrat, A., and Duretz, T.: 2D numerical models of ductile rupture propagation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4156, https://doi.org/10.5194/egusphere-egu25-4156, 2025.

The Korean Peninsula, located on the eastern margin of the Eurasian plate, has historically exhibited the low seismicity characteristic of intra-plate regions. However, the 2016 M 5.8 Gyeongju earthquake, the largest instrumentally recorded inland earthquake in the region, challenged the perception of the peninsula as a seismically safe zone. This event underscored the need for comprehensive seismic hazard assessment and mitigation strategies. Understanding ground motion characteristics of future large earthquakes is critical for advancing these efforts. Recently, physics-based broadband ground motion simulations using dynamic rupture models have gained popularity for studying near-source strong ground motion characteristics. In this study, I performed broadband (0.1–10.0 Hz) ground motion simulations of the 2016 Gyeongju earthquake using dynamic rupture modeling with the slip-weakening friction law on high-performance computing platforms. To enhance the heterogeneity of rupture processes and generate high-frequency (> 1 Hz) ground motions, I incorporated heterogeneity in the slip-weakening distance, modeled using the von Karman distribution. The distribution was controlled by three key input parameters: correlation length, Hurst exponent, and standard deviation. Preliminary results indicate that incorporating heterogeneous slip-weakening distances produces higher-frequency ground motions compared to homogeneous models. However, the simulated high-frequency energy remains insufficient to match the observed data fully. This highlights the importance of further refining physics-based broadband ground motion simulation methods to support advanced seismic hazard assessments. Future work will explore a broader parameter space for the heterogeneity of dynamic rupture parameters, including stress drop, strength excess, and slip-weakening distance. Additionally, the developed dynamic rupture models could be used to derive pseudo-dynamic rupture models, leveraging the source statistics of key kinematic parameters. These efforts aim to establish a robust physics-based broadband ground motion simulation platform for improved seismic hazard evaluation.

How to cite: Song, S. G.: Physics-based broadband ground motion simulation of the 2016 M 5.8 Gyeongju, South Korea, earthquake, using slip-weakening distance heterogeneity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5500, https://doi.org/10.5194/egusphere-egu25-5500, 2025.

Physics-based numerical simulations of seismic ground motion are crucial for advancing our understanding of regional earthquake hazard and risk. Complex geometries in sedimentary basins, coupled with strong surface topography, can cause significant variations in ground motion. To model these effects, a 3D representation of surface topography and available 3D models of the sedimentary basin velocity structure are used in numerical simulations. Using the spectral-element method, we synthesise displacement, velocity and acceleration waveforms in the Rhône valley, Switzerland. This region is characterized by complex topography, morphology, and significant seismic hazard.

We perform spectral-element waveform simulations with a maximum resolvable frequency of 5 Hz to investigate the joint effects of 3D basin structure and surface topography on ground shaking. Moderate-magnitude earthquakes that have been recorded in the area are used as point sources. Additionally, we compute waveforms for scenario earthquakes taken from the disaggregation of the current Swiss hazard model, SUIhaz2015 (Wiemer et al., 2016).

Our goal is to assess how these factors affect amplification patterns in different basin parts and topographic areas. We do so by comparing ground motion peak values at different altitudes (on mountains and valley floors with soft sediment conditions). Additionally, we calculate engineering-relevant ground motion parameters, such as cumulative velocity and significant duration up to the resolved frequencies, that help improve hazard estimations in the Rhône valley.

With our study, we show that the joint effects of topography and basin structure lead to larger amplification variations within the basin, and in the surrounding reliefs. We conclude that physics-based simulations have the potential to provide an adequate alternative for input ground motion in seismic hazard analysis. This is particularly relevant for modelling hypothesized earthquakes, which are essential for the assessment of seismic hazard in areas facilitating crucial infrastructure.

How to cite: Koroni, M., Ermert, L. A., Bergamo, P., and Fäh, D.: Physics-based modelling of ground motion in alpine valleys including strong surface topography and 3-D basin structure: A case study of the Rhône valley (CH), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5638, https://doi.org/10.5194/egusphere-egu25-5638, 2025.

Accurate modeling of earthquake ground motions is critical for understanding rupture dynamics and assessing seismic hazard. However, traditional models that employ simple, smooth dynamic rupture representations often struggle to capture ground motion beyond the corner frequency. This limitation stems from their inability to account for the high-frequency content generated by small-scale complexities in the rupture process, leading to underestimation of the spectral content observed in real earthquake recordings. Here we investigate dynamic rupture models incorporating broadband spatial variability in dynamic rupture parameters. Our study implements a multi-scale heterogeneity approach based on the Von Karman autocorrelation function with power spectral density of k^-2 (Hurst exponent of zero) and correlation lengths that scale with the rupture size. We thereby introduce variations in initial stress, fault strength, and characteristic slip weakening distance across the fault plane. The degree of rupture complexity in our simulations is effectively controlled by the standard deviation of the imposed heterogeneities. We demonstrate the effectiveness of our approach by modeling the apparent source spectra of two Mw~4 events from central Italy up to 25 Hz. The first is a unilateral event showing a strong azimuthal dependence of spectral amplitudes due to directivity effects, while the second is a non-directive bilateral event exhibiting a more homogeneous distribution. By comparing synthetic and observed apparent source spectra, we show how our approach successfully models these two contrasting rupture processes. Furthermore, comparison with the theoretical ω^-2 model provides additional insights into the relationship between source complexity and source spectral characteristics. The dynamic rupture heterogeneities prove crucial for reproducing the high-frequency ground motion components that simple models typically fail to capture. This work represents a significant step forward in bridging the gap between earthquake recordings and numerical modeling, providing a robust framework for understanding and predicting ground motions applicable in earthquake scenario simulations.

How to cite: Joshi, L. and Gallovič, F.: Heterogeneity in Dynamic Rupture Models: Bridging the Gap Between Observed High-Frequency Ground Motion and Rupture Process Complexity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5717, https://doi.org/10.5194/egusphere-egu25-5717, 2025.

On February 6, 2023, two catastrophic earthquakes struck the Kahramanmaraş region, marking some of the most destructive seismic events in modern Turkish history. These earthquakes caused extensive loss of life and widespread destruction across southeastern Türkiye and northwestern Syria. The two earthquakes ruptured different fault segments with comparable magnitudes, generating exceptionally strong ground motions. Particularly, in the southern section of the fault rupture along the Amos fault segment, extreme ground motions were recorded. The earthquakes' complex rupture processes, characterized by sequential bilateral ruptures, varying rupture velocities and geometries, across branched fault segments, provide crucial insights into rupture dynamics and seismic hazard. Understanding the spatial variability of ground motions and the directivity effects associated with these complex rupture dynamics is essential.

This study focuses on analyzing the spatial variability of ground motions generated by the Mw 7.8 mainshock. By simulating ground motions on a regular grid across the affected region, we investigate the spatial distribution of ground motion intensity measurements and the rupture directivity effects. Using advanced kinematic rupture modeling techniques, simulations were performed with the Graves and Pitarka (2016) hybrid-source method, combined with Frequency-Wavenumber (FK) 1D Green’s functions computed for a regional velocity model. High-slip patches and stochastic small-scale slip variations were incorporated to create hybrid rupture models.

The simulations, validated against strong-motion data, effectively reproduced near-fault ground motions within the 0–3 Hz frequency range, allowing for near-fault ground variability analysis. This study provides valuable insights into the spatial variability of ground motion amplification patterns and their relationship with rupture directivity, enhancing our understanding of earthquake ground motion variability and seismic hazard.

How to cite: Artale Harris, P., Pitarka, A., Akinci, A., Tsuda, K., and Graves, R. W.: Ground Motion Variability During the February 6, 2023 M7.8 Kahramanmaraş Earthquake, Türkiye, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6323, https://doi.org/10.5194/egusphere-egu25-6323, 2025.

The 2016 Mw 6.2 left-lateral strike-slip Tottori earthquake occurred in the central part of the Tottori Prefecture in the Chugoku region in western Japan. Published rupture models inferred either from geodetic or seismic data exhibit significant discrepancies. In this study, we perform a so-far missing dynamic rupture simulation to improve the understanding of the event. We utilize 3D finite-difference staggered grid code FD3D_TSN to simulate the dynamic rupture propagation assuming the classic linear slip-weakening friction law on a planar vertical fault. Synthetic seismograms are calculated using the representation theorem by convolving the obtained slip rates with Green's functions precalculated in 1D velocity models acquired for each station from a 3D model.

We utilize the fd3d_tsn_pt code to perform a dynamic source inversion with spatially variable prestress and friction parameters, formulated in a Bayesian framework, employing the Parallel Tempering MCMC approach to sample the posterior distribution of the model parameters. We use local low-frequency seismic waveforms (up to 0.6-1.2 Hz) and GNSS static coseismic displacements. We obtain posterior model samples with complex rupture propagation, discuss the inferred heterogeneous rupture parameters in a statistical sense, and compare them with previously published models. We evaluate correlations and trade-offs among kinematic and dynamic rupture parameters. We find that the slip-weakening distance increases in average linearly with distance from the hypocenter, which can be interpreted as an apparent feature substituting the effect of (umodeled) significant off-fault yielding. We also try to include the slip-strengthening (SS) area at shallow depths and find that it is an unnecessary feature for this event.

How to cite: Hronek, M. and Gallovič, F.: Inverse Physics-based Modeling of the 2016 Mw 6.2 Tottori Earthquake, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6559, https://doi.org/10.5194/egusphere-egu25-6559, 2025.

Large subduction earthquakes with magnitudes (Mw) greater than 8.0 are devastating events. Such large earthquakes remain poorly recorded due to their infrequent occurrence. This lack of observational data limits our ability to study rupture dynamics and accurately predict future broadband ground motion. To address these limitations, physics-based modeling has emerged as a powerful approach for understanding the rupture dynamics of large subduction earthquakes and associated ground motions. In this study, we analyze kinematic rupture processes and their influence on synthetic seismogram simulations. We revisit the rupture characteristics and ground motion variability for a large megathrust earthquake in Central Chile, the Illapel Mw 8.3 (2015). We use the data derived from the Bayesian inversion framework presented by Caballero et al. (2023) as input for the forward modeling of ground motion. To capture the finite source effects of a heterogeneous slip distribution, we discretize each sub-fault into point sources limited by a separation dependent on the maximum frequency resolution. With this in mind, we interpolate the seismic moment and define the rupture propagation across the rupture plane. We implement two different codes to compute the resulting ground motions: Axitra (Cotton & Coutant, 1997) and Pyrocko-GF (Heimann et al., 2019). Both codes employ distinct methods for Green’s functions computation and source representation, allowing a comparative analysis of their capabilities in reproducing strong motion. Pyrocko-GF efficiently handles low-frequency simulations with pre-computed Green’s functions, while Axitra provides broadband synthetic seismograms up to 20 Hz. However, with Pyrocko-GF it is also possible to reach high frequencies by adding Green´s functions to its FOMOSTO program. The synthetic seismograms were compared against strong-motion data, focusing on stations at a maximum of 5° of the hypocenter. Key parameters such as peak ground displacement, waveform similarity, and spectral content were analyzed. Additionally, we evaluated the impact of different source time functions on predictions. Our results provide insights into the importance of incorporating heterogeneous rupture scenarios for large earthquakes, as well as the challenges of modeling high-frequency ground motions using different numerical approaches. It is foreseen that our methodology and results will be used in a full physics-based seismic-tsunami hazard assessment for Central Chile.

How to cite: Buenrostro, A. M., Cotton, F., Jara, J., Crempien, J. G. F., and Jünemann, R.: Synthetic Seismograms from Physics-based Modeling of Heterogeneous Rupture for Large Subduction Earthquakes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6860, https://doi.org/10.5194/egusphere-egu25-6860, 2025.

Recent improvements of large-scale ground motion simulations resulting from physics-based rupture and wave propagation models and accessible high-performance computing have made possible the potential use of synthetic ground motion in engineering applications. They provide scientists and engineers with new data that can yield new insight on the characteristics of ground motions and the variability of the infrastructure response. Such simulations can also make a significant contribution to the reduction of uncertainty in ground motion models (GMMs) that are being developed for large earthquakes at near-fault distances where ground motion variability is not fully captured by current sparsely recorded data.

An important aspect of the development and calibration of regional physics-based simulation platforms is the validation of their methodology and synthetic ground motion. Independent criteria that involve direct comparisons with recorded earthquakes, comparisons with exiting region-specific ground motion models for scenario earthquakes, and comparison with recorded buildings response should be requirements for trusted validation analysis.

In an effort to build confidence in simulated ground motion we compiled published results of validation analysis performed by several modeling teams and analyzed the general performance of their physics-based ground motion simulations. We focused on broad-band simulations that use a deterministic approach in computing ground motion time histories for crustal earthquakes. The analysis includes simulation results of selected recorded and scenario earthquakes with the magnitude ranging from 5.4 to 7.5.   The goals of our study are to demonstrate that current physics-based kinematic rupture models can produce ground motions that agree with observed ones and empirical estimates, and that well-constrained reginal velocity models are capable of producing the expected wave scattering affecting ground motion variability and amplitude at local and regional distances. Satisfying these goals provides confidence in the predictive capabilities of the simulation platforms and the quality of synthetic ground motion in various engineering applications, including development of non-ergodic GMMs and building response analysis.

How to cite: Akinci, A. and Pitarka, A.: Performance of Physics-based Deterministic Ground Motion Simulations: Building Confidence in Using Broad-Band Synthetic Ground Motion in Engineering Applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7343, https://doi.org/10.5194/egusphere-egu25-7343, 2025.

Simulating seismic wave propagation in 3D is a complex task, largely due to challenges in accurately representing the on-fault stress state prior to an earthquake. A common approach involves assigning initial fault stress by determining its normal and tangential components based on regional stress conditions. However, for faults with complex geometries (e.g., curved surfaces or overlapping sub-faults) and inhomogeneous material properties, this method often struggles to establish a stress state that aligns with fault geometry, residual stress concentrations, far-field loading, and material heterogeneity.

This study focuses on prescribing initial fault stress based on far-field deformation and the fault’s governing frictional behavior. This enables the assignment of a consistent stress state for faults with complex configurations and non-linear, heterogeneous material or frictional properties. Fault rupture is modeled as a dynamically propagating shear crack along a pre-existing fault plane according to rate-and-state friction. We use PDS-FEM due to its computational efficiency in modeling discontinuities.

To address the computational demands of large-scale numerical models, we propose an MPI+MPI hybrid approach, where MPI shared memory windows are efficiently managed using C11/C++11 atomic operations. Standard MPI RMA synchronization functions, such as MPI_Win_sync(), MPI_Win_fence(), etc.,  are designed conservatively, which can limit compiler optimizations and hinder out-of-order execution by hardware schedulers. By replacing these synchronization functions with C11/C++11 atomic operations and the associated multi-thread memory model, we achieve efficient management of MPI-3 shared memory windows. Performance tests demonstrate that this approach equals, and in some cases surpasses, more conventional methods, particularly for classical applications like ghost updates.

The fault rupture model was validated by reproducing supershear rupture in a 2D fault, illustrating the Burridge-Andrews mechanism. Furthermore, we analyzed the sensitivity of rupture behavior to initial stress conditions using the Palu-Koro fault as a case study, observing transitions between sub-Rayleigh and supershear regimes.

References
[1] Quaranta, L., Maddegedara, L., Kato, A., Hori, M., Ichimura, T., Fujita, K., & Nicolin, E. (2024). Large scale simulation of 3D fault rupture subjected to far‐field loading with PDS‐FEM: Application to the 2018 Palu Earthquake. Journal of Geophysical Research: Solid Earth, 129(9), e2024JB028783.

[2] Quaranta, L., & Maddegedara, L. (2021). A novel MPI+ MPI hybrid approach combining MPI-3 shared memory windows and C11/C++ 11 memory model. Journal of Parallel and Distributed Computing, 157, 125-144.

How to cite: Nicolin, E., Maddegedara, L., Quaranta, L., Fujita, K., Ichimura, T., and Hori, M.: Fault rupture simulation via physics-based prescription of consistent fault stress according to far-field loading conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7927, https://doi.org/10.5194/egusphere-egu25-7927, 2025.

The challenge of modeling site effects in complex geological environments remains a central topic in engineering seismology and is the focus of this session.

Addressing this issue begins with identifying "complex" sites where simple prediction models, based on 1D velocity profiles, fail to provide satisfactory results. This requires comparing actual site effects with predictions from physical models across large datasets. Recent advances now enable such analyses, thanks to the quantification of site effects through generalized inversion methods or spectral ratio calculations between deep and surface stations in regions equipped with borehole networks. Systematic tests using extensive data from Japan’s Kik-net and K-NET networks reveal that a significant proportion of sites deviate from 1D behavior, particularly at frequencies above 3 Hz.

To meet this challenge, we propose three complementary approaches to improve site effect predictions for complex environments:
- Enhanced High-Frequency Physical Modeling: Improving and calibrating attenuation models is essential and feasible, paving the way for more accurate high-frequency predictions.
- Increased Observation Density: Expanding observational coverage in urban areas through innovative methods, such as leveraging smartphone data, can significantly enhance datasets and support the development of high-resolution amplification maps.
- Machine Learning Applications: Developing machine learning models tailored to available site information—ranging from geological and geotechnical data to recorded seismic data—offers a flexible, novel, and testable framework for site effect prediction.

This presentation will discuss the methodologies and results of recent studies, highlighting how these strategies can advance our understanding and modeling of site effects in complex geological settings.

How to cite: Cotton, F., Bossu, R., Finazzi, F., Pilz, M., and Zhu, C.: Predicting Site Effects on "Complex" Geological Sites in the Era of Big Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8664, https://doi.org/10.5194/egusphere-egu25-8664, 2025.

Earthquakes pose a significant threat worldwide, requiring rapid and accurate damage assessments to guide emergency response efforts. While empirical ground motion models are commonly used for their speed and simplicity, they often fail to account for critical factors such as site effects and the spatial variability of ground shaking. Physics-based ground shaking simulations can provide a more accurate alternative by modelling fault rupture, wave propagation and local site effects. However, their application in the near real-time has been limited due to computational complexity and long processing times. To overcome these limitations, we have developed UrgentShake, an HPC-based system designed to generate physics-based ground shaking scenarios under strict time constraints. A key focus of UrgentShake is the implementation of efficient strategies to reduce the computational time required to produce reliable solutions without compromising accuracy. Preliminary evaluations on two reference seismic events demonstrate UrgentShake's capability to significantly reduce time-to-solution, ensuring its potential to meet the critical timing demands of seismic emergency responses.

How to cite: Zuccolo, E., Bolzon, G., Pitari, F., Rodríguez Muñoz, L., Monsalvo Franco, I. E., Scaini, C., Poggi, V., Smerzini, C., and Salon, S.: UrgentShake: An HPC System for Near Real-Time Physics-Based Ground Shaking Simulations to Support Emergency Response Efforts , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9353, https://doi.org/10.5194/egusphere-egu25-9353, 2025.

The Campi Flegrei caldera, with its complex volcanic structure, represents an intriguing challenge for understanding seismic wave propagation. This study presents forward simulations of seismic wave propagation in the caldera area performed by using the spectral element method software SPECFEM3D (Peter et al., 2011). Moment tensor solutions for three seismic events that occurred between May and June 2024, including the Md 4.4 Solfatara earthquake, were considered. Simulations were performed using five different wavespeed models: (1) a 1D model without topography; (2) the same model incorporating the regional topography; (3) the local tomographic model from Giacomuzzi et al. (2024); (4) a 3D model including local attenuation from Calò & Tramelli (2018); and (5) the Italian 3D tomographic model Im25 by Magnoni et al. (2022).

Our approach aims to compare these models for the same seismic sources, in order to highlight the key factors influencing waveform fit between observed and synthetic data. Implementing high-resolution surface topography —characterized by volcanic structures, depressions, and abrupt variations—is crucial to improve waveform fit and reproduce seismogram behavior. Moreover, the results highlight the importance of adopting tomographic models with tailored attenuation and 3D velocity structures that effectively capture the lateral heterogeneities of such a complex area. This is especially crucial when modeling the Campi Flegrei caldera characterized by solidified intrusions and partially melted regions in order to achieve more accurate regional predictions.

Given the coastal setting of the considered area, we also investigate whether the presence of a water layer (i.e., acoustic elements), absent in current simulations, might influence the quality of the fit between observed and synthetic data. To this aim, we explore a simplified scenario that would be representative of the studied region.

How to cite: Fabrizi, F., Magnoni, F., Casarotti, E., and Tinti, E.: Investigating the role of topography and attenuation in volcanic areas by testing different structural models of the Campi Flegrei , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11229, https://doi.org/10.5194/egusphere-egu25-11229, 2025.

Repeating earthquakes most often occur as frequent small magnitude events, with repeating time increasing with magnitude. The Bárðarbunga caldera however is a rare example where moderate magnitudes (Mw ≥ 5) repeat frequently. During the 6 months caldera collapse event (2014-2015), 77 Mw ≥ 5 earthquakes were recorded and, in the years following the collapse, observations show quasi-periodic Mw ≥ 5 events. Recent seismological field campaigns have provided further constraints on the caldera’s geometry, mapping out a ring fault structure and offering excellent depth constraints. The repetitive nature of the earthquake sequences and good structural controls, make this case especially suitable to investigate using Sequences of Earthquakes and Aseismic Slip (SEAS) models. Furthermore, the periodicity of the ring fault is well captured to the first order by adopting a Spectral Boundary Integral approach. To mimic the observed data, we project relatively relocated seismic events to a mesh representation of the ring fault; we then convert the projected point clusters into frictional parameter maps that define the distribution of the rate weakening and rate strengthening asperities in the simulation. Finally, we quantify the differences between the simulated results and the observed seismic data by comparing for example reoccurrence time, magnitudes and partial rupture characteristics. We use the JAX Python library to achieve well resolved simulations, realistic frictional parameters and domain size. This library provides tools such as Just-In-Time (JIT) and Ahead-Of-Time (AOT) compilation, automatic differentiation and vectorization, all of which significantly speed up runtime. Moreover, JAX provides a flexible framework for running code on different accelerators such as GPUs, drastically reducing code runtime for parallelizable operations such as FFT computations. 

How to cite: Benedetti, G., Heimisson, E. R., Winder, T., and De Vries, Y. A.: Asperity cycle simulation using observed seismicity: Applications to the Bárðarbunga caldera, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13050, https://doi.org/10.5194/egusphere-egu25-13050, 2025.

Small earthquakes are frequently modeled as point sources or as ruptures with a simple circular geometry. While these representations are sufficiently accurate within a specific range of frequencies, the actual rupture processes of these earthquakes are inherently more complex.

Earthquake waveforms represent a convolution of source and propagation effects, requiring their separation to enable independent analysis of each component. To investigate the earthquake source, isolating the source time function is crucial. Kinematic rupture models are commonly constructed using Theoretical Green's Functions (TGFs), which rely on simplified one-dimensional (1-D) velocity models that incorporate anelastic attenuation and wave propagation. However, for small earthquakes, this method requires highly detailed structural models, which are often unavailable.

An alternative approach utilizes deconvolution with the Empirical Green’s Function (EGF), obtained from a smaller, co-located event recorded by the same instruments. In this study, we employed the EGF method to extract the source function for small earthquakes (Mw ~3.5) that occurred in the Alto Tiberina fault area. The Landweber deconvolution technique (Bertero et al., 1998) was applied, with a semi-automated selection of parameters, including the signal window and the maximum duration of the apparent source time function (ASTF), the latter based on the methodology proposed by Meng et al. (2020). When automated selection was not possible, we performed a parametric analysis to map the uncertainty on the final results corresponding to the different choice of possible parameters.

Additionally, we used the fault isochrone back-projection method outlined in Király-Proag et al. (2019) to investigate the kinematic source process of these events.

The findings show that this approach allows resolving finite fault properties and rupture directivity of small earthquakes, along with their related uncertainty.

How to cite: Cuius, A., Satriano, C., Supino, M., Tinti, E., and Chiaraluce, L.: Finite source analysis of small earthquakes using the fault isochrone back-projection method: examples from the Alto Tiberina fault., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13418, https://doi.org/10.5194/egusphere-egu25-13418, 2025.

Numerical simulations of Sequences of Earthquakes and Aseismic Slip (SEAS) have rapidly progressed to address fundamental problems in fault mechanics and provide self-consistent, physics-based frameworks to interpret and predict geophysical observations across spatial and temporal scales. Challenges in SEAS modeling include resolving the multiscale interactions between slow slip, earthquake nucleation, and dynamic rupture; and understanding the physical factors controlling observables such as seismicity and deformation. To advance SEAS simulations with rigor and reproducibility, we pursue community efforts to verify numerical codes in an expanding suite of benchmarks, including problems considering earthquake sequences on 2D and 3D fault models obeying rate-and-state friction with different treatments of inertial effects and fault dip under slow tectonic loading (Erickson et al., 2020; Jiang et al., 2022; Erickson et al., 2023).

Here we present code comparison results from a new set of benchmark problems that focus on aseismic processes and earthquake nucleation, including the influence of (1) changes in effective normal stress and pore fluid pressure due to fluid injection and diffusion and (2) different formulations of fault friction evolution. Benchmark problem BP6-QD-A/S/C is a 2D problem that considers a single aseismic slip transient induced by changes in pore fluid pressure consistent with fluid injection and diffusion in fault models with different treatments of fault friction, including rate-and-state fault models using the aging (-A) and slip (-S) law formulations for frictional state evolution, respectively, as well as a constant friction coefficient (-C). BP7-QD/FD-A/S is a 3D problem with a 2D rate-and-state fault considering a circular velocity-weakening asperity that can produce sequences of repeating earthquakes or alternating seismic and aseismic ruptures, under different considerations of fault friction evolution and inertial effects. Comparisons of problems using the aging versus slip law illustrate how models of seismic and aseismic slip can differ in the timing and amount of slip achieved with different treatments of fault friction, including for individual aseismic slip events induced by the same perturbations in pore fluid pressure for BP6. 

We utilize simulations from different groups to explore how various numerical factors affect the simulated evolution of pore pressure and interaction between aseismic and seismic processes. We achieve excellent quantitative agreement across participating codes that utilize distinct numerical methods, by ensuring sufficiently fine time-stepping, large enough domain size for volumetric methods and consistent treatment of boundary conditions. Through these comparative studies, we seek to determine best practices for improving the accuracy and efficiency of SEAS simulations and develop quantitative metrics for benchmarking modeling results. These community-led exercises will foster the development of more realistic multi-physics SEAS models and their integration with geophysical observations, contributing to an improved understanding of fault dynamics.

How to cite: Lambert, V., Erickson, B., Jiang, J., Romanet, P., and Thakur, P. and the Sequences of Earthquakes and Aseismic Slip (SEAS) Community Code-Verification Initiative: Community Code Verification Exercises for Simulations of Earthquake Sequences and Aseismic Slip (SEAS): Effects from Fluids and Fault Friction Evolution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14426, https://doi.org/10.5194/egusphere-egu25-14426, 2025.

The South Iceland Seismic Zone (SISZ) is characterized by a complex "bookshelf" fault system composed of multiple short, parallel, North-South oriented right-lateral and near-vertical strike-slip faults assisting crustal block rotation to accommodate the overall East-West sinistral transform motion. The tectonic strain release across the entire zone is known to occur with 130–140 years intervals, in sequences of moderate earthquakes, up to Mw7. The most recent earthquakes occurred in 2000 and 2008 and reached magnitudes Mw6.3-6.5. Despite its importance for mitigating regional seismic hazards, the seismic cycle in the SISZ remains poorly understood. Thus, we develop a rate-and-state friction-based sequences of earthquakes and aseismic slip (SEAS) model to investigate the long-term seismic behavior of the SISZ.

We utilize the open-source code TANDEM (https://github.com/TEAR-ERC/tandem), a discontinuous Galerkin volumetric solver, and perform 2D simulations on the supercomputer ELJA, operated by the Icelandic High-Performance Computing Centre. Quasi-dynamic simulations with rate-and-state friction are applied to single planar fault models with antiplane shear motion in a homogeneous, isotropic, linear elastic half-space. We model two separate faults within the “bookshelf fault system,” representing the east-western regions of the transform zone. The primary focus of our 2D models is to reproduce the recurrence pattern of the seismic cycle, including hypocentral depth, fault slip, and approximate magnitudes. To configure reliable simulation parameters, we explore diverse models with varying rate-and-state frictional properties, effective normal stresses, and critical slip distances as well as other crucial factors.

Preliminary results indicate recurrence intervals for SISZ earthquakes ranging from 104 to 130 years across the transform zone's western and eastern sections, which agrees well with the observational data. Incorporating varying seismogenic depths in separate models-12 km in the west and 15 km in the east-improves our hypocentral depth predictions. Our study demonstrates the effectiveness of using seismic cycle simulations empowered by high-performance computing with TANDEM, even with a simplified single fault model, to elucidate the seismic processes of the SISZ. The model adequately captures some key characteristics of the seismic cycle of the SISZ, highlighting its potential to inform future seismic hazard assessments in Iceland within the larger scope of the ChEESE-2P project (https://cheese2.eu/).

How to cite: Cubuk-Sabuncu, Y., Gabriel, A.-A., Halldórsson, B., Oryan, B., Yun, J., and May, D. A.: Towards seismic cycle modeling of the complex “bookshelf” South Iceland Seismic Zone, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17611, https://doi.org/10.5194/egusphere-egu25-17611, 2025.

One of the key challenges of empirical ground-motion models is the ability to capture ground-motion variability, which may stem from different source, path and site effects. This challenge may be addressed using simulated data from physics-based, non-ergodic earthquake simulations. Dynamic rupture models capture the nonlinear interaction of source, path and site effects in a self-consistent way and once integrated with observations, reproduce a variety of geodetic and seismic data well to first order (e.g., Taufiqurrahman et al., 2022; Jia et al., 2023; Gabriel et al., 2023). However, these models may not fully account for the variability in ground motions, particularly in the orientation, periods (Tp), and amplitudes of long-period pulses (Yen et al., 2024). In addition, the physical mechanisms underlying high-frequency radiation remain debated (Graves & Pitarka, 2016; Ben-Zion et al., 2024). 

In this study, we investigate the effects of incorporating both on-fault and structural small-scale heterogeneities within 3D dynamic rupture models of the 2023 Turkey earthquake doublet. Specifically, we focus on how these heterogeneities influence rupture dynamics, together with the spectral content and variability of the modelled ground motions. We analyse the impact of small- and large-scale fractal on-fault roughness, a heterogeneous distribution of fracture energy (Dc) and the dynamic friction coefficient (𝜇d), and initial supershear rupture speed compared to sub-shear earthquake initiation.

Our findings reveal that rupture dynamics are most significantly influenced by the introduction of an initial supershear rupture speed, which results in an expected larger seismic moment along the nucleating Nurdaği-Pazarcik splay fault and an earlier triggering of the East Anatolian Fault (EAF) compared to the other models. Although this leads to a greater overall energy release, the release pattern along the EAF remains fairly consistent across all models, suggesting that the added ingredients primarily act to amplify seismic moment rather than drastically alter rupture dynamics. Additionally, Dc heterogeneities have the most significant influence on long-period pulse properties. In contrast, small-scale roughness and 𝜇d heterogeneities exhibit a damping effect on pulse period (Tp) as they mostly influence high-frequency radiation. However, these modifications fail to translate into significant changes in the overall spectral content across the different models. Notably, despite the added heterogeneities, the pulse orientations remain predominantly fault normal and are only minimally impacted by Dc heterogeneities, supershear rupture speeds, and large-scale roughness. 

This study demonstrates that incorporating a heterogeneous distribution of fracture energy has one of the strongest impacts on both the rupture dynamics and frequency content of 3D dynamic rupture simulations, further contributing to a better understanding of how different dynamic rupture heterogeneities influence ground shaking, a critical step towards comprehensively capturing ground-motion variability and enhancing physics-based seismic hazard assessment.

How to cite: Preca Trapani, R., Gabriel, A.-A., Marchandon, M., Ulrich, T., Wu, B., Yen, M.-H., and Cotton, F.: Do physics-based models improve predicted ground motion variability? Insights from dynamic rupture simulations of the 2023 Turkey Earthquake Sequence , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18476, https://doi.org/10.5194/egusphere-egu25-18476, 2025.

We aim to simulate the wave propagation in the complex near-subsurface in urban environments. Here, we focus on the DESY and physics campus with its large underground accelerator tunnels in Hamburg, Germany. Since these particle accelerators, and other sensitive physics experiments on campus, are sensitive to seismic vibrations, it is important to understand how the seismic wavefield couples into the building structures.

To achieve this, there is a need to develop a high resolution subsurface model of Hamburg region. However, to model the whole seismic wave path in 3D for teleseismic events is computationally expensive. In order to circumvent this issue, the present work explores using RegHym package for earthquake simulation of teleseismic events. This package combines the efficiency of Axisem and flexibility provided by SPECFEM3D to embed the intricacies at regional scale in the model. This allows to simulate wave propagation for a teleseismic event on a regional scale in a computationally less-expensive way. 

We are exploring the package to broaden its usability to model full waveforms of tele-seismic events for Hamburg area, incorporating the complex geological and urban environments. The subsurface model will be refined through consecutive comparisons of the synthetic data with data available from a DAS network implemented at the DESY campus in Hamburg. The real data from DAS will be enriched by incorporating recorded data from seismometers deployed in the vicinity of the DAS network.

The refined model will be a fundamental step for numerically investigating the coupling of incoming seismic waves into urban infrastructures as well as into gravitational wave observatories, which are sensitive to very small seismic disturbances.

How to cite: Hejazi Nooghabi, A., Dhabu, A., and Hadziioannou, C.: Developing a High-Resolution Subsurface Model through Teleseismic Wave Simulation in Hamburg, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18687, https://doi.org/10.5194/egusphere-egu25-18687, 2025.

Seismic arrays are essential for collecting and analyzing seismic data, significantly enhancing our understanding of geophysical processes such as the localization of seismic sources. We introduce the concept of Virtual Seismic Arrays, where the array recordings are predicted from a single reference station, removing the need for continuous deployment of all array stations. This work builds on the research by Klinge et al. (2025), which introduced a Deep Learning approach using encoder-decoder networks to learn and predict transfer properties between two seismic stations. By training the algorithm on data of the Gräfenberg array in the secondary microseism frequency band, we develop models that effectively capture the transfer characteristics between a chosen reference station and each of the other stations within the array. To evaluate how well the models represent the underlying wave propagation, we use beamforming and apply it to both the original data from all stations and the corresponding predictions generated by the models. We assess two scenarios: one where the dominant backazimuths and slownesses are consistent with the training dataset, and another where the models are applied to data from different conditions. Our results show strong agreement between the predicted and original beamforming results, demonstrating the potential of Virtual Seismic Arrays for future application.

How to cite: Klinge, J., Schippkus, S., and Hadziioannou, C.: Enhancing seismic monitoring with Virtual Seismic Arrays: Application of a deep learning framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-453, https://doi.org/10.5194/egusphere-egu25-453, 2025.

Detecting seismic events is essential for monitoring tectonic and volcanic activity, especially in marine environments where noise makes analysis particularly challenging. This study introduces a method that combines Evolutionary Neural Architecture Search (ENAS) with the third generation of the Non-dominated Sorting Genetic Algorithm (NSGA-III) to design and optimize neural networks for seismic event detection using Ocean Bottom Seismometers (OBS) data.

In this work we developed a methodology to analyze heavily noisy data recorded by the TYDE OBS experiment in the southern Tyrrhenian Sea, Italy. In 2000, 14 seismic stations were deployed on the seafloor and around the Aeolian Islands recording data for about 6 months. Stations consisted of wide-band Ocean Bottom Seismometers (OBS) and Hydrophones (OBH).

The preprocessing pipeline includes feature extraction with Discrete Wavelet Transform (DWT) and dimensionality reduction using Principal Component Analysis (PCA), which reduces over 6000 coefficients to just 55 while preserving 95% of the variance. Applied to 90-second overlapping windows, this approach has achieved strong results, with F1 scores exceeding 90% in balanced noisy datasets.

Building on these results, this study explores unsupervised clustering to group similar seismic events and identify possible false positives through anomaly detection. By using adaptive clustering methods that determine the optimal number of clusters based on the data, this approach aims to enhance reliability while providing additional insights into the detected seismic events.

This work highlights the potential of automated tools to complement traditional seismic monitoring methods, balancing accuracy and model complexity while improving efficiency in noise-heavy environments.

How to cite: Cortés Barajas, A. X., Calò, M., Molino Minero Re, E., and Di Luccio, F.: Optimizing neural network architectures and using clustering to detect seismic events in noisy ocean bottom seismometer data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-739, https://doi.org/10.5194/egusphere-egu25-739, 2025.

Mount Fuji volcano, located 100~km away from Tokyo, directly threatens over 30~million people. It last erupted in 1707, and has remained dormant since then. Seismicity --and particularly Low-Frequency Earthquakes (LFE)-- is to now the primary indicator of processes occurring beneath the volcano and is usually linked to fluid movement. Yet, these signals are usually manually picked and classified as such, without the ability to formally define them for automatic detection systems. 

Our goal is to develop an automatic method to detect and classify LFEs, among other seismic events at Mount Fuji using the continuous seismic records from 2008 at 11 stations. First, we use the CovSeisNet software to detect events by analyzing the wavefield coherence, derived from the network covariance matrix width. Over one year of continuous data, the wavefield coherence shows distinct patterns that correspond to various event types, including LFEs and tectonic earthquakes. To enable interpretation, we apply a manifold learning algorithm (UMAP) to reduce the dimension of the coherence patterns into two dimensions to ease the interpretation. We name this low-dimensional representation a "coherence atlas" where each point represents a time window of seismic data, grouped by similarity. This automatic approach enables not only the detection but also the classification of seismic events, as compared with the Japan Meteorological Agency catalog. Moreover, the atlas helps identify previously unrecorded events and facilitates the definition of new event classes. By autonomously mapping and classifying seismic activity beneath Mount Fuji, this method offers unprecedented insights into its activity and allows us to detect new events that had been hidden in the manually prepared catalog.

How to cite: Doucet, A., Seydoux, L., Fuji, N., Aoki, Y., and Métaxian, J.-P.: Unsupervised exploration of seismic activity at Mount Fuji, Japan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2833, https://doi.org/10.5194/egusphere-egu25-2833, 2025.

State-of-the-art earthquake early warning systems use the early records of seismic waves to estimate the magnitude and location of the seismic source before the shaking and the tsunami strike. Because of the inherent properties of early seismic records, those systems systematically underestimate the magnitude of large events, which results in catastrophic underestimation of the subsequent tsunamis. Prompt elastogravity signals (PEGS) are low-amplitude, light-speed signals emitted by earthquakes, which are highly sensitive to both their magnitude and focal mechanism. Detected before traditional seismic waves, PEGS have the potential to produce unsaturated magnitude estimates faster than state-of-the-art systems. Accurate instantaneous tracking of large earthquake magnitude using PEGS has been proven possible through the use of a Convolutional Neural Network (CNN). However, the CNN architecture is sub-optimal as it does not allow to capture the geometry of the problem. To address this limitation, we design PEGSGraph, a novel deep learning model relying on a Graph Neural Network (GNN) architecture.
PEGSGraph accurately estimates the magnitude of synthetic earthquakes down to Mw~7.6-7.7 and determines their focal mechanisms (thrust, strike-slip or normal faulting) within 70 seconds of the event's onset, offering crucial information for predicting potential tsunami wave amplitudes. Our comparative analysis on Alaska and Western Canada data shows that PEGSGraph outperforms PEGSNet, providing more reliable rapid magnitude estimates and enhancing tsunami warning reliability.

How to cite: Hourcade, C., Juhel, K., and Bletery, Q.: PEGSGraph: A Graph Neural Network for Fast Earthquake Characterization Based on Prompt ElastoGravity Signals, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2836, https://doi.org/10.5194/egusphere-egu25-2836, 2025.

The mechanics of slow frictional creep in landslides remains debated and only a few detailed seismic studies have been conducted in landslide-prone areas. To illuminate basal slip processes for a slow moving landslide, we deployed a dense 80 node seismic array at Oak Ridge Earthflow in California’s Diablo Range for one to two months during the rainy seasons of 2023 and 2024, both winters where decimeters of landslide displacement occurred at Oak Ridge. Simultaneously, GNSS receivers, strain meters, and piezometers were deployed at the same site. During our deployments, various types of very small signals were recorded by the seismometers. These events were local, detected only by nearby stations sited within about 100 m of each other. The cause of these events remains unclear, whether due to shear slip at the base of the earthflow or other sources, such as water movement or animal activity. To investigate the cause of these signals, and evaluate the role of stick-slip motion and shear localization, we automatically detected the events and analyzed their spatiotemporal distribution. We used quakephase (Shi et al., 2024) to identify the phases of the very small signals. The primary challenge with automatic picking in our dataset is the long processing time due to high sampling rates. To address this issue, we applied array signal processing, covseisnet (Seydoux et al., 2016), to extract signal candidates based on the coherence of dominant frequencies across the seismic array, followed by automatic picking. This approach successfully and efficiently identified specific signals we believe are associated with earthflow motion. These signals are not continuously observed but concentrate within specific time periods. We focused on events in these time periods, utilizing scattering networks and matched-filter techniques for more detailed classification. By combining our results with other temporal data, such as pore fluid pressure, precipitation, temperature, and displacement, we will discuss the causes of these signals to better understand the mechanism of the earthflow motion.

How to cite: Iwasaki, Y., Schwartz, S., and Finnegan, N.: Classification of Small Seismic Signals Associated with the Oak Ridge Earthflow in California Using a Combination of Machine Learning and Array Signal Processing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5384, https://doi.org/10.5194/egusphere-egu25-5384, 2025.

Traditional well-log analysis often involves incomplete datasets, which reduces the accuracy of petrophysical assessments. This study thus introduces an innovative dual-model approach that integrates a conditional variational autoencoder (CVAE) with a long short-term memory (LSTM) to predict missing shear-slowness (DTS) data and other well-log data. Utilizing well-logs and the corresponding lithological sequence from the Volve oil field in the North Sea, the proposed model demonstrates excellent prediction capabilities when facing multiple types of missing well-logs. Our findings reveal that the CVAE-LSTM model not only enhances DTS prediction accuracy but also adapts to the inherent variability of geological formations. It outperforms traditional autoencoder and standalone LSTM models across a range of metrics, including correlation coefficients, the root mean squared error, and Kolmogorov–Smirnov statistics, validating the predictive accuracy of the proposed model and the alignment of the statistical distributions for predicted and actual logs. The robustness of the proposed model is further highlighted by its ability to maintain its high performance despite the absence of key well-log data such as compressional slowness and the neutron porosity index. This study demonstrates the effectiveness of advanced machine-learning techniques in overcoming the limitations associated with incomplete well-log datasets.

How to cite: Park, J., Jeong, J., and Sinda, M.: Deep-learning-based dual model with an iterative prediction process for the improvement of missing well-log predictions , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5429, https://doi.org/10.5194/egusphere-egu25-5429, 2025.

Monitoring microseismicity is fundamental to advancing our understanding of fault mechanics under natural and anthropogenic influences. Recent advancements in seismological methodologies, particularly those employing deep learning techniques, have significantly improved the detection of weak earthquakes while preserving high levels of precision and reliability.

This study aims to enhance the detection and characterization of seismicity in the Val d’Agri region (Southern Italy) by implementing advanced deep learning-based methodologies, focusing on understanding the tectonic and anthropogenic influences driving seismic activity. The Val d’Agri region is a tectonically active area of considerable scientific and industrial relevance, hosting Europe’s largest onshore oil reservoir and an artificial lake. By employing state-of-the-art deep learning and full waveform earthquake detection methods we identified and located seismic events over a three-year period, achieving a twofold increase in detected events compared to the manually revised bulletin, with a recall rate of ~95%.

Spatial and temporal analyses, based on a density-based clustering approach, revealed distinct seismic clusters. The seismicity is mostly concentrated along the Monti della Maddalena fault system in the southwestern region, characterized by shallow earthquakes (5–7 km depth), while the northeastern and northwestern areas exhibit sparser and deeper activity (15–20 km depth). High-resolution event localization illuminated fault geometries and spatial distributions with high detail. Additionally, our dataset highlights a temporal correlation between seismicity rates and the filling and emptying phases of the Pertusillo artificial reservoir.

Our findings underscore the utility of automated workflows in improving seismic monitoring and fault characterization, providing critical insights into tectonic processes and reservoir-induced seismicity.

How to cite: Caredda, E., Morelli, A., Errico, M., Zerbinato, G., Isken, M. P., and Cesca, S.: Enhancing seismicity detection and characterization in the Val d’Agri region: insights into tectonic and induced processes using Deep Learning techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6412, https://doi.org/10.5194/egusphere-egu25-6412, 2025.

Laboratory acoustic emissions (AEs), resembling small-scale earthquakes, provide vital insights into frictional instability mechanics. Recent advancements in acoustic monitoring technology allow for the rapid collection of thousands of AE waveforms within minutes, highlighting the critical need for efficient detection and analysis methods. This study presents a deep learning model designed to automatically detect AEs in laboratory shear experiments.

Our dataset comprises approximately 30,000 manually identified AE waveforms obtained under varying experimental boundary conditions using two fault gouge materials: Min-U-Sil quartz gouge and glass beads. We modified the PhaseNet model, originally designed for detecting seismic phases in natural earthquakes, by optimizing its architecture and training process to develop AEsNet—an advanced AE detection model that consistently outperforms existing picking methods for Min-U-Sil quartz gouge and glass beads.

To assess the model's generalizability across different boundary conditions and materials, we employed transfer learning, examining performance relative to training dataset size and material diversity. Results indicate that while model performance remains consistent across varying boundary conditions, it is notably influenced by the specific material type due to distinct frequency characteristics inherent to each material. This sensitivity stems from the distinct frequency characteristics of AEs, reflecting the microphysical processes unique to each granular material. Consequently, models trained on one material do not transfer effectively to another.

However, rapid fine-tuning of AEsNet substantially improves its performance, outperforming a similarly fine-tuned PhaseNet model pre-trained on natural earthquakes. This highlights the necessity of tailoring models to the specific features of laboratory-generated AEs, aligning with observations in transfer learning applications for natural seismicity.

In summary, our deep learning approach effectively enhances AE detection across diverse laboratory settings, enabling the creation of reliable AE catalogs that deepen our understanding of fault mechanics. This advancement facilitates the development of reliable AE catalogs, significantly contributing to the understanding of fault mechanics in controlled experimental environments.

How to cite: Scuderi, M. M., Poggiali, G., Pignalberi, F., and Mastella, G.: Readapting PhaseNet to Laboratory Earthquakes: AEsNet, a Robust Acoustic Emission Picker Illuminating Seismic Signatures of Different Fault Gouge Materials, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6730, https://doi.org/10.5194/egusphere-egu25-6730, 2025.

Earthquake Early Warning (EEW) systems are vital for providing timely alerts in seismically active regions, potentially reducing damage and saving lives. However, achieving both rapid and reliable alerts remains a significant challenge. Recent advances in deep learning (DL) and established workflows for picking, associating, and locating events offer complementary paths to improved performance. In this work, we propose to investigate a multi-station deep learning framework that can be integrated with existing event-location pipelines or used directly to estimate ground shaking (e.g., peak ground acceleration, PGA). By fusing raw seismic waveforms with station metadata (e.g., location, sensor characteristics) in an end-to-end manner, the approach aims to capture both local site conditions and regional propagation effects. As an initial step, we will establish baseline performance using simpler neural networks (e.g., CNNs, LSTMs), then expand to more advanced models to evaluate potential gains in accuracy and speed. Preliminary findings indicate that aggregating real-time signals from multiple stations can outperform single-station methods in both alert timing and predictive reliability. Ultimately, our goal is to develop an adaptable, data-driven EEW pipeline that accommodates either direct shaking forecasts or event-based parameter estimation, enabling seamless integration into larger-scale monitoring networks and enhancing the timeliness of earthquake alerts.

How to cite: Puente Huerta, J. A., Sippl, C., and Kuna, V.: Toward a Multi-Station Deep Learning Framework for Enhanced Earthquake Early Warning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7736, https://doi.org/10.5194/egusphere-egu25-7736, 2025.

Seismology, beyond the study of earthquakes, has become an indispensable tool for understanding environmental changes, offering unique insights into a wide range of phenomena and natural risks, from slope instabilities to glacial dynamics and hydrological hazards. However, the sheer volume and complexity of modern seismic datasets, amplified by the emergence of dense seismic networks and technologies such as Distributed Acoustic Sensing (DAS), pose significant challenges. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized our ability to analyze these datasets, enabling a deeper exploration of seismic data to find rare and exotic environmental seismic sources. 

Supervised learning approaches have been successfully applied to create large-scale instrumental catalogs of landslides and other environmental processes, at different  spatio-temporal scales, from short-term datasets recorded on dense local seismic stations networks, to chronicles spanning decades on seismic networks covering whole regions of the world (Alaska, Alps, Greenland). These techniques achieve high detection rates and robust classification of seismic events, even for low-magnitude or rare signals that traditional methods might overlook. Supervised learning approaches also allow us to advance our capability to estimate physical properties from seismic waves, such as the use of machine  learning to infer mass and kinematics of slope instabilities, which provide critical inputs for understanding the dynamics of these events and their associated hazards. These methodologies not only allow us to document environmental processes more exhaustively but also open up possibilities for studying poorly understood or previously undetectable seismic sources. Going beyond supervised learning, we have developed workflows based on self-supervised and unsupervised approaches to analyze continuous seismic data, uncovering unexpected patterns and revealing hidden environmental seismic sources recorded by dense seismic stations networks. Distributed Acoustic Sensing represents another frontier, turning fiber optic cables into dense seismic networks. By combining DAS with innovative AI-driven methods, we have demonstrated the potential to detect and classify low-magnitude earthquakes and anthropogenic sources, even in noisy environments, paving the way for real-time seismic monitoring on unprecedented scales.

By applying these AI-driven approaches, we are enhancing the field of environmental and exotic sources seismology, improving our ability to analyze vast seismic archives, and offering new tools to monitor, understand, and mitigate geohazards in a changing environment. This talk will highlight the latest methodological advances and showcase how they are applied to various geological and environmental contexts, from landslides, avalanches and glaciers in the Alps to fiber optic networks at different scales, underscoring the far-reaching implications of AI for seismological sources identification.

How to cite: Hibert, C., Rimpot, J., Huynh, C., Groult, C., Malet, J.-P., Forestier, G., Weber, J., Jestin, C., Lanticq, V., Provost, F., Turquet, A., and Stangeland, T.: Uncovering environmental and other exotic seismic sources with machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9276, https://doi.org/10.5194/egusphere-egu25-9276, 2025.

How to cite: Dervisi, F., Segou, M., Baptie, B., Poli, P., Main, I., and Curtis, A.: Explainable artificial intelligence for short-term data-driven aftershock forecasts , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9795, https://doi.org/10.5194/egusphere-egu25-9795, 2025.

China is a country prone to earthquakes. Earthquake early warning (EEW) is one of the important means to mitigate earthquake disasters. To mitigate the damage caused by destructive earthquakes, we are currently developing an artificial intelligence (AI)-based EEW system in China. By utilizing emerging technologies such as artificial intelligence and big data analysis, we have developed a complete set of methods for continuous measurement of EEW parameters based on AI. This AI-based method achieves the research goal of improving the accuracy and timeliness of EEW parameters measurement in the entire workflow of EEW system, including waveform interference elimination, earthquake event recognition, P-wave picking, magnitude estimation, seismic damage zone prediction, and so on. Presently, the offline testing results of this AI-based method on earthquake data in the Sichuan-Yunnan region of China show that the AI-based magnitude estimation reaches ±1 magnitude estimation error 4 s earlier than the existing EEW system. Meanwhile, some modules such as AI-based magnitude estimation and waveform interference elimination have been running online in the Fujian Earthquake Agency in China. In the future, China is expected to establish and improve an AI-based EEW system, and further reduce the casualties and economic losses caused by earthquakes through AI-based EEW system.

How to cite: Song, J., Ren, Y., Wang, H., and Wen, R.: The development of AI-based earthquake early warning system in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10208, https://doi.org/10.5194/egusphere-egu25-10208, 2025.

Earthquake early warning (EEW) systems implemented worldwide use early seismic records of P waves to rapidly detect, locate, and estimate the magnitude (M_w) of potentially damaging earthquakes. These systems are well known to saturate for large magnitude events, which results in dramatic underestimation of the subsequent tsunamis. Alternative approaches based on different signals have been proposed to rapidly estimate the magnitude of large events, but these approaches are much slower (taking 10 to 20 minutes for first warning). Prompt elasto-gravity signals (PEGS) are light-speed gravitational perturbations induced by large earthquakes that can be recorded by broadband seismometers. They have tremendous potential for early warning but their extremely small amplitudes (on the order of 1 nm/s²) have challenged their possible operational use. We designed a deep learning approach to rapidly estimate the magnitude of large earthquakes based on PEGS. We applied this approach to the seismic networks operating in Japan, Chile, Alaska and Peru. We will present the performances obtained in these different contexts. In Alaska, the approach has proven capable to reliably estimate the magnitude of M_w ≥ 7.6 earthquakes (without saturation) in less than 2 minutes, outperforming state-of-the-art tsunami early warning algorithms. Motivated by these performances, we initiated a first implementation of an operational tsunami warning system based on PEGS in Peru. We will present the simulated real-time performance of this system. 

How to cite: Bletery, Q., Arias, G., Juhel, K., Hourcade, C., Licciardi, A., Inza, A., Vallée, M., and Ampuero, J.-P.: Machine-learning-based operational tsunami warning from light-speed elasto-gravity signals, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10901, https://doi.org/10.5194/egusphere-egu25-10901, 2025.

We developed a deep learning model for automatic dimensionality reduction and feature extraction from time series. The model employs an encoder-decoder architecture with skip connections, enabling efficient compression and reconstruction of input data while preserving essential features. These features are used for unsupervised clustering enabling anomaly detection, and pattern recognition.

We initially developed the model to analyze seismic data from the 2021 Geldingadalir volcanic eruption in Iceland, successfully identifying a weak yet important pre-eruptive tremor that commenced three days before the eruption. Advancing the architecture with additional layers and skip connections allowed for highly accurate input reconstruction. The latter version, named AutoencoderZ, demonstrated its ability to process different data types. We applied AutoencoderZ to investigate low-frequency patterns preceding the 2023 M_W 7.8 Kahramanmaraş Earthquake in Türkiye. The model identified tremor-like episodes linked to anthropogenic activities at cement plants near the earthquake’s epicenter. Additionally, we applied AutoencoderZ to strainmeter data from the Sea of Marmara, achieving accurate reconstructions and enabling the detection of distinct tectonic-related signals.

This study highlights AutoencoderZ’s potential as a powerful tool for uncovering patterns in continuous geophysical data, providing valuable insights for monitoring and interpreting seismic and strainmeter signals.

How to cite: Zali, Z., Martínez-Garzón, P., Kwiatek, G., Beroza, G., Cotton, F., and Bohnhoff, M.: Unsupervised Clustering and Pattern Identification from Continuous Seismic and Strainmeter Data in Tectonic and Volcanic Settings, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11335, https://doi.org/10.5194/egusphere-egu25-11335, 2025.

To date, the discrimination of seismic events recorded in seismic networks is often performed manually by experts, classifying events into categories such as earthquakes, quarry blasts, or mining events. While recent studies have shown that deep learning algorithms, particularly Convolutional Neural Networks (CNNs), can efficiently and accurately distinguish between different types of seismic events, their application for automated seismic event discrimination remains limited. This limitation arises from several factors, including the absence of globally applicable models that maintain high precision for local seismic networks, the scarcity of data required for fine-tuning Deep Learning (DL) models, and the lack of interpretability in the decision-making processes of these black-box models.

In this contribution, we explore the use of Vision Transformers (ViTs) as a novel approach for automating seismic event discrimination. To assess their potential for accuracy and explainability, we applied CNNs and ViTs to classify seismic events such as earthquakes, quarry blasts, and mining events. For this purpose, we pretrained the models on openly available seismic event data from Utah and Northern California and then fine-tuned and tested them on data from the Seismic Network of the Ruhr-University Bochum (RuhrNet) and the Thuringian Seismic Network (TSN).

Our findings reveal that ViTs can analyze the entire spectrogram of a seismic event in a coherent manner, offering superior generalizability in pattern recognition compared to CNNs. In addition to achieving high discrimination accuracy, the attention weights of ViTs provide insights into the models’ decision-making process, offering a transparent and interpretable explanation of the underlying mechanisms driving its classifications.

How to cite: Kasburg, V., van Laaten, M., Zehner, M., Müller, J., and Kukowski, N.: Automating Seismic Event Discrimination: A Comparative Study of Convolutional Neural Networks and Vision Transformers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11459, https://doi.org/10.5194/egusphere-egu25-11459, 2025.

In this work, we introduce HEIMDALL, a grapH-based sEIsMic Detector And Locator specifically designed for microseismic applications. Building on recent progress in deep learning (DL), HEIMDALL employs spatiotemporal graph-neural networks to detect and locate seismic events in continuous waveforms. It simultaneously associates and provides preliminary locations by leveraging the output probability functions of the graph-neural network over a dense, three-dimensional grid (0.1 km spacing). By integrating detection and location within a single framework, HEIMDALL aims to address persistent challenges in microseismic data analysis, such as accurately associating wavefront arrivals and enabling consistent and robust event localization in complex geothermal regions. To train our models, we utilize synthetics generated using Green’s function available in the area, in combination with a small fine-tuning over a subset of real data. This approach allows us to achieve homogeneous coverage of the study area while addressing nuances that inevitably arise across synthetic and real domains.

Our evaluation focuses on data collected at the Hengill Geothermal Field in Iceland as part of the COSEIMIQ project (December 2018 to August 2021). Specifically, we analyzed one month of continuous seismic recordings from December 2018 and a brief sequence on February 3, 2019, which occurred in the middle of the geothermal plant. The dataset also features frequent burst sequences, providing an ideal testbed for advanced detection and location algorithms. By benchmarking HEIMDALL against multiple approaches, we reveal both the strengths and limitations inherent in our novel method and in more conventional workflows used in observational seismology.

Ultimately, we highlight the importance of continued innovation in ML-based workflows for induced seismicity monitoring at enhanced geothermal system (EGS) sites, where the capacity to detect and accurately locate a large number of microseismic events can be critical for operational safety and resource management.

How to cite: Bagagli, M., Grigoli, F., and Bacciu, D.: A Waveform-Based Graph Neural Network Approach for Microseismic Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13238, https://doi.org/10.5194/egusphere-egu25-13238, 2025.

Ice mass loss in polar regions is a major contributor to sea level rise driven by climate change. To better predict ice mass loss due to calving and melting, it is essential to monitor ice dynamics by linking observed seismic signatures to physical processes such as meltwater infiltration into crevasses or crack formation caused by high tides at the grounding line. However, current knowledge about the distinct patterns of icequake types remains limited.

To address this gap, approximately 15 years of continuous seismic data from the Watzmann Array near the Neumayer Station in Antarctica are analyzed to automatically cluster seismic recordings. This analysis involves the automatic extraction of seismic events and the application of beamforming to each event. As a result, directional information is incorporated and the local noise is significantly reduced.

In the following, clustering methods, combined with techniques like dynamic time warping and feature extraction, are employed to categorize seismic events into distinct groups representing different icequake types. A key focus of this work is on leveraging dynamic time warping to cluster seismic waveforms directly, prioritizing the identification of physical properties inherent in the signals rather than relying solely on features extracted through machine learning. This approach ensures that the obtained clustering reflects the true underlying source processes rather than being limited to abstract feature representations.

In a follow-up study, these clusters can be related to environmental factors and directional information. Finally, with this we hope to shed some light on the hidden source processes of observed icequake types.

How to cite: Kiel, A., Schlindwein, V., and Hammer, C.: Towards the robust Clustering of various cryogenic signal types using Seismic Array Information, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13430, https://doi.org/10.5194/egusphere-egu25-13430, 2025.

Recent advances in machine learning (ML) have enabled significant progress in geoscience by capturing complex relationships and enhancing predictive skills. However, the success of many ML algorithms in data-rich settings does not seamlessly transfer to climate and atmospheric applications, where observational datasets are often limited. This underscores the need for methods that deliver high predictive accuracy under data-scarce conditions while retaining interpretability.

Here, we compare various ML approaches with the Nonlinear AutoRegressive Moving Average model with eXogenous inputs (NARMAX) in typical small-data climate applications, such as seasonal weather forecasting and Greenland Blocking Index (GBI) prediction. NARMAX, a transparent, interpretable, parsimonious and simulatable (TIPS) framework, demonstrates robust performance and avoids common pitfalls such as overfitting and unstable predictions when data are scarce. Notably, it achieves superior or competitive forecast accuracy for small or limited data conditions, underscoring its practical value in operational climate science. By adopting a sparse system identification approach, NARMAX yields model structures that readily reveal key predictors and their relative contributions, providing valuable physical and statistical insights into climate variability.

Our findings illustrate how NARMAX bridges the gap between purely data-driven modelling (focusing on prediction) and mechanistic modelling (focusing on physical insights), offering a clear pathway for refining model strategies and deepening our understanding of climate dynamics. We propose that NARMAX and similar methods play an inherently powerful role for both small and large data modelling problems and meanwhile serve as potent components to potentially improve the explainability of ML methods. By showcasing both interpretability and predictive efficacy, this work encourages the adoption of machine learning methods that best meet the needs for specific data modelling tasks in climate science and beyond.

How to cite: Sun, Y., Wei, H.-L., Hanna, E., and Luu, L.: Small Data for Big Tasks in Seasonal Weather Forecasting: A Balanced Perspective on Interpretability and Predictability of NARMAX and Machine Learning Methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13666, https://doi.org/10.5194/egusphere-egu25-13666, 2025.

Substorms are a magnetospheric phenomenon which causes high geomagnetic and auroral disturbances. It is widely accepted that substorm activity is controlled by solar wind conditions. It is, however, difficult to predict substorms deterministically because of the complex physical processes underlying substorm occurrences. We propose a framework for modelling time series of event occurrences controlled by external forcing. In this framework, occurrences of external-driven events are represented with a non-stationary Poisson process, and its intensity, which corresponds to the occurrence rates per unit time, is described with a simple machine learning model, the echo state network, which is fed with forcing variables. The echo state network is trained by maxmising the likelihood given the event time series data. 

We apply this approach for analysing time series of substorm onsets identified from Pi2 pulsations, which are irregular geomagnetic oscillations associated with substorm onsets. We train the echo state network to well describe the response of substorm activity to solar-wind conditions. We then examine the characteristics of the substorm activity by feeding synthetic solar-wind data into the echo state network. The results show what solar wind variables effectively contribute to the substorm occurrence. 

Our echo state network model is also useful for examining the statistical properties of the substorm occurrence rate. For example, we can evaluate what mainly controls the seasonal and UT variations of substorm activity. There are two explanations for the seasonal and UT variations. One explanation is that the seasonal and UT variations is controlled by the inner product between the solar-wind magnetic field and the Earth's dipole axis. The other is that the variations are due to the angle between the solar-wind flow and the Earth's dipole axis. Since these two effects are related with different input variables in our echo state network model, we can examine the contribution of each effect to the substorm occurrence frequency. The result shows that the seasonal and UT variations are mostly dependent on the angle between the solar-wind flow and the Earth's dipole axis. 

How to cite: Nakano, S., Kataoka, R., and Nose, M.: Modelling of time series of external-driven events with echo state network and its application to substorm activity analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14041, https://doi.org/10.5194/egusphere-egu25-14041, 2025.

Seismic data recorded in shallow water in the Arctic Ocean contain not only primary reflections but also surface-related multiples with strong amplitudes and short-period characteristics. These multiples generate false stratigraphic boundaries on stacked seismic sections, thereby reducing the accuracy of geological interpretation. Therefore, the attenuation of multiples is an essential step in seismic data processing for accurate geological interpretations. Recently, with the advancement of deep learning technology, research on suppressing surface-related multiples using deep learning networks (such as U-Net and stacked BiLSTM) has been actively proposed.

Firstly, surface-related multiple suppression algorithms using U-Net and stacked BiLSTM were applied to Arctic field data respectively. Each algorithm was designed to predict surface-related multiples by using input data that contained both primary reflections and surface-related multiples. Fractional Fourier transform (FrFT) and continuous wavelet transform (CWT), which represent time-series data in the time-frequency domain, were applied to synthetic data and used as input data feature for each network. Finally in order to suppress the surface-related multiples for seismic data in shallow depth Arctic Ocean, the proper methods (network architectures, input data feature) are suggested.

Acknowledgments

This research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries, Korea (RS-2023-00259633).

How to cite: Chae, H., Na, D., Kang, S.-G., and Chung, W.: Deep Learning-Based Surface-Related Multiple Suppression in Shallow Arctic Seismic Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14779, https://doi.org/10.5194/egusphere-egu25-14779, 2025.

Advances in the processing of Global Navigation Satellite System (GNSS) positioning data and the increasing densification of geodetic networks have provided an unprecedented opportunity to detect and analyse transient deformation signals, including Slow Slip Events (SSE). These events, characterised by very slow rupture and durations of days to months, are often associated with areas of low coupling and sometimes show clear recurrence patterns. Despite their importance in subduction zones, reliable detection of SSEs remains an ongoing challenge. The sheer volume of GNSS data, combined with high noise levels and the subtle nature of these signals, requires efficient and robust methods capable of rapidly processing large datasets.

To overcome these challenges, we propose a method that relies on feature extraction techniques and machine learning to improve the detection and analysis of possible SSEs. Specifically, we use the TSFRESH algorithm to extract relevant features from GNSS time series, coupled with supervised machine learning classification techniques. Preliminary results of our current model, trained on synthetic data and validated through various performance tests, demonstrate high detection capabilities and accuracy. We further validated the model using a collection of GNSS time series from the Cascadia subduction zone with a single-station method scaled to the entire network, where the model showed satisfactory performance in detecting possible SSEs compared to similar work. Future efforts will focus on improving the robustness and generalisation of the model to new data, and refining methods for estimating the slip and duration of each possible SSE.

How to cite: Sepúlveda, M., Moreno, M., and Miller, M.: Machine learning and feature extraction for detecting transient signals in GNSS time series, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15039, https://doi.org/10.5194/egusphere-egu25-15039, 2025.

 In seismic exploration data from the Arctic Ocean, refractions are recorded earlier than direct waves due to the shallow depths and subsea permafrost with high velocity. These refraction signals could be utilized for estimating the velocity, thickness, and depth of the subsea permafrost. However, it is very challenging work to pick the accurate first arrivals of seismic data in the Arctic Ocean because of many factors such as ambient noise and etc. Therefore, identifying first-break refractions is crucial and can be performed by manual or automated picking methods. Various semi-automatic techniques have been developed to identify first-break refractions, but these methods are often sensitive to pulse variations and require parameter tuning. Recently, deep learning-based methods have also been explored, but their reliance on training data often results in inconsistent performance, making it essential to generate training data optimized for the target environment. 

This study presents a recurrent neural network-based algorithm optimized for Arctic Ocean environments to automatically identify first-break refractions. To effectively classify first-break refractions, a stacked bidirectional long short-term memory (BiLSTM) network was constructed to iteratively learn bidirectional long-term dependencies by utilizing the temporal patterns of time-series data. Additionally, the training data were generated by creating velocity models that reflect the subsurface properties of subsea permafrost, enabling the generation of first-break refraction label data. The proposed network demonstrated superior performance in identifying first-break refractions from noisy data, achieving over 95% accuracy in numerical experiments and field tests. Field data applications demonstrated that the proposed network achieves high accuracy in classifying first-break refractions, validating its robustness and adaptability.

Acknowledgments

This research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries, Korea (RS-2023-00259633).

How to cite: Jeong, S., Chae, H., Kang, .-G., Shin, .-R., and Chung, W.: Auto-Picking of First-Break Refractions in Arctic Ocean Seismic Data Using Stacked BiLSTM Networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15129, https://doi.org/10.5194/egusphere-egu25-15129, 2025.

Estimation of earthquake parameters has always been a focus for seismologists. Efficient and rapid determination of earthquake location and magnitude is essential for mitigating the potential hazards associated with seismic shaking. Nowadays, Earthquake Early Warning Systems (EEWS) are implemented in most earthquake-prone areas, with the system varying according to the specific needs. Although methods for their estimation exist, many still lack a fast enough process, which is crucial for reducing the waiting time before issuing a warning.

Here, we propose a novel model to enhance multi-station EEWS using Large Language Models (LLM). We adopt a pre-trained LLM and fine-tune it on a customized version of INSTANCE (The Italian Seismic Dataset for Machine Learning), thus eliminating the need to develop and train a tailor-made architecture. The model uses stations with P-wave arrival times up to 5 s apart from the first recorded one, and, for each seismic trace, it exploits a very small time window around the P-wave arrival time (0.21 s), thus effectively reducing warning latency.

Comparative analysis against the automatic method employed by the Italian National Institute of Geophysics and Volcanology (INGV) demonstrates that our model achieves comparable performance in magnitude estimation and superior accuracy in epicenter, hypocenter and origin time prediction. For instance, the LLM-based model achieves average errors of 6.3 km, 11.1 km, and 1.1 s for epicenter, hypocenter, and origin time estimation, respectively, in contrast to 8.6 km, 15.0 km, and 1.8 s for the INGV automatic solution resulting in an average improvement of more than 26% for all parameters.

We study the validity of our model by assessing its ability using P- and S-waves to predict magnitude, and show that in this case study the S-waves are not strictly necessary for accurate predictions.

How to cite: Bassani, A., Trappolini, D., Poggiali, G., Tinti, E., Galasso, F., Maron, C., and Michelini, A.: Real Time Estimation of Earthquake Location and Magnitude Using Large Language Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16157, https://doi.org/10.5194/egusphere-egu25-16157, 2025.

Fault zone properties evolve dynamically during the seismic cycle due to stress changes, microcracking, and wall rock damage. Understanding these changes is vital to gaining insights into earthquake preparation and post-seismic processes. The latter include fault healing, which refers to the recovery of mechanical and elastic properties in fault zones after seismic and aseismic fault slip. Despite its importance, detecting and characterizing fault healing through seismic signals remains a challenge due to the subtle nature of these changes.

In this study, we investigate the potential of deep learning techniques, specifically a 4-layer Convolutional Neural Network (CNN), to characterize post-seismic evolution by analyzing raw seismic waveforms recorded after the largest event (Mw 6.5,  30 October) of the 2016 Central Italy seismic sequence. These data provide a unique opportunity to examine fault zone dynamics. A key aspect of our approach is the hypothesis that ray paths traversing highly impacted areas of the fault zone contain richer information about its temporal evolution. To test this hypothesis, we examined seismic waves from two clusters — DHwS, located in the hanging wall beneath the hypocentral region, and C1, situated in the footwall. They represent contrasting ray trajectories as recorded on seismic stations MC2 and MMO1. Seismic waves recorded at MC2 pass through heavily damaged fault regions, which are likely to reveal evolving fault properties, whereas MMO1 predominantly captures paths that skirt or in the case of C1 completely miss these impacted areas, serving as a comparative baseline.

We assessed temporal variations in elastic properties using binary classification tests on normalized, raw seismic waveforms of events before and after a reference date. This date was arbitrarily selected within the temporal range of the analyzed seismicity and serves solely as a neutral point of comparison. Our hypothesis is that if the CNN can achieve good classification performance, it implies the presence of time-evolving properties in the fault zone, potentially linked to healing processes or other time-dependent factors.

To further validate these findings, we employed adversarial training, a technique designed to disentangle time-dependent effects from structural changes. By introducing controlled label noise into one cluster during training, we isolated the influence of confounding factors such as seasonal variations. Preliminary results suggest that adversarial training enhances the model's robustness and provides valuable insights into the time-evolving properties of the fault zone.

Deep learning offers significant potential for analyzing spatiotemporal changes in elastic properties and thus the evolution of fault zone properties over the seismic cycle. By detecting subtle temporal and structural changes, we hope to gain a deeper understanding of fault dynamics and post-seismic processes.

How to cite: Paoletti, G., Trappolini, D., Tinti, E., Galasso, F., Collettini, C., and Marone, C.: Deep learning to investigate post-seismic evolution of fault zone elastic properties, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17400, https://doi.org/10.5194/egusphere-egu25-17400, 2025.

Reliable synthesis and prediction of seismic waveforms play an important role in evaluating seismic hazards and designing earthquake-resilient structures. However, current methods, such as ground motion models and physics-based simulations, are often limited in fully capturing the complexity of seismic wave propagation, at higher frequencies (>5 Hz). Some of these limitations can potentially be overcome through machine learning techniques. In earthquake engineering, machine learning models have been used for predicting peak ground accelerations and Fourier spectra responses. To model entire waveforms, extensive efforts to generate seismic waveforms have employed advanced machine learning techniques, such as generative models, with most previous approaches relying on generative adversarial networks (GANs). In contrast to these earlier models, this study presents an efficient and extensible generative framework to produce realistic high-frequency seismic waveforms, compared to GANs. Our approach encodes spectrograms of the waveform data into a lower-dimensional sub-manifold using an autoencoder, and a state-of-the-art diffusion model is subsequently trained to generate these latent embeddings. Conditioning is currently performed on key parameters: earthquake magnitude, recording distance, site conditions, and faulting style. The resulting generative model can synthesize waveforms with frequency content up to 50 Hz, from which several scalar ground motion statistics, such as peak ground motion amplitudes, spectral accelerations, or Arias intensity can be directly derived. We validate the quality of the generated waveforms using standard seismological benchmarks and performance metrics from image generation research. Our openly available model produces high-frequency waveforms that align with real data across a wide range of input parameters, including regions where observations are sparse, and accurately reproduces both median trends and variability of empirical ground motion statistics. Our generative waveform model can be potentially used to perform seismic hazard where broadband data are often required such as to train earthquake early warning model. Given the increasing number of generative waveform models, we emphasize that they should be openly accessible and included in community efforts for ground motion model evaluations.

How to cite: Palgunadi, K. H., Bergmeister, A., Bosisio, A., Ermert, L., Koroni, M., Perraudin, N., Dirmeier, S., and Meier, M.-A.: High Resolution Generative Waveform Modeling Using Denoising Diffusion, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19236, https://doi.org/10.5194/egusphere-egu25-19236, 2025.

Single-station waveforms of teleseismic earthquakes are highly complex, because they are a superposition of numerous phases corresponding to different wave types and propagation paths. Moreover, data recorded at single stations is contaminated by noise, which often has similar or larger amplitudes than the arrivals of teleseismic earthquakes, especially in densely-populated areas. For high precision research facilities, for example in the field of particle physics or gravity wave detection, a precise knowledge of the seismic wavefield generated by teleseismic earthquakes can be critical for the calibration of experiments. However, the density of seismological stations is often sparse, particularly in regions with low seismic hazard such as Northern Germany.

To overcome this limitation, we introduce a deep learning scheme for the prediction of very-low-frequency earthquake waveforms from synthetic data at arbitrary locations within the metropolitan area of Hamburg, Germany. For this aim, we propose to train a convolutional neural network (CNN) to predict the measured earthquake waveforms from their synthetic counterparts. While synthetic earthquake waveforms can be conveniently generated for arbitrary coordinates and moment tensors with Instaseis and the IRIS synthetics engine (Syngine), the amount of available measured waveforms is constrained by the availability of seismological stations and their installation date. In this work, we use measured data from a station in Bad Segeberg, north of Hamburg, which has been measuring continuously since 1996. During first experiments, we trained a CNN on data from earthquakes larger than M6.0 and obtained reasonable initial results. However, the number of such earthquakes is limited and the measured waveforms used as labels partly contained noise of considerable amplitude, which caused the neural network to predict unwanted noise.

In order to increase the amount of earthquakes in the training data and mitigate their contamination with noise, we propose a two-step approach. In the first step, we generate a large number of noise-free synthetic waveforms and contaminate them with artificially generated noise that has the same characteristics as the noise measured at the station in Bad Segeberg. With this dataset, we train a first CNN to denoise the synthetic earthquake waveforms. In the second step, we apply the trained neural network to the actual earthquake waveforms measured in Bad Segeberg to denoise them. We then train a second CNN to translate synthetic earthquake waveforms to the denoised measured ones. Results for earthquakes not part of the training data demonstrate that the second CNN provides convincing estimates of measured earthquake waveforms, not only for the station in Bad Segeberg, but also for stations in Hamburg. This can be seen as a first step towards a three-dimensional prediction of the earthquake wavefield without the need for densely-distributed stations.

How to cite: Bauer, A., Walda, J., and Hammer, C.: Prediction of measured earthquake waveforms from synthetic data: a two-step deep learning approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21456, https://doi.org/10.5194/egusphere-egu25-21456, 2025.

The recent advances in seismic data acquisition technology allow to transform fiber-optic cables into dense arrays of geophones that sample nearly continuously the seismic wavefield. This technology, known as Distributed Fiber-Optics Sensing (DFOS) (or Distributed Acoustic Sensing, DAS), has the potential to make cost-effective microseismic monitoring operations in borehole installations. Unlike conventional geophones, fiber-optic cables can be easily installed behind well casings without interfering with injection or production activities, eliminating the need for drilling dedicated monitoring wells.

Despite these benefits, DFOS data is generally characterized by higher noise levels when compared to conventional seismometers. The development of efficient denoising techniques is therefore a critical step to improve the Signal-to-Noise Ratio (SNR) of DFOS recordings, enhancing the capability to detect and analyse microseismic events. Traditional filtering techniques often struggle to recover low-amplitude signals, leading to limited noise reduction performances. In this study, we propose an effective denoising workflow based on an adaptation of spectral-subtractive algorithms, typically used in the context of speech enhancement for audio signals. 

We validate this approach first simulating synthetic DFOS data resembling realistic data acquisition geometries and noise conditions. Then, our denoising workflow is further applied to real DFOS data recorded during the April 2022 stimulation campaign at the FORGE (US) EGS project. 

Our results from both synthetic and real DFOS data show significant SNR improvements, showcasing the robustness of our method even when the original data show poor SNR conditions. This algorithm outperforms standard filtering techniques, offering a promising solution for enhancing DFOS data and improving the detection of previously hidden signals.

How to cite: Pascucci, G., Gaviano, S., and Grigoli, F.: A Speech Enhancement-based Method for Denoising Microseismic Distributed Fiber-Optic Sensing (DFOS) data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1192, https://doi.org/10.5194/egusphere-egu25-1192, 2025.

Distributed Acoustic Sensing (DAS) technology offers a valuable opportunity to enhance seismological monitoring of ocean floors. Typically, in a standard monitoring network, most seismic stations are installed on land, with only a few ocean-bottom seismometers available. Consequently, monitoring oceanic seismicity is intrinsically challenging due to the lack of observations near seismic sources. In this context, DAS systems, for instance, those installed on fibers connecting islands and land-islands, can help bridge this observational gap. However, integrating the spatially dense DAS data (meter scale) with the often sparser seismometer data (kilometer scale) is not straightforward. Specifically, inverting DAS alongside seismometer P- and S-wave arrival times can lead to biased location results due to the numerical disparity between datasets and/or outliers not identified. Moreover, employing the full set of DAS arrival times can be computationally intensive, limiting its feasibility for real-time monitoring and integration into routine seismological software.

Automated weighting methods can help mitigate bias introduced by arrival time outliers in data inversion. This is particularly useful for DAS data, where user control over individual channels is limited. However, suppose the goal is to use DAS as a complementary tool to a seismometer network, and meter-scale spatial density is not essential. In that case, DAS data selection/sub-sampling can improve computational efficiency. To this end, we propose an approach that, for a given seismological network and DAS system, a) automatically identifies “reliable” DAS channels using a machine learning classifier trained on specific data attributes and b) further selects a subset of DAS channels to achieve similar interchannel spacing to the network. The proposed strategy generates a final set of DAS P- and S-wave arrival times with a number of observations comparable to the network. To test the benefits of this procedure on oceanic seismic monitoring, we use data from a fiber optic cable northeast of Gran Canaria and seismometers operated by the Instituto Geográfico Nacional (IGN) in the Canary Islands. We then compare event locations obtained using: a) IGN-only P- and S-wave arrival times, b) DAS-only P- and S-wave arrival times (unselected), and c) IGN and selected DAS P- and S-wave arrival times using the proposed method. Event locations are estimated using a hierarchical Markov Chain Monte Carlo approach.

Preliminary results show promising improvements in the location uncertainty of oceanic seismicity when the proposed data integration approach is applied. Additionally, a refined location catalog, incorporating a more detailed velocity model, is compiled using standard monitoring software alongside the proposed data selection approach.

How to cite: Bozzi, E., Piana Agostinetti, N., Ugalde, A., Cubas Armas, M., Rodriguez, T., Latorre, H., J. Vidal-Moreno, P., and Saccorotti, G.: A Pragmatic Approach for Integrating Submarine DAS Data with Onshore Seismometer Networks: A Case Study in the Canary Islands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1193, https://doi.org/10.5194/egusphere-egu25-1193, 2025.

Recent advances in seismology underscore the potential of innovative instrumentation and data-driven methods to overcome long-standing challenges in source localization. Traditional techniques such as P-wave polarization or arrival time analysis often suffer from reduced precision in complex wavefields, where scattering and heterogeneities distort seismic signals. These limitations highlight the need for methods that leverage emerging technologies and provide robust uncertainty quantification.
In this study, we used a novel approach for determining the back azimuth of seismic sources using six degrees of freedom (6-DoF) ground motion measurements, enabling precise source localization from single-point data. We tested the method in a controlled experiment, tracking the migration of a vibroseis truck across 160 distinct locations. Each of the 480 recorded sweep signals, lasting 15 seconds and spanning a frequency range of 7–120 Hz, was analyzed to derive back azimuth values.
One key innovation in this approach is the use of a wavefield fingerprinting algorithm to isolate SV-type waves, significantly improving the precision of back azimuth estimates. This step addresses the inherent challenges posed by the sensitivity of rotational sensors primarily to S waves and the scattering effects that degrade localization accuracy as the source moves farther from the receiver. By isolating the SV wavefield, our method reduced deviations in back azimuth estimates to a maximum of 2.2 degrees, compared to deviations of up to 48.6 degrees when the entire wavefield was analyzed.
Our findings not only demonstrate the value of combining advanced monitoring instruments with wavefield-specific processing techniques but also highlight the importance of integrating uncertainty quantification into seismic analyses. This approach offers a pathway to more robust localization methods, especially for applications requiring high-resolution imaging or real-time seismic monitoring in complex tectonic environments.

How to cite: Izgi, G., Eibl, E. P. S., Krüger, F., and Bernauer, F.: Accurate Back Azimuth Determination Using 6-DoF Measurements and Wavetype Fingerprinting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4181, https://doi.org/10.5194/egusphere-egu25-4181, 2025.

Machine-learning algorithms are widely applied to facilitate human tasks. For instance, in seismology, they can help deliver high-resolution seismic catalogs including very small magnitude events that usually remain undetected by human analysts and by standard monitoring procedures. 

The new frontier of modern seismology is to exploit deep neural networks (DNN) to automatically detect P- and S-wave arrival times to obtain good-quality seismic event locations in terms of hypocentral errors. Increasing the number of events and seismic phases is essential to build complete earthquake catalogs to be used in seismological analyses (such as seismic hazard estimation, seismic tomography, fault zone structure determination, rupture mechanism study, etc.). 

Machine Learning methods are being integrated into large Research Infrastructures (RIs), like the European Plate Observing System (EPOS ERIC), which brings together 10 different scientific domains in Solid Earth Sciences. In this contribution, we present results from a specific Sponsored Research Activity promoted by the RI EPOS and dedicated to ML-driven methods for phase picking in seismic time series.

To ensure the correct recognition of seismic waves, neural networks trained on large and representative training datasets are essential. Here, we investigate the influence of the training dataset on the DNN PhaseNet performance, applying the method to three case studies. In the first case, the DNN trained with the Italian seismicity dataset (INSTANCE) is used to build a catalogue on the Fucino basin (Central Italy) study area; in the second case, we use the AQ2009 training dataset (based on the L’Aquila 2009 aftershocks) to analyse the 2016-2017 Amatrice-Visso-Norcia seismic sequence; and finally, the CREW training dataset (that contains P- and S-waves reflected on the mantle Earth and recorded at large epicentral distance) to detect P- and S- waves of teleseismic (regional) data acquired by the Adria Array project network. 

The use of different training datasets greatly improves the performance of the neural network in recognizing P and S phases, reducing the number of false positives and providing more accurate and precise P and S-phase arrival times. 

How to cite: Fonzetti, R., Bailo, D., De Gori, P., Valoroso, L., Anselmi, M., Bagh, S., Trani, L., and Chiarabba, C.: How do automatic phase pickings based on deep neural networks perform on different-scale case studies?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5992, https://doi.org/10.5194/egusphere-egu25-5992, 2025.

This work introduces a novel framework for full-waveform seismic source inversion using simulation-based inference (SBI). Traditional probabilistic approaches often rely on simplifying assumptions about data errors, which we show can lead to inaccurate uncertainty quantification. SBI addresses this limitation by building an empirical probabilistic model of the data errors using machine learning models, known as neural density estimators, which can then be integrated into the Bayesian inference framework. We apply the SBI framework to point-source moment tensor inversions as well as joint moment tensor and time-location inversions. We construct a range of synthetic examples to explore the quality of the SBI solutions, as well as to compare the SBI results with standard Gaussian likelihood-based Bayesian inversions. We then demonstrate that under real seismic noise, common Gaussian likelihood assumptions for treating full-waveform data yield overconfident posterior distributions that underestimate the moment tensor component uncertainties by up to a factor of 3. We contrast this with SBI, which produces well-calibrated posteriors that generally agree with the true seismic source parameters, and offers an order-of-magnitude reduction in the number of simulations required to perform inference compared to standard Markov chain Monte Carlo techniques. Finally, we apply our methodology to a pair of moderate magnitude earthquakes in the North Atlantic. We utilise seismic waveforms recorded by the recent UPFLOW ocean bottom seismometer array as well as by regional land stations in the Azores, comparing full moment tensor and source-time location posteriors between SBI and a Gaussian likelihood approach. We find that our adaptation of SBI can be directly applied to real earthquake sources to efficiently produce high quality posterior distributions that significantly improve upon Gaussian likelihood approaches.

How to cite: Saoulis, A., Piras, D., Spurio Mancini, A., Joachimi, B., and Ferreira, A.: Can you trust your uncertainties? Improving Bayesian earthquake source inversions using simulation-based inference, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6145, https://doi.org/10.5194/egusphere-egu25-6145, 2025.

Understanding rupture mechanisms, seismicity propagation, distribution, and migration after a major earthquake relies on the quality of earthquake catalogs, particularly their detection capabilities, location accuracy, and magnitude completeness. On February 27, 2010, a Mw 8.8 earthquake struck the Maule region in south-central Chile, causing widespread damage and substantial loss of life. As the largest well-instrumentally recorded earthquake in Chile, this event offers a unique opportunity to revisit an old dataset, refine the aftershock sequence analysis, and gain deeper insights into subduction zone dynamics.

Here we analyze ~10 months of continuous seismic data from the International Maule Aftershock Deployment (IMAD), a temporary network with about 156 stations deployed throughout the rupture zone. Using the recent Back-Projection and Matched Filtering (BPMF) workflow, which integrates PhaseNet, a deep-learning-based phase picker, we detected more than 100,000 earthquakes with at least 10 P and S-wave arrival phases. We relocated these events using NonLinLoc-SSST-Coherence, a probabilistic algorithm. A subset of them served as templates for template matching, producing a final catalog of about 375,000 events. This represents nearly a ninefold increase in detected events compared to prior catalogs and achieves a magnitude of completeness of Mw ~1.7, lowering it by over one order of magnitude.

Our catalog significantly enhances the spatio-temporal resolution, revealing intricate seismic structures (e.g., fault geometries) and dynamic post-seismic activity. Our improved relocations draw these key features, including the shallower seismic zone in the Pichilemu-Vichuquén region (33.5°S–35°S) and deeper seismic clusters near Concepción (37°S–38°S) in unprecedented detail. Temporal b-value variations (0.8–1.1) reveal zones of high-stress accumulation and the activation of multiple fault systems, highlighting the heterogeneous nature of post-seismic deformation. This high-resolution dataset underscores the potential of modern methodologies and algorithms, unveiling features from older data with improved clarity and detail.

How to cite: Flores-Allende, R., Seydoux, L., Beaucé, É., Bonilla, L. F., Gueguen, P., and Satriano, C.: An enhanced earthquake catalog of the 2010 Mw 8.8 Maule aftershock sequence using modern tools, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6874, https://doi.org/10.5194/egusphere-egu25-6874, 2025.

An inverse problem involves deducing the causes from observed effects within a system, often requiring the solution of partial differential equations that describe the underlying physics. In geophysical seismic imaging, the objective is to reconstruct subsurface structures, such as velocity and density fields, by analyzing seismic waveforms recorded at the Earth’s surface. This process involves solving the non-linear wave equation to model seismic wave propagation through the Earth. Full Waveform Inversion (FWI) is a deterministic technique that employs gradient-based methods. Despite its potential, FWI faces challenges such as non-uniqueness, local minima, and computational complexity, highlighting the critical need for advanced methods to address these issues and quantify uncertainties in subsurface property estimation.

Bayesian inference provides a robust framework for solving inverse problems and estimating uncertainties by applying Bayes' theorem. This approach derives a posterior probability density function for model parameters based on observed data. In this study, we present an innovative method that parametrizes unknowns using Generative Adversarial Networks (GANs), enabling the creation of realistic subsurface representations by learning the prior distribution in a latent space. Once trained, the GAN remains fixed, serving as a generative prior for Bayesian posterior sampling.

We compare and evaluate four posterior sampling methods, i.e. the Metropolis-adjusted Langevin Algorithm (MALA), variational Bayesian inference using normalizing flows (NF), inference neural networks (INN), and Stein Variational Gradient Descent (SVGD). The performance of these methods is assessed in terms of computational efficiency and accuracy in capturing the posterior distribution. By integrating deep generative priors with advanced Bayesian sampling techniques, we demonstrate significant improvements in handling the high dimensionality and non-linearity inherent in geophysical inverse problems. This work contributes to the development of advanced methods for seismic imaging and uncertainty quantification, aligning with the need for robust, data-driven approaches in the field of geophysics.

How to cite: Xie, Y., Chauris, H., and Desassis, N.: Generative AI and Bayesian methods for seismic imaging and uncertainty estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7358, https://doi.org/10.5194/egusphere-egu25-7358, 2025.

Finding optimal wave sampling methods has far-reaching implications in wave physics, such as seismology, acoustics, and telecommunications. A key challenge is surpassing the Whittaker-Nyquist–Shannon (WNS) aliasing limit, establishing a frequency below which the signal cannot be faithfully reconstructed. However, the WNS limit applies only to periodic sampling, opening the door to bypass aliasing by aperiodic sampling. In this work, we investigate the efficiency of a recently discovered family of aperiodic monotile tilings, the Hat family, in overcoming the aliasing limit when spatially sampling a wavefield. By analyzing their spectral properties, we show that monotile aperiodic seismic (MAS) arrays, based on a subset of the Hat tiling family, are efficient in surpassing the WNS sampling limit. Our investigation leads us to propose MAS arrays as a novel design principle for seismic arrays. We show that MAS arrays can outperform regular and other aperiodic arrays in realistic beamforming scenarios using single and distributed sources, including station-position noise. While current seismic arrays optimize beamforming or imaging applications using spiral or regular arrays, MAS arrays can accommodate both, as they share properties with both periodic and aperiodic arrays. More generally, our work suggests that aperiodic monotiles can be an efficient design principle in various fields requiring wave sampling.

How to cite: Grushin, A. and Mordret, A.: Beating the aliasing limit with aperiodic monotile arrays, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7397, https://doi.org/10.5194/egusphere-egu25-7397, 2025.

Seismic activity provides critical insights into subsurface processes such as tectonic movements, volcanic activity, and fluid migrations, with accurate earthquake locations being essential for enhancing our understanding of these seismic behaviors. However, seismic array geometry significantly influences earthquake location accuracy. Over the past two decades, seismologists have improved earthquake catalogs by expanding seismic networks and densifying station coverage in seismically active regions, leading to more precise event detection and location accuracy. Following major seismic events, temporary seismometer deployments refine monitoring and analysis, particularly for aftershocks, enhancing the understanding of the region's seismic behavior and potential risks. In this study, we introduce a novel method that benefits from these temporary deployments to relocate hypocenters determined by a permanent seismic array, using hypocenters derived from a combination of permanent and temporary arrays with better geometry. Our method employs a random forest algorithm to learn how to relocate seismic
events detected with the permanent, low-density seismic array. We developed the method in the case of Mayotte Island, where, following the eruption in 2018, scientists deployed ocean-bottom seismometers (OBS) and land seismic sensors to build high-quality catalogs that provide a better understanding of the region's dynamics. Our findings show a significant reduction in root-mean-square error between the hypocenters located with permanent seismic stations and those located with a combination of permanent and temporary seismic stations, demonstrating the method's effectiveness in reducing systematic biases and enhancing location accuracy. This method is applicable across a range of contexts, particularly in scenarios characterized by suboptimal seismic station geometry, offering a robust framework for enhancing the location accuracy of seismic events.

How to cite: Mohammadi, F., Seydoux, L., Retailleau, L., and Satriano, C.: Machine learning enhanced earthquake relocation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10327, https://doi.org/10.5194/egusphere-egu25-10327, 2025.

Enriching seismic network catalogues is a key object to understand the seismotectonic processes and seismic hazard. In areas with poor knowledge of seismotectonic patterns, the installation of dense local seismic networks is essential. The enhanced density of seismic network coverage improves the detection of seismic events of low energy. Nowadays, Machine Learning (ML) techniques are becoming widely used in seismology, in addition to standard automatic procedures (i.e., STA/LTA-based algorithms).

In this study, we assess the performance of automatic P- and S-wave picking and earthquake detection algorithms for the period from 2013 to 2022, applied to the data recorded by the OTRIONS seismic network (FDSN code OT), a local network installed in 2013 in the Apulia region (Southern Italy) by UniBa and INGV.

The aim is to provide an automated data analysis system to collect a catalogue of the seismic activity of the Gargano area. For the period 2013-2022, a catalogue has been collected by employing CASP (Complete Automatic Seismic Processor), a software based on STA/LTA algorithms for automated event detection, picking and location. The obtained CASP automated catalogue has been manually revised to identify false events and quarry blasts.

Now, for the same period, the goal is to compile a new seismic catalogue for the Gargano area by using PhaseNet, an ML algorithm for phase detection, based on a deep neural network. We used GaMMA algorithm for phase association. Finally, NonLinLoc software was used to locate the events.

The results revealed a significant increase in the number of detected events with respect to the CASP processing. To evaluate the reliability of the results obtained by PhaseNet and GaMMA, a manual revision has been carried out on a sub-dataset of the collected event catalogue and compared with the CASP manual catalogue for the same period: we observed a significant increase in the earthquake detection. This increase also relates to events whose reliability has been verified.

From a seismotectonic point of view, the newly detected seismicity confirms the seismicity pattern of the Gargano Promontory, characterized by a deepening of the earthquakes trend moving northwards in the area with a clear and well defined cut off of the seismicity in the lower crust. This peculiar result is one of the most intriguing findings of the study and could provide important indications on the thermo-rheological characteristics of the lower crust.

Finally, to improve the knowledge of the seismogenic structures of the Gargano area, the new package of NonLinLoc, NLL-SSST-coherence, was used to looking for seismogenic structures. Preliminary results show that the Gargano area is characterised by widespread seismicity.

How to cite: Ferreri, A. P., Cecere, G., Filippucci, M., Ninivaggi, T., Panebianco, S., Romeo, A., Satriano, C., Serlenga, V., Selvaggi, G., Stabile, T. A., and Tallarico, A.: Enhancing the seismic catalog of the Gargano Area (Southern Italy) with machine learning-based event detection and earthquake relocation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13258, https://doi.org/10.5194/egusphere-egu25-13258, 2025.

The Artius broadband node represents a transformative innovation in seismic instrumentation, designed to bridge the gap between traditional broadband seismometers and popular nodal systems. While broadband seismometers offer unparalleled sensitivity and frequency range, their cost and complexity often limit large-scale and dense deployments. Conversely, geophones provide cost-effective solutions for high-frequency applications but lack sensitivity to low-frequency seismic signals, which are critical for many research and monitoring purposes. Artius provides a cost-effective compromise, delivering the increased sensitivity and a true broadband frequency range at an economic price point.

Designed by Güralp Systems, Artius integrates a compact, highly sensitive broadband seismometer with an environmentally sealed anodized aluminium enclosure, ensuring optimal performance and robustness across diverse geophysical applications. Boasting a response of 30 seconds to 200 Hz, Artius greatly outperforms geophone-based systems while remaining perfectly suited to rapid, temporary deployments where it can be either pushed or staked into the ground and connected to an external power supply. Artius pushes the limits of versatility, facilitating real time data monitoring, as well as passive data collection. Artius also features an onboard SEEDlink server, ensuring compatibility with all standard seismological monitoring techniques and distinguishing it from existing solutions.
To manage the ever-growing seismic datasets generated by instruments like Artius, Güralp's Discovery software provides an advanced platform for seismic data acquisition, processing, and analysis. Discovery was designed to be proficient in handling large datasets and integrates seamlessly with Artius supporting both offline playback and real-time data analysis. 

Artius is designed to be docked into an eight-node capacity docking station for data validation and mass data download. The docking station also serves as a “huddle” system for configuration and testing prior to deployment, ensuring each node is performing optimally prior to deployment. The Artius nodes are intended to be deployed in large arrays, perfect for passive seismology, ambient noise studies, and earthquake studies.
By combining advanced instrumentation like the Artius broadband node with innovative data processing capabilities provided by Discovery, Güralp Systems is advancing the frontiers of observational seismology. These technologies empower researchers to tackle new challenges in seismic data analysis.

How to cite: Mohr, S., Hill, P., Lindsey, J., Restelli, F., Watkiss, N., Calver, J., and Kerkenyakova, A.: Güralp Artius - A novel triaxial broadband node designed for Large-N arrays, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17158, https://doi.org/10.5194/egusphere-egu25-17158, 2025.

Machine learning (ML) has seen widespread use in seismology recently, with a significant focus on earthquake monitoring. ML models are now available for phase picking, first motion polarity determination, etc. Implementing them in standard monitoring software (e.g. SeisComP) could significantly improve the automatic earthquake monitoring and save time for human analysts, whilst leveraging all the existing benefits of existing mature monitoring frameworks. An important first step for moving the ML models from research into production has been the Python package SeisBench (Woollam et al., 2022; DOI: 10.1785/0220210324), which allows users to benchmark and access ML models and datasets. The scdlpicker SeisComP module (Tillman et al., 2023; DOI: 10.5194/egusphere-egu23-10046) created an interface between SeisComP and the trained ML pickers in SeisBench to allow event-based re-picking (i.e., not real-time phase onset detection) as demonstrated using teleseismic earthquakes and the GEOFON network. Here, we build on top of the existing scdlpicker module to provide both P and S picks at local distances, and add pick uncertainty and P-pick first motion polarity. We demonstrate the performance of this extended module in routine earthquake monitoring at the Swiss Seismological Service (SED) and show the improvements over classical pickers currently in use. We show that the ML pickers improve the automatic monitoring in both the number and the quality of the picks, leading to better automatic locations and magnitudes. We show that the ML picker’s characteristic function provides a good proxy of the human analyst assigned pick uncertainty. Additionally, this extended SeisComP module provides the ML-determined first-motion polarity for each pick, fully characterizing the pick itself (pick time, pick uncertainty, first motion polarity) in the same way a manual analyst would do. This allows the adoption of streamlined workflows in which the automatic (i.e. ML) picks would only be reviewed (and in most cases accepted) rather than re-picked from scratch by the human analyst (as currently done at SED).  

How to cite: Jozinović, D., Clinton, J., Massin, F., Diehl, T., and Saul, J.: Advancing Operational Earthquake Monitoring at Local and Regional Scales with Machine Learning-Enhanced SeisComP Tools - as Demonstrated in Switzerland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17226, https://doi.org/10.5194/egusphere-egu25-17226, 2025.

In recent decades, geothermal systems have gained increasing importance and attention. They have the potential to greatly contribute to the transition toward green energy and the establishment of a climate-neutral economy. Enhanced Geothermal Systems (EGS) represent a significant advancement in energy production methodologies. EGS utilize hydraulic stimulation techniques to inject and extract fluids, thereby enabling the harnessing of geothermal energy, which is crucial for electricity generation.

In addition to the existing natural seismicity, this production loop of hot and cold fluids may generate induced seismic events, specifically those caused by pressure changes that affect active faults or lead to stress variations within the rock volume. For these reasons, EGS could potentially induce medium to severe earthquakes that might impact nearby communities if not properly monitored and managed, or if strict monitoring methods are not followed to mitigate risks at EGS sites, particularly during operational stages.

Various physical and mechanical properties are recorded in real-time during operational stages. With the continual advancement of deep learning methods, these time series data can be analyzed individually and collectively for short-term forecasting of expected seismic magnitudes from future earthquakes.

Specifically, this work presents an experimental technique that leverages the spatio-temporal capabilities of graph neural networks by connecting these time series within a dynamic graph structure for short-term predictions of the maximum expected magnitude. This method is effective in identifying relationships that traditional approaches can sometimes overlook. Our preliminary results indicate that our algorithm can indeed assist in risk mitigation at EGS sites, potentially serving as a valuable complement to the current state-of-the-art frameworks (i.e., Traffic Light Systems, TLS) used globally.

How to cite: Bagagli, M., Grigoli, F., and Bacciu, D.: Leveraging Deep-Learning Methods for Operational Analysis at Enhanced Geothermal Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17747, https://doi.org/10.5194/egusphere-egu25-17747, 2025.

In the process of coal mining, pressure will be induced in the mining face, resulting in the stress concentration of surrounding rock, which will affect the safety and orderly normal operation of coal energy mining. The all-fiber optic micro-seismic monitoring technology has the advantages of high sensitivity, wide range and high safety, and can monitor the pressure activity of coal mine in real time. Taking the II1012 mining face of Taoyuan Coal Mine as the engineering background, the all-fiber optic micro-seismic monitoring work is carried out, and the data are analyzed by the methods of micro-seismic event detection, identification, classification and location. The characteristics of micro-seismic activity during the first pressure, periodic pressure and square pressure are studied. The results show that: Large energy events in the first pressure stage play a major role in roof failure, and the first pressure interval is 25.10 m. In the periodic pressure stage, the influence of micro-seismic activity on the roof is greater than that on the floor, but the influence on the floor is increasing. The large energy events increase significantly in the square pressure stage, which is easy to promote the frequent occurrence of high intensity and stress micro-seismic activities. The occurrence of micro-seismic events in mine pressure phases have advanced characteristics. There is a positive correlation between the intensity of micro-seismic activity and the rate of recovery, and the all-fiber optic micro-seismic has a good response to the mine pressure. The research work provides theoretical basis and technical support guidance for the safe production of the II1012 mining face in Taoyuan Coal Mine and other similar mining faces in other coal mines, reduces the risk of geological disasters caused by micro-seismic events during the pressure period, and further guarantees the safe and normal orderly development of the subsequent production work of the mining face. It is of great significance to the safe mining of coal energy and the supply of production and life.

How to cite: Wang, K.: Research on Coal Mine Pressure Characteristics Based on All-fiber Optic Micro-seismic Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17795, https://doi.org/10.5194/egusphere-egu25-17795, 2025.

The Central Apennines region (Italy) is regularly affected by seismic activity. In 2009, a Mw 6.1 earthquake occurred close to L’Aquila city. Although the crisis is over, there is still seismic activity in the region (ML<2.8).  To improve our understanding of the structure and dynamics of seismogenic fault zones in Central Apennines, within the GRANDE (hiGh Resolution imAging  of Normal faults Damage zonEs) experiment, we deployed nine dense linear arrays of seismic nodes (157 nodes) crossing two well studied fault damage zones at the surface (Campo Imperatore and Monte Marine fault systems). Previous geophysical and geological surveys precisely characterized these fault damage zones down to a few tens of meters (Fondriest et al., 2020; Cortinovis et al., 2024), but how this fault-related damage extend in depth is yet poorly understood. This temporary network was installed in May 2024, for a one-month period of recording in continue. We used a software using machine-learning to pick, detect and create a preliminary seismic event catalogue. Several tests were realized to check the nature of the event (anthropic or seismic) and the quality of detection. Then, we applied a relocation program to obtain a better location of the events and create a high-quality event catalogue for the one-month recording period. Moreover, we cross-correlated the signal at pairs of stations to retrieve a local tomographic picture of the studied areas. This new and original seismological dataset allow deepening our understanding of the structure of fault damage zones from surface to depth and improve our knowledge about dynamics of large earthquakes rupture, interseismic strain accumulation and release for one of the most hazardous faults in Europe.

How to cite: Vassallo, M., Basques, M., Pavoni, M., Tarantino, S., Barone, I., Fondriest, M., Di Giulio, G., Boaga, J., Poli, P., Courbis, R., and Hillers, G.: The GRANDE Array project: temporary seismic network in the Central Apennines, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18368, https://doi.org/10.5194/egusphere-egu25-18368, 2025.

Recent studies have demonstrated the potential of deep learning (DL) techniques for denoising seismic signals and improving signal analysis, but they are not yet widely adopted in seismic monitoring. Denoising models are typically applied to short segments of triggered data. To use the full potential of state-of-the-art denoising models for seismicity catalogue generation, the methods need to be applicable to continuous data. Several challenges arise in this case, in particular during dense (aftershock) sequences, as models may fail to consistently detect signals near or across the window edges, or require overlapping windows that lead to several parallel denoised waveform solutions. 

We investigate the optimal integration of denoising approaches into operational network settings to enhance seismic catalogues, focusing on improving detections, phase picks, and peak amplitude measurements. As we are most interested in characterising weak events that are commonly missing or poorly described in existing catalogues,  special attention is given to them. We train and compare a range of promising algorithms, including a method that operates on time-frequency representation of the data and outputs segmentation masks to separate event and noise signals. 

We evaluate the approach on seismic data recorded by the Swiss network, and train a model on recorded noise and about 25k earthquake signals, corresponding to most of the available high-quality, local recordings. 

To assess the benefit of the denoiser, we test it on a dense seismic sequence recorded by different types of seismic sensors under diverse site and noise conditions. We employ the denoiser to detect event signals, and produce continuous denoised data, which then serve as the input for standard phase pickers and event associators. We compare the derived catalogue to those obtained with i) standard and ii) DL tools, both applied on raw data. We demonstrate i) significantly deeper catalogues in the first case, and ii) catalogues comparable to those obtained with DL pickers, but with enhanced characterisation, including event location and magnitudes.

How to cite: Dahmen, N., Clinton, J., and Meier, M.-A.: Operational Seismic Denoising Workflow to Enhance Seismic Catalogues, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20208, https://doi.org/10.5194/egusphere-egu25-20208, 2025.

Focal mechanism and moment tensor computation based on regional and local waveforms has become routine task in seismology. These tools are essential for understanding seismotectonic stress regimes and are among the most widely used data for stress inversion, playing a crucial role in identifying deformation zones and tectonically active structures at both local and regional scales (e.g., Totaro et al., GRL 2016; Martínez-Garzón et al., JGR SE 2016).

Many different and similar approaches are available to perform inversion for the double-couple, deviatoric or full moment tensor. However, a key aspect often not fully addressed is the estimation of moment tensor uncertainty. It can be mostly caused by measurement (e.g., data contamination by noise) and theory errors (e.g., mathematical simplifications), and can affect the accuracy of results limiting their interpretation. Over the past decades, considerable efforts have been made in this context, and Bayesian inference is increasingly being applied in moment tensor inversion problems due to the advantage of quantifying parameter uncertainties (Vasyura-Bathke et al., SRL 2020). The Bayesian approach allows for a thorough exploration of the solution space by using appropriate samplers (e.g., Del Moral et al., JRSS 2006) and generates an ensemble of solutions rather than a single optimal one, providing a measure of uncertainty based on the solution distribution.

In this study, we focused on testing the stability of double-couple solutions obtained using two recently developed open-source software packages: BEAT (Bayesian Earthquake Analysis Tool; Vasyura-Bathke et al., SRL 2020) and MCMTpy (Yin and Wang, SRL 2022). These moment tensor inversion algorithms are extremely useful for estimating source parameter uncertainties and the range of acceptable solutions. We applied them to the 2016 Mw 6.0 Amatrice mainshock and a Mw 3.2 earthquake from the same sequence occurred in Central Italy, in order to check the performance of the algorithms at different magnitude levels. We selected this region due to several reasons: it is characterized by active tectonics, it benefits from good azimuthal coverage of seismic stations, and it offers plenty of moment tensor solutions obtained using different approaches (e.g., Scognamiglio et al., BSSA 2009; Artale Harris et al., JGR SE 2022).

For these two earthquakes we compared the results obtained by BEAT and MCMTpy with solutions available in the main seismic catalogs to evaluate the overall coherence of the results and the possible improvements in resolution and robustness. Then, we focused on the performance evaluations by proposing a series of methodological tests which simulate different data setup as not-optimal network geometry, epicentral location errors, biases in the velocity model. By applying these tests on the selected algorithms, we (i) explored their stability, (ii) identified their limitations in resolving double-couple moment tensors and (iii) evaluated the related uncertainty estimates. By doing so, we provide a comprehensive understanding of how these algorithms perform in real-world scenarios and we also suggest an approach useful to verify and eventually compare the performance of moment tensor inversion algorithms also taking into account the uncertainty estimates.

How to cite: Mancuso, T., Totaro, C., and Orecchio, B.: Comparison of moment tensor inversion methods in a Bayesian framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-399, https://doi.org/10.5194/egusphere-egu25-399, 2025.

The study area is Tbilisi, the capital of Georgia, which is the most densely populated part of the region and is undergoing rapid urbanization. Tbilisi is situated in a tectonically active and stressed region, characterized by significant seismic activity. Additionally, the area features various geological rock structures and complex topography; thus, the seismic effects of an earthquake will vary across different geological zones.

Given these factors, assessing the impact of natural hazards on building sites is a critical prerequisite for construction projects. To achieve this, it is necessary to analyze soil categories and physical-mechanical properties in accordance with building codes.

In the initial phase, our objective was to collect all available materials from geophysical and geological surveys conducted in Tbilisi. We created an online database that facilitated the selection of new research locations based on an engineering-geological map. Subsequently, we performed a geophysical survey at over 100 locations and generated a map of Vs30 points across Tbilisi.

Calculating the average shear wave velocity Vs for a specific depth range (top 30 meters) can be performed using various methods. We used the seismic refraction method and multi-channel analysis of surface waves (MASW), tailored to the geological area. Field data collection, processing, and interpretation were conducted according to ASTM standards. Seismic data were processed using the SeisImager and ParkSEIS software packages.

Following the guidelines outlined in the Georgian building code and Eurocode 8, we classified the ground category at each surveyed point.

How to cite: Akubardia, D., Godoladze, T., Javakhishvili, Z., Jorjiashvili, N., Tserodze, M., Tsiklauri, D., and Tatunashvili, G.: Geophysical Analysis Of Soil Properties For Engineering-Geological Studies And Urban Planning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1838, https://doi.org/10.5194/egusphere-egu25-1838, 2025.

The use of open-source processing tools represents a strategic resource for the scientific community. The Open Science philosophy (https://www.unesco.org/en/open-science) promotes transparency, reproducibility and accessibility to data and source codes. This not only ensures continuous and collaborative development, but also increases the quality of proposed solutions.

Characterizing the near surface based on geophysical methods is of considerable interest for many disciplines, and the reliability and quality of the provided results is tied to the available processing resources. The surface wave analysis (SWA) of active seismic data is widely used to determine the shear wave velocities of a site. Several efforts have been made to create open-source tools for SWA, starting with the precursor Geopsy (Wathelet, 2005), continuing with the more recent SWIP (Pasquet and Bodet, 2017), MASWaves (Olafsdottir et al., 2018), and SWprocess (Vantassel and Cox, 2022). The classical procedure they propose is limited to a local 1D analysis on (moving) spatial windows, where homogeneous conditions are assumed. Although this is a robust approach, it does not highlight small-scale lateral variations.

In this talk, we introduce a new open-source tool under continuous development  for processing surface wave data. The Python-based library incorporates, in addition to the classical 1D analysis on moving windows, more advanced techniques such as the Multi-Offset Phase Analysis (MOPA; Strobbia and Foti, 2006) and the Tomography-like approach (Barone et al., 2021), which perform high-resolution 2D SWA for a more accurate identification of lateral velocity variations. The ultimate intent of our Python library is to contribute to further developing standards for processing and inversion of surface wave data in a proper 2D sense.

References

Barone I., Boaga J., Carrera A., Flores Orozco A. and Cassiani G., 2021. Tackling Lateral Variability Using Surface Waves: A Tomography-Like Approach. Surveys in Geophysics 42, no. 2, 317–38. https://doi.org/10.1007/s10712-021-09631-x

Olafsdottir E. A., Erlingsson S., and Bessason B, 2018. Tool for Analysis of Multichannel Analysis of Surface Waves (MASW) Field Data and Evaluation of Shear Wave Velocity Profiles of Soils. Canadian Geotechnical Journal 55, no. 2, 217–233. https://doi.org/10.1139/cgj-2016-0302

Pasquet S., and Bodet L., 2017. SWIP: An Integrated Workflow for Surface-Wave Dispersion Inversion and Profiling. GEOPHYSICS 82, no. 6, WB47–61. https://doi.org/10.1190/geo2016-0625.1

Strobbia C., and Foti S., 2006. Multi-Offset Phase Analysis of Surface Wave Data (MOPA). Journal of Applied Geophysics 59, no. 4, 300–313. https://doi.org/10.1016/j.jappgeo.2005.10.009

Vantassel J. P., and Cox B.R., 2022. SWprocess: A Workflow for Developing Robust Estimates of Surface Wave Dispersion Uncertainty. Journal of Seismology 26, no. 4, 731–56. https://doi.org/10.1007/s10950-021-10035-y

Wathelet M., 2005. Array recordings of ambient vibrations: surface-wave inversion. Ph.D. Thesis, University of Liège (Belgium)

How to cite: Barone, I., Roser, N., Carrera, A., and Flores Orozco, A.: A new open-source Python toolbox for processing seismic surface wave data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6731, https://doi.org/10.5194/egusphere-egu25-6731, 2025.

How to cite: Redfearn, A. and Leptokaropoulos, K.: Interactive Time-Dependent Seismic Hazard Assessment with SHAppE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8491, https://doi.org/10.5194/egusphere-egu25-8491, 2025.

The Matlab toolbox B3AM (B3AMpy for Python) for three-component beamforming of ambient noise data provides a means to characterise the seismic (noise) wavefield and image near-surface seismic properties quickly and cheaply. Provided with three-component array data, B3AM outputs dispersion curves for pro-/retrograde Rayleigh and Love waves, estimates of wavefield composition and propagation direction as a function of frequency, and can be extended for surface wave anisotropy analysis. We present recent results from seismic array data gathered at geothermal sites in the Netherlands, the UK, and Switzerland using B3AM or B3AMpy.

For the geothermal site Kwintsheul (NL), we derive a shear-velocity profile for the first 500 meters, updating an existing profile based on P velocities and regional v_p/v_s estimates. Comparing dispersion curves from beamforming to those from cross-correlation interferometry, we find that the Rayleigh first higher mode seems to provide most of the energy in the considered frequency range and that the fundamental mode can only be recovered using the beamforming scheme but not from interferometry.

Using a nodal seismic data set collected at the Eden geothermal project (Cornwall, UK), we investigate the anisotropy of the ambient noise wavefield and relate it to faults and fractures in the area. With the additional module AssessArray we estimate the effect array geometry and source distribution have on observed anisotropy. AssesArray synthesises a data set by computing (vertical component) phase shifts at each station location corresponding to a wavefield excited by a single source or multiple sources distributed randomly around the array. We then beamform the data set as we do for real data (although for 1 component only) and analyse the variation in velocity and number of detections as a function of azimuth and frequency. We find that the array design introduces frequency dependent anisotropy as well as apparent dominant directions of wave energy that align with the maximum aperture of the array. Further, we find that the number of sources used in creating the synthetic wavefield affects the observed anisotropy. In general, we observe a larger magnitude of anisotropy for a larger number of sources, i.e., for a more complex wavefield, whereas apparent anisotropy is small or not detectable for fewer sources or a single source, respectively.

For the GeoHEAT project, which explores a joint analysis of passive seismic and borehole geo-radar data for characterising and monitoring fractured geothermal systems, we implemented and tested the beamforming workflow for a novel nodal data set from the Kanton of Thurgau (CH). Besides dispersion analysis and source directionality, we consider wavefield composition and classify time windows with respect to their dominant wave type to inform and improve Green’s function recovery for ambient noise cross-correlation tomography.

How to cite: Löer, K., Simonet, G., Kennedy, H., Finger, C., and Hudson, T.:  Near-surface characterisation with B3AM: case studies of 3C ambient noise beamforming from geothermal sites across Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9026, https://doi.org/10.5194/egusphere-egu25-9026, 2025.

Recently the automatic processing of seismological data recorded in areas with anthropogenic seismicity has become an important issue, as the number of available recordings has increased significantly over the past few years. The problem is also addressed within the DT-Geo Project WP8: Anthropogenic Geophysical Extremes, where a specific workflow is being developed for the automatic processing of induced seismicity-related waveforms. The workflow is designed as a set of independent applications implemented inside the interactive, publicly available Episodes Platform (https://episodesplatform.eu/). The applications created for the workflow include:

• A tool for picking the first arrivals of seismic waves using neural network-based solutions available within the SeisBench library,
• A phase association tool that employs the PyOcto algorithm,
• Location procedures already existing within the Episodes Platform.
• An application for calculating spectral parameters using the spectral fitting method,

Ultimately, the workflow will enable fully automated processing of raw and continuous seismic data, including event detection, localization, and spectral parameter calculation. The workflow can be used on the Episodes Platform either with data collected from various Episodes, which are geophysical datasets related to regions where induced seismicity has been observed, or with datasets uploaded by the user.

This work is supported by Horizon Europe grant DT-Geo 101058129 and a project co-financed by the Minister of Science Republic of Poland under contract no. 2024/WK/05. We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2024/017279.

How to cite: Rudzinski, L., Kokowski, J., Kocot, J., and Siejkowski, H.: Automatic workflow for detection, localization, and calculation of spectral parameters for induced seismicity on the Episodes Platform, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10682, https://doi.org/10.5194/egusphere-egu25-10682, 2025.

Fracture surface morphology influences important rock joint behavior, such as shear strength, fluid flow, contaminant transport, and heat transfer. Digitizing a fracture surface in the laboratory or from a drill core is state of the art – its quantitative assessment is not. There are many suggestions and comparisons of roughness parameters, each highlighting a different morphological feature. However, models and experiments dealing with surface roughness often present only a single quantity – if any at all. This makes those experiments and simulations difficult to reproduce and compare.

On the other hand, temperature profiles along boreholes, fractures, or mine shafts can provide a tremendous amount of information. For example, the determination and monitoring of water and heat fluxes, as well as heat generation mechanisms, are possible through the analysis of such temperature-depth profiles. Hence, understanding complex hydraulic systems using temperature as a tracer is possible with comparatively simple measurement devices. However, the analysis and processing of such profiles are so far primarily based on experience and individual data perception.

This work presents two toolboxes developed to standardize data-driven analysis of geophysical data: (1) FSAT – A fracture surface analysis toolbox; (2) TDprof – Algorithm-based segmentation of temperature-depth profiles. Both toolboxes provide easy access to common methods of data analysis in their field. This includes well-documented open-source code, maintenance of the code base, videos, guides, and manuals.

Building on the experience with these two toolboxes for geophysical data analysis, this contribution highlights the differences, additional efforts needed, and potential benefits of going the extra mile in delivering (re-)usability to the scientific community, while being “low-key” on continuous maintenance.

How to cite: Heinze, T.: A fracture surface analysis toolbox and a temperature-depth profiler – toolboxes for standardized geophysical data analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12479, https://doi.org/10.5194/egusphere-egu25-12479, 2025.

The 2D Bayesian transdimensional inversion methodology is a data-driven methodology that allows for multiple solutions and does not need regularization. This is because Bayesian transdimensional inversion allows the retrieval of the parameters and the number of parameters needed to explain the data simultaneously. It also has intrinsic parsimony, meaning simple solutions will be chosen over complex ones. For all these reasons, it is a perfect tool to retrieve the spatial b-value variation. 
The b-value corresponds to the slope of the Gutenberg–Richter law, which relates the number of earthquakes with their magnitude. Several authors agree that the changepoints of the b-value (i.e., the places where the b-value varies) show more valuable information than the value by itself. In particular, the spatial changes in the b-value in seismicity catalogs have been associated with different stresses, fluid processes, geological structures, and earthquake hazard estimation. 
Given this parameter's importance, robustly retrieving and characterizing b-values and their changepoints is essential. In general, most of the methodologies to retrieve the b-value fix the spatial window of the seismic catalog (i.e., binning) and/or use optimization methods to obtain the values, introducing methodological bias in the solutions. For this reason, we use the Bayesian transdimensional approach to objectively estimate b-value variations along two arbitrary dimensions. This implementation allows a self-defined seismic domain according to the seismic catalog information, where it is unnecessary to prescribe the location and extent of domains, as other methodologies do. 
This study focuses on obtaining 2D spatial b-values changes across the seismic region. To explore the possible changes in the b-value along the space, we use the TransTessellate2D algorithm that allows us to implement the trans-dimensional inference methodology for 2D cartesian problems with Voronoi cells. The synthetic tests were performed to analyze the spatial resolution of the methodology and the smallest b-value variation that the method can retrieve. This methodology has been successfully implemented in central-northern Chile and California, allowing us to characterize the mechanical behavior on the plate interface of subduction and cortical zones, obtaining a similar solution to previous studies, evidencing the reliability of the Bayesian transdimensional method for capturing robust b-value variations. Our future work includes extending the approach to other 2D dimensions (e.g., time, latitude, longitude, depth). 

How to cite: Morales-Yáñez, C., Benavente, R., Cummins, P., Sambridge, M., and Hawkins, R.: 2D Bayesian transdimensional inversion for b-value variations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14527, https://doi.org/10.5194/egusphere-egu25-14527, 2025.

TerraceM is an open-source software for mapping and analysing marine terraces. One of the primary challenges in accurately mapping marine terraces is the limited availability of digital elevation data with the resolution necessary to capture the subtle and ephemeral morphology of these geomorphic features. Recent advancements in remote sensing, such as NASA's ICESat-2 satellite mission, offer new opportunities to address this limitation. The ICESat-2 was designed to study Earth's polar ice, land canopy, and bare-earth topography using its Advanced Topographic Laser Altimeter System (ATLAS), a laser-based instrument similar to a LiDAR sensor, providing highly accurate surface elevation measurements in the form of geolocated photons along profiles. While the data are not continuous, the mission has completed thousands of orbits, densely covering most of the world's coastal areas with photon profiles, making it possible to achieve highly accurate mapping of marine terraces.

The latest version of TerraceM introduces new scripts and graphical user interfaces (GUIs) to efficiently interact with ICESat-2 photon data. These features enable users to select, download, preprocess, and map marine terraces interactively. Preprocessing capabilities include filtering canopy signals and reconstructing nearshore bathymetry, allowing the analysis of both subaerial and submarine terraces. Additionally, the new version of TerraceM supports MATLAB and Python, broadening its accessibility to a wider range of users. TerraceM-3 delivers advanced modelling and mapping functionalities, empowering researchers and students involved in marine terrace studies. By leveraging ICESat-2 data, TerraceM significantly extends our ability to analyse past sea-level changes and understand the interplay between tectonics and climate processes in coastal environments.

How to cite: Jara-Muñoz, J., Weiß, M., Mey, J., Pedoja, K., and Melnick, D.: TerraceM 3.0: Advancing marine terrace mapping using worldwide open satellite altimetry of the ICESat-2 mission., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15670, https://doi.org/10.5194/egusphere-egu25-15670, 2025.

This study is about the pilot development of a satellite image-based spatial analysis tool to support rural spatial planning. For the sustainable development and systematic use of space in rural areas, accurate, integrated, and data-driven decision-making is essential. However, the dispersed management of rural-related data, non-standardized formats, and the absence of periodic monitoring systems are acting as obstacles to establishing effective plans. To address this issue, this study applied the following methodology. First, rural-related data stored separately by the central government and local governments were collected and processed into standardized spatial data based on Geographic Information System (GIS).  Second, a satellite image-based facility detection and classification tool was developed for periodic and efficient monitoring of rural facilities. The target facilities were selected as livestock, factories, and solar panels, and the latest deep learning model based on HRNet-OCR architecture was implemented and optimized for the rural environment. For the training and validation data, the mosaic image of the Korean Peninsula (2019~2020) produced by KOMPSAT satellite images was used, which provided a high-resolution spatial resolution of 1m and multiple spectral bands to enable the analysis of various indicator characteristics. Third, to verify the effectiveness of the developed tool, Seosan City, Anseong City, Naju City, and Geochang County in the Republic of Korea were selected as pilot areas. These regions were deemed to represent diverse rural characteristics and facility distributions. Finally, a user-friendly web-based information support tool was developed by integrating processed rural data and satellite image analysis results. The results of this study are expected to be utilized as foundational data for establishing rural spatial plans to support rural spatial restructuring and regeneration, and the developed spatial analysis tool is deemed capable of contributing to the formulation of more efficient and sustainable rural development strategies by providing a data-driven decision support system to rural policymakers.

How to cite: Oh, K.-Y., Lee, K.-J., Chang, J.-Y., Lee, M.-J., Chae, G.-Y., and Park, N.-J.: Pilot development of a satellite image-based spatial analysis tool to support rural spatial planning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17793, https://doi.org/10.5194/egusphere-egu25-17793, 2025.

Confirming the prompt and accurate notification of earthquakes is vital for mitigating their potential impacts. To achieve this, statistical approaches, including Machine Learning (ML), have become indispensable tools across various scientific fields, particularly in Seismology and seismic data. This research explores the utilization of ML techniques to improve earthquake real time alerts. The case study is Greece and the surrounding region, an area with highest seismic activity throughout the Mediterranean.   

This work is focused on the real time collection and processing of an extensive earthquake dataset to generate earthquake alerts by making phone calls and providing details about the time, magnitude, and epicenter of each seismic event. Previous efforts aimed to extend these alerts beyond the notifications (emails and messages) that analysts at the Seismological Center of AUTH (Aristotle University of Thessaloniki) received during their duty. The goal was to make these alerts accessible to all citizens, communities, civil protection agencies and various authorities (e.g. municipalities, schools, police, etc.). The island of Kefalonia served as a pilot region where this functionality was initially implemented. We then chose to extend the application to all Ionian islands to encompass the entire region.

The new insight here is the development of a mobile application that allows users to define a specific geographical region for receiving notifications-alerts. The AI Service will combine the real time earthquake information in conjunction with the geometry defined by each user in order to classify whether a notification should be sent to that specific user.

As training input data used in the application, we first require a catalog of earthquakes spanning from 2011 to 2025 with M≥3.0, along with demographic data for Greece region provided by the Hellenic Statistical Authority. A radius around each epicenter is calculated by considering the earthquake’s macroseismic Intensity (I), the earthquake’s magnitude (M), earthquake depth, total population and number of households within the calculated radius. The labeled dataset is then used to train a classification model via Azure AutoML. This model identifies significant earthquakes and determines which areas to call in order to provide earthquake alert. Notification messages could be to any subscribed mobile number with the calling voice available in Greek, English, or French. 

How to cite: Paradisopoulou, P., Spyrou, G., Karagianni, I., Adamaki, A., and Leptokaropoulos, K.: Applying Statistical and Machine Learning Methods for rapid earthquake alert system in Greece with a new mobile application , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19627, https://doi.org/10.5194/egusphere-egu25-19627, 2025.

Thermochronology is one of the most versatile tools available to geoscientists to constrain the colling and exhumation history of rocks. In tectonically active mountain belts around the world, it is not unusual to have many hundreds, if not thousands of published ages available from various studies. Although several well-established thermal models allow for a detailed exploration of how cooling or exhumation rates evolved in a limited area or along a transect, integrating large, regional datasets into such models remains a major challenge. Here, we present age2exhume, a thermal model in the form of a Matlab or Python script, which can be used to rapidly obtain a synoptic overview of exhumation rates from large, regional thermochronometric datasets. The model incorporates surface temperature based on a defined lapse rate and a local topographic relief correction that is dependent on the thermochronometric system of interest. Other inputs include sample cooling age, uncertainty, and an initial (unperturbed) geothermal gradient. The model is simplified in that it assumes steady, vertical rock-uplift and unchanging topography when calculating exhumation rates. For this reason, it does not replace more powerful and versatile thermal-kinematic models, but it has the advantage of simple implementation and rapidly calculated results. In our example datasets, we show exhumation rates calculated from 1785 cooling ages from the Himalaya, 1587 cooling ages from New Zealand, and 916 cooling ages from Central Asia (Tian Shan and Pamir). Despite the synoptic nature of the results, they reflect known segmentation patterns and changing exhumation rates in areas that have undergone structural reorganization. These regionally estimated exhumation rates have been used in combination with other datasets to assess regional climatic versus tectonic controls on key aspects of the landscape, including river valley width and modern erosion patterns.

How to cite: Schildgen, T. and van der Beek, P.: Age2exhume - A Matlab/Python script to calculate exhumation rates from thermochronometric ages, with application to the Himalaya, New Zealand, and Central Asia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20472, https://doi.org/10.5194/egusphere-egu25-20472, 2025.

The International Seismological Centre (ISC) combines seismic observations from ~150 agencies in ~100 counties to produce the definitive global earthquake catalogue by combining seismic phase arrivals. As well as seismic phase data, hypocentres and magnitudes the ISC Bulletin includes other earthquake parameters such as moment tensors that are reported by many agencies. This data is freely accessible, searchable and downloadable through the ISC website (www.isc.ac.uk/iscbulletin). The ISC Earthquake Toolbox for MATLAB provides access to this parametric earthquake data via a graphical user interface (GUI) within the MATLAB environment. The GUI replicates the search options of the ISC website and reads this data into MATLAB. Several live scripts are included to demonstrate how to interrogate the ISC Bulletin data. Examples include plotting earthquake aftershock sequences, comparing different magnitude and hypocentre types and authors, as well as plotting moment tensors reported in the ISC Bulletin. The toolbox also enables 3D visualisation of earthquake distributions, 2D and 3D moment tensor plotting, as well as introducing new functionality to plot moment tensors within MATLAB mapping toolbox figures. It is hoped that the ISC Earthquake Toolbox for MATLAB will be used as a teaching tool to explore the wealth of earthquake data available at the ISC, as well as a tool for researchers to build more complex applications upon. The toolbox is publicly available to download via GitHub (github.com/tomgarth/ISC_Earthquake_Toolbox) and MathWorks file exchange (https://uk.mathworks.com/matlabcentral/fileexchange/167786-isc-earthquake-toolbox).

How to cite: Garth, T., Gallacher, R., and Leptokaropoulos, K.: The International Seismological Centre (ISC) Earthquake Toolbox for MATLAB: Interactive Access to Earthquake Observations & Parameters, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20943, https://doi.org/10.5194/egusphere-egu25-20943, 2025.

Statistical models are a frequently used tool in hydrology, especially when it comes to estimating design floods, i.e. flood events that used to design flood protection systems or reservoirs. The often complex hydrological data, which are affected by e.g. missing values, extremes or time-varying processes, require sophisticated statistical models that take these challenges into account. As a scientist, developing such models can be a lot of fun and provide interesting insights. After months of thinking about the best model under certain statistical assumptions, proving asymptotic theorems and testing the model with synthetic data, you are happy and proud to have developed a new model. This model will hopefully be widely used in future research. The next step is to apply the model to a large real data set. The results look good on average. The results will be shared with practitioners, because of course you want the model to be useful for science and practice. And then: the phone call. You are told that your results are not plausible for a certain catchment area. And in general, the new model is not needed in practice because there is an established model. This example describes such a case and discusses ways of dealing with it. It is intended to illustrate the importance of communication between science and practice and a general understanding between both sides.

How to cite: Fischer, S.: When practical considerations impact your scientific model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1620, https://doi.org/10.5194/egusphere-egu25-1620, 2025.

In the early 1990's, fractals and chaos were hot. In 1987, James Gleick had published "Chaos: Making a New Science", popularizing non-linear dynamics. Hydrologists played an important role in the development of fractal theory. Hurst had discovered that sequences of dry and wet years for the Nile showed very long memory effects. Instead of the chance of a dry year following a dry year being 50%, Hurst found that there were surprisingly many long series of dry or wet years. Seven fat years, seven lean years, as it is noted in Genesis. Scott Tyler found fractals in soils ("Fractal processes in soil water retention"). At Cornell, where we were at the time, David Turcotte described "Fractals in geology and geophysics". A few years later, Ignacio Rodríguez-Iturbe and Andrea Rinaldo would publish "Fractal River Basins: Chance and Self-Organization". In short, fractals were exciting scientific gold.

A fractal is not just an obscure mathematical object but something that can actually be found everywhere in nature. Early on, a paper was published in Nature with the title "Fractal viscous fingering in clay slurries" by Van Damme, Obrecht, Levitz, Gatineau, and Laroche. They "only" did an experiment on a fractal embedded in 2D; we should be able to do one better and find the fractal dimension of the surface of cracking clay embedded in 3D. So out we went, collected some clay, mixed it with water in a cement mixer, siliconed together a shallow "aquarium", and poured in the slurry. To observe the cracking of the drying slurry, a video camera was mounted above the experiment, looking down and taking time-lapse images. To access the views from the sides, mirrors were installed at 45 degrees at each of the four sides. Lights made sure the camera captured high quality images. The whole set-up was enclosed in a frame with dark cloth to ensure that lighting was always the same.  We already had some box-counting code ready to calculate the fractal dimension of the surface, called the Minkowski–Bouligand dimension. One variable needed some extra attention, namely the boundary between the clay slurry and the glass sides. If the clay would cling to the sides, it would be difficult to understand the effects that this boundary condition had on the outcome of the experiment. Moreover, the cracks may not have become visible in the mirrors when the sides were covered with mud. So, instead, it was decided to make the sides hydrophobic with some mineral oil. This ensured that when the clay would start to shrink, it would come loose from the sides. Now, all we had to do was wait. It took only a week or so before the consolidated slurry started to shrink and to come loose from the sides. After that, the clay continued shrink for many weeks. This is how we learned that the fractal dimension of a shrinking brick of clay is (very close) to 3.0. 

How to cite: van de Giesen, N. and Selker, J.: The Minkowski–Bouligand dimension of a clay brick, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1660, https://doi.org/10.5194/egusphere-egu25-1660, 2025.

In 2018, we found exciting new results in landform evolution modeling by coupling the two simplest models of fluvial erosion and hillslope processes. While the stream-power incision model is the simplest model for detachment-limited fluvial erosion, the diffusion equation is the simplest description of hillslope processes at long timescales. Both processes were added at each grid cell without an explicit separation between channels and hillslopes because fluvial erosion automatically becomes dominant at large catchment sizes and negligible at small catchment sizes.

We found that increasing diffusion reduces the relief at small scales (individual hillslopes), but even increases the large-scale relief (entire catchments). As an immediate effect, the hillslopes become less steep. In turn, however, we observed that the network of the clearly incised valleys, which indicates dominance of fluvial erosion over diffusion, became smaller. So a smaller set of fluvially dominated grid cells had to erode the material entering from the hillslopes. To maintain a morphological equilibrium with a given uplift rate, the rivers had to steepen over long time. This steepening even overcompensated the immediate decrease in relief of the hillslopes.

This result was counterintuitive at first, but we were happy to find a reasonable explanation. So we even prepared a short manuscript for a prestigious  journal. We just did not submit it because we wanted to explain the effect quantitatively from the physical parameters of the model. From these theoretical considerations, we found that our numerical results did not only depend on the model parameters, but also on the spatial resolution of the model and noticed that this scaling problem was already discussed in a few published studies. Beyond the scaling problem, we also realized that applying the concept of detachment-limited fluvial erosion to the sediment brought from the hillslopes into the rivers is quite unrealistic. A later study including fluvial sediment transport and a model for hillslope processes that avoids scaling problems did not predict any increase in large-scale relief. So we finally realized that our original findings were mainly the result of a specific combination of models that should not be coupled this way and are not  as relevant for landform evolution as we thought.

This example illustrates many of the pitfalls of numerical modeling beyond purely technical issues. In particular, combining models that are widely used and make sense individually may still cause unexpected problems.

How to cite: Hergarten, S. and Robl, J.: Landslides and hillslope erosion increase relief, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5035, https://doi.org/10.5194/egusphere-egu25-5035, 2025.

In this presentation, I will discuss my research into the simple climate model Hector, which calculates temperature change based on the impact of various climate scenarios. More specifically, I will discuss how an artistic-led approach through (un)voluntary-caused computational bugs can help document the model's logic and socio-political implications. I will describe methods for collective 'debugging' to produce transdisciplinary knowledge (beyond solely scientific inquiry) to foster conversation about the potential and limits of current climate infrastructure to foster concrete climate actions. This research investigates the field of climate science through artistic practice, software and infrastructure studies, and participatory methods. To expand on the role of bugs in my investigation, I will elaborate on concrete examples of differences in perception of 'error' in the fields of arts and science, looking at case studies where mistakes or glitches have been valorised and mobilised through artistic practice to grapple with, appropriate, and/or repurpose scientific instruments.

How to cite: Legrand, G.: (Re)(De)bugging tragedies with Hector, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5091, https://doi.org/10.5194/egusphere-egu25-5091, 2025.

"Doesn't this look a bit strange?" 

It began with an innocent question during one of our Master's colloquia. And it could have ended there. "We were just following an approach from the literature". And who could argue against following the literature?

But it bugged me. During a long train ride, I began to think about the issue again. 10 hours and many papers later, I was only more confused: was it really that obvious, and why had no one picked up on it before? But sometimes the most obvious things are the most wicked, and after a few conversations with knowledgeable colleagues, I was sure we were in for an unexpected surprise. 

A commonly used approach to defining heat extremes is as exceedances of percentile-based thresholds that follow the seasonal cycle. Such relative extremes are then expected to be evenly distributed throughout the year. For example, over the 30-year period 1961-1990, we expect three (or 10%) of January 1s to exceed a 90th percentile threshold defined for the same period - and the same for all other days of the year. In a recent study, we show that there are many cases where this does not hold, not even close (Brunner and Voigt 2024).

Here, we tell the story of how this blunder spread in the literature out of the desire to improve extreme thresholds. We show that seemingly innocent changes can sometimes have unintended consequences and that taking the time to check the obvious can help avoid mistakes in science. 

Brunner L. and Voigt A. (2024): Pitfalls in diagnosing temperature extremes, Nature Communications, https://doi.org/10.1038/s41467-024-46349-x

How to cite: Brunner, L., Meindl, M., and Voigt, A.: Improving extreme temperature definitions until they are wrong, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5951, https://doi.org/10.5194/egusphere-egu25-5951, 2025.

When economists estimate the expected economic damages from current-day CO₂ emissions, they usually calculate the social cost of carbon – that is, the aggregated damage caused by the emission of an additional ton of CO₂. Several cost-benefit integrated assessment models (IAMs) are built to assess this quantity, and among them is the META model. This model is built specifically to assess the effects of tipping points on the social cost of carbon, and it usually operates stochastically. When integrating a deterministic, but small carbon cycle tipping point into the model, however, the social cost of carbon seems to explode: a few gigatons of additional emissions almost double the impact estimates of CO₂ emissions! Well, maybe. In fact, these results are a pure artifact of two things: 1) the way in which social cost of carbon estimates are calculated with IAMs; and 2) the way that tipping points are implemented in the META model. And, of course, 3): a lack of initial thoughtfulness on behalf of myself. A thorough look into this issue shows that, as expected, a marginal change in emissions leads to a marginal change in damage estimates. While that result is rather boring, the previous blunder can actually be instructive about the scarcely-known methods used to obtain economic impact estimates of climate change.

How to cite: Schaumann, F.: Drastic increase in economic damages caused by a marginal increase in CO2 emissions?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9145, https://doi.org/10.5194/egusphere-egu25-9145, 2025.

Historically, large areas across the globe have been affected by deforestation or irrigation expansion. The replacement of forests with agricultural land and increased water availability in irrigated croplands altered the land’s surface properties, leading to influences of biogeophysical changes on near-surface temperature. From limited observations and mostly idealized simulations, we know that sufficiently large alterations of land surface properties can theoretically lead to systematic temperature and precipitation changes outside and even far from the altered areas. Not only the advection of temperature anomalies, but also changes in circulation and ocean feedbacks have been shown to be potential drivers of such non-local responses in single and multi-model studies.

We tested the robustness of non-local temperature signals to internal variability in the fully coupled Community Earth System Model 2 (CESM2) simulations of the historical period (1850 – 2014) with all forcings vs. all-but-land-use-change forcings. Doing so, we first found seemingly robust non-local temperature effects of land use change on the global and regional scale. But when accounting for the sampling of internal variability in the model using a large initial condition ensemble, the global scale signal was found to be indistinguishable from noise. Only regionally in some hotspots, we found robust and historically important non-local temperature signals. Through increasingly rigorous analysis, we reached a partly negative and unexpected but important finding, which may have implications for future assessments of comparably weak or spatially heterogeneous forcings to the Earth system.

How to cite: Jäger, F., Sieber, P., Simpson, I., Lawrence, D., Lawrence, P., and Seneviratne, S. I.: How robust are modeled non-local temperature effects of historical land use changes really?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10285, https://doi.org/10.5194/egusphere-egu25-10285, 2025.

Failures are only common in science, and hydrological modelling is no exception. However, we modellers usually do not like to talk about our mistakes or our overly optimistic expectations and, thus, “negative” results usually do not get published. While there are examples where model failures indicated issues with the observational data, in this presentation the focus is on modelling studies, where some more (realistic) thinking could have helped to avoid disappointments. Examples include the unnecessary comparison of numerically identical model variants, naively optimistic expectations about increasing the physical basis of bucket-type models and excessively hopeful assumptions about the value of data.

How to cite: Seibert, J., Clerc-Schwarzenbach, F., van Meerveld, I., and Vis, M.: Think twice – pitfalls in hydrological modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10615, https://doi.org/10.5194/egusphere-egu25-10615, 2025.

The idea of invisible ship tracks for the study of aerosol-cloud interactions sounds promising: We have been studying the effects of aerosols on clouds for many years, among others by investigating the bright lines of clouds left in low marine clouds by ships. However, only a small fraction of ships leaves behind visible tracks. This means we can only study aerosol-cloud interactions under certain meteorological conditions, biasing our understanding. Instead, by studying all clouds polluted by ships ('invisible ship tracks') with a methodology we developed, we should be able to get a full picture of aerosol-cloud interactions. A number of interesting and impactful results have come out of this research, along with several setbacks and corrections to initial results. Here, we examine them in order, showing how correcting for one identified bias can introduce two new ones. Unexpected glitches arise from sources as varied as: choices regarding ship track definition, retrieval geometry, specific weather systems biasing results, and mathematical subtleties. What can we conclude after four years of progress on this methodology? While some results still stand, others had to be significantly corrected. This makes us see invisible ship tracks as an example of research that is closer to a method of 'tinkering' than to a 'magnificent discovery'.

How to cite: Manshausen, P., Tippett, A., Gryspeerdt, E., and Stier, P.: Two steps forward, one step back: four years of progress and setbacks on invisible ship tracks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11357, https://doi.org/10.5194/egusphere-egu25-11357, 2025.

Earth system models are important tools used to understand our climate system and project possible changes in our climate due to anthropogenic and natural forcings. Human errors can occur in the development of Earth System models, i.e., bugs, giving an unphysical representation of our climate. A way to identify and solve bugs is to apply physical concepts. Here, we present an experience that occurred in the development of the ICOsahedral Non-hydrostatic model (ICON) as a kilometer-scale Earth System model, in which physically understanding a bug in the surface energy budget fixed land precipitation. 

In a simulation of ICON, referred to as ICON-bug, precipitation over tropical land continuously decreased across the simulation. This led to a ratio of land-ocean precipitation in the tropics of less than 0.7, which, otherwise, should be more than 0.86. As part of the possible explanations, the surface energy budget over land was targeted as a culprit. This idea relies on the influence of the interaction between soil moisture, surface heat fluxes, and winds to generate circulation favoring precipitation over dry land surfaces (Hohenegger and Stevens 2018). Indeed, the surface energy budget over dry surfaces in the ICON-bug showed an error in sensible heat flux. The sensible heat flux transmitted to the atmosphere was 70% of what was calculated for the surface module. Fixing this error closed the surface energy budget and increased land precipitation over the tropics, leading to a ratio of land-ocean precipitation of 0.94, close to observations. 

How to cite: Segura, H., Hohenegger, C., Schnur, R., and Stevens, B.: Physical understanding of bugs to improve the representation of the climate system  , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12720, https://doi.org/10.5194/egusphere-egu25-12720, 2025.

Whenever you study a phenomenon of mm to a few cm-scale in the laboratory which involves an interface, the question of surface tension arises. Surface tension is due to the fact that molecules prefer to stay with their own kind. Therefore, the creation of an interface between two fluids requires energy, and this influences the dynamics around the interface.

Surface tension can be a blessing: it produces the round shape of rain drops or the nice bubble shapes of colorful liquid in a lava lamp. It allows objects with a higher density to float on a liquid (such as an insect on water, or a silicone plate on sugar syrup). It can generate flow up a capillary.

However, it can also be a curse in the case of thermal convection. Purely thermal convection  develops when a plane layer of fluid is heated from below and cooled from above. The engine of motion is the thermal buoyancy of the fluid. This is what is happening in a planetary mantle on scales of hundreds to thousands kilometers. This is also what is happening in a closed box in the laboratory. But as soon as an interface exists, either between an upper and a lower experimental mantle, or in the case of a free surface at the top of the fluid layer, surface tension effects can become important. For exemple, the variation of surface tension with temperature was responsible for the beautiful honey-comb patterns imaged by Benard (1901) in the first systematic study of thermal convection with a free-surface. Surface tension is also going to act against the initiation of subduction (which acts to break the surface). 

We shall review in this presentation the signatures of surface tension in a convective context, and the different ways to minimize and/or remove the effects of surface tension in convection experiments, such as using miscible liquids, or a layer of experimental « sticky air ».

How to cite: Davaille, A.: Analog studies of mantle convection: the curse of surface tension (or not) ?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15059, https://doi.org/10.5194/egusphere-egu25-15059, 2025.

In hydrology we measure and follow the water. What if there is too much or too little? It happens a lot. As a field hydrologist, I frequently have to determine the location of a measurement, the time to take the measurement, the location to set up a field experiment, or the amount of a tracer to inject to study a hydrological system. However, this is a very bumpy road, as variability is often not in favor of my decisions because the distribution is wider than expected, bimodal instead of unimodal, or the probability of an event is theoretically small, but still an extreme event occurs during our experiment. I will showcase some examples to demonstrate what I mean and what I experienced, as well as how frequently the PhD students or Postdocs have suffered as a result of my decisions or of the unexpected variability: Climatic variability resulted in a winter without snow, just as new sensors were already deployed. Or the winter snowpack was extremely high, preventing any work at high altitudes in the Alps until mid of July, thereby reducing our field season by half. An ecohydological study to observe the effects of drought in a forest with a rainout shelter was ineffective because it occurred during an extremely dry year, making the control just as dry as our drought treatment. The automatic water sampler was set-up to collect stream water samples, but it was washed away four weeks later by the 50-year flood. The calculated amount of artificial tracer was either way too low, because the transit times of the system were much longer than expected, or it was far too high, resulting in colored streams or samples that had to be diluted by a factor of 100 due to much faster transit times Finally, and most expensively, we installed many trenches along forest roads to measure subsurface stormflow but after three years, we abandoned the measurements because we never measured a drop of water coming out of the trenches, as the bedrock permeability was much higher due to many high permeable fissures that prevented the formation of subsurface stormflow.  These experiments or observations failed because of unexpected variability in input, system properties or a lack of technical variability in the equipment. I will reflect on residual risk of failure in fieldwork related to that crux and discus approaches to reduce this risk.

How to cite: Weiler, M.: The crux with variability: too much or too little, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15457, https://doi.org/10.5194/egusphere-egu25-15457, 2025.

The emergence of global km-scale climate models allows us to study Earth's climate and its changes with unprecedented local detail. However, this step change in spatial resolution to grid spacings of 10 km or less also brings new challenges to the numerical methods used in the models, the storage of model output, and the processing of the output data into actionable climate information. The latest versions of the ICON-Sapphire model developed in the frame of the NextGEMS project address these challenges by running on an icosahedral grid while outputting data on the so-called HEALPix grid. Both grids are unstructured grids, which avoids, for example, the issue of longitude convergence. In addition, HEALPix allows data to be stored in a hierarchy of resolutions at different discrete zoom levels, making it easier for users to handle the data.  

The transition from the native 10 km grid to the output grid is made by a simple but very fast nearest-neighbour remapping. An advantage of this simple remapping approach is that the output fields are not distorted, i.e. the atmospheric states in the output remain self-consistent. As HEALPix only provides discrete zoom levels in the setup of the run, it was decided to remap to the closest available resolution of 12 km rather than to the next finer resolution of 6 km. This decision was made to avoid artificially increasing the number of grid points and to avoid creating duplicates through the nearest neighbour remapping.

As a consequence of this approach, wave-like patterns can emerge due to the Moiré effect that can result from the interaction of two grids. We find these patterns when looking at certain derived precipitation extremes, such as the annual maximum daily precipitation, the 10-year return level of hourly precipitation, or the frequency of dry days. At first, we interpreted these patterns as a plotting issue, as the figures might have too low resolution to cope with the high-resolution global plot (aliasing) leading to a Moiré pattern.

However, zooming in on the affected regions and closer examination of the data revealed that the pattern is in fact in the data. Further investigation with synthetic data confirmed the suspicion that the Moiré pattern was indeed caused by the remapping of the native 10 km icosahedral grid to the slightly coarser 12 km HEALPix grid. We hypothesise that precipitation is particularly affected by this issue, as it typically contains many grid cells with zero precipitation, with local clusters of non-zero values at the 15-minutely output interval. Yet, we cannot exclude the possibility that other variables are also affected.

As a consequence, if remapping is required, it is recommended to first remap from the native resolution to a finer resolution grid. As a next step, the conservative nature of the HEALPix hierarchy can be used to compute the coarser level. In this way it is likely to be possible to avoid aliasing and still keep the amount of output data the same.

How to cite: Poschlod, B., Brunner, L., Blanz, B., and Kluft, L.: Output regridding can lead to Moiré pattern in km-scale global climate model data from ICON, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15826, https://doi.org/10.5194/egusphere-egu25-15826, 2025.

Plastic pollution is a global issue, across all environmental compartments. Rivers connect the terrestrial with the marine environment, and they transport various materials, among these plastic pollution. Rivers not only transport plastic, but also accumulate and store it, especially on riverbanks. In fact, plastic deposition and accumulation on riverbanks is a common occurrence. However, our understanding of why plastic is deposited on a certain riverbank is rather limited. Riverbanks along all major Dutch rivers have been monitored for plastic and other litter twice a year by citizen scientists, in some locations since 2018. This provides an extensive dataset on plastic accumulation, and we used these data with the aim of understanding the factors determining plastic concentration/accumulation variability over time and space. We tested multiple riverbank characteristics, such as vegetation, riverbank slope, population density, etc., hypothesized to be related to plastic litter. After having exhausted a long list of auxiliary data and analysis strategies, we found no significant results. Ultimately, we had a close look at ten consistent hotspots of macroplastic litter, along the Meuse, and Waal river. And once again, they seem to have nothing in common. But, there is a pattern, because some riverbanks have consistently very high densities of plastic litter so it does not seem completely random. We have been looking to explain spatial variability, whereas we might have to look at temporal consistency, and we shall not give up our efforts to bring order to this chaos.

How to cite: Hauk, R., Teuling, A. J., van Emmerik, T. H. M., and van der Ploeg, M.: What river plastic hotspots do not have in common, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17676, https://doi.org/10.5194/egusphere-egu25-17676, 2025.

Grande Dixence, the tallest gravity dam in the world, is located in the Swiss Alps on the Dixence River with a catchment area of 4 km² at a towering elevation of 2000m. The lake serves as a collecting point of melt water from 35 glaciers and reaches full capacity by late September, subsequently draining during winter and dropping to lowest levels in April. For a reservoir as large as the Grande Dixence, the variation in hydrological load can be expected to induce changes in crustal stress. The goal of this study was to harness the loading effect of the time-varying level of reservoir load as a source of known stress to investigate the variation in seismic velocity of the bedrock due to changes induced in crustal stress and strain rates. 22 seismic nodes were thus deployed along the banks of the reservoir which were operational from mid-August to mid-September, corresponding to the time period when the lake level reaches its maximum. Of the 22 nodes, 18 were deployed in closely spaced patches of six in order to carry out coherent stacking and to increase the signal-to-noise ratio, besides one group of three nodes and one single node. Measurement quality appears satisfactory: small local earthquakes are recorded well, and the probabilistic power spectral densities (PPSDs) computed for data quality validation evidence the ambient noise levels to be well within the global noise limits. However, the recorded noise is unexpectedly complex and, at periods shorter than 1 second, varies strongly by location. The 0.5--5s (0.2--2 Hz) period band at lakes generally records a diurnally varying noise level, often associated with lake generated microseism. Diurnal variations around 1 second of period are observed in our study as well. The amplitude of ambient noise level around 1 second of period is observed to be highest when the lake level changes, along with the prominent diurnal variation. A similar variation is observed in the seismic velocity variation (dv/v) computed from cross-correlated and auto-correlated ambient noise filtered between 0.5--1 Hz, with dv/v exhibiting a drop with rising lake level. These results provide preliminary evidence for possible change in crustal stress state with changing hydrological load. Future direction of this study consists of analytically modeling the results to quantify the influence of thermobarometric parameters on PPSDs and dv/v, and deconvolve it from the lake induced variations.

How to cite: Uthaman, M., Ermert, L., Ling, A., Junker, J., Ghisleni, C., and Obermann, A.: Temporal variation of ambient noise at the Grande Dixence reservoir recorded by a nodal deployment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17811, https://doi.org/10.5194/egusphere-egu25-17811, 2025.

Rivers play an important role in the global distribution of plastic pollution throughout the geosphere. Quantifying and understanding river plastic pollution is still an emerging field, which has advanced considerably thanks to broad efforts from science, practice, and society. Much progress in this field has been achieved through learning from failures, negative results, and unexpected outcomes. In this presentation we will provide several examples of serendipity and stupidity that has led to new insights, theories, methods, and completely new research lines. We will share what we learned from rivers flowing in the wrong direction, sensors that disappear, equipment blocked by invasive plants, and dealing with suspicious local authorities. Pushing the science sometimes requires an opportunistic approach, embracing surprises and chaos you may face along the way.

How to cite: van Emmerik, T. and the WUR-HWM River Plastic Team: Advancing river plastic research through serendipity and stupidity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18185, https://doi.org/10.5194/egusphere-egu25-18185, 2025.

With the advent of parallel programming in the late 1990s. A port of the than available Max Planck Institutes for Meteorology spectral atmospheric model echam5 to MPI and OpenMP was done. For testing and validation of the hybrid parallelization a coherence algorithm was developed. The implementation has been incorporated into todays NWP and climate model ICON as well. The coherence algoritm consists of several stages: first one MPI rank is running the serial model against an n-task MPI parallelized model. During runtime the state vector is checked for binary-identity. If successfull a m-task MPI version can be compared to an m-task MPI version for high processor counts. The same schema can be used OpenMP parallelization. ONe MPI task runs the model serial using one OpenMP thread and a second MPI task runs k OpenMP threads. Again, the results are compared for binary-identity. As the testing needs to be done automatically, bit-identity is important for testing not necessarily for production.

The tesing revealed plenty of problems during the initial parallelization work of echam5 and showed constant appearing problems in the ICON development phase.

However, far in a couple of century long simulation the bit-identity was just by accident found to be broken: the search of the cause started!

How to cite: Kornblueh, L.: MPI and OpenMP coherence testing and vaildation: the hybris of testing non-deterministic model code, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18400, https://doi.org/10.5194/egusphere-egu25-18400, 2025.

Addressing positive publication bias and clearing out the file drawer has been at the core of the Journal of Trial and Error since its conception. Publishing the trial-and-error components of science is advantageous in numerous ways, as already pointed out in the description of this panel: errors can lead to unexpected insights and warning others about dead ends can prevent wasted time and other resources. Besides those advantages, publishing negative and null results facilitates conducting robust meta-analyses. In addition, predictive machine learning models benefit from training on data from all types of research rather than just data from studies with positive, exciting results; already researchers are reporting that models trained on published data are overly optimistic.

Besides publishing negative and null results as well as methodological failures, the Journal of Trial and Error couples each published study with a reflection article. The purpose of these reflection articles is to have a philosopher, sociologist or domain expert reflect on what exactly went wrong. This helps contextualize the failure, helping to pinpoint the systematic factors at play as well as helping the authors and other scientists to draw lessons from the reported research struggles which can be applied to improve future research.

Publishing failure brings with it some practical challenges: convincing authors to submit manuscripts detailing their trial-and-error; instructing peer reviewers on how to conduct peer review for the types of articles; differentiating between interesting … and uninformative, sloppy science; and determining the best formats to publish various failure-related outcomes in. Authors are still hesitant to publish their research struggles due to reputational concerns and time constraints. In addition, authors often fear that peer reviewers will be more critical of articles describing research failures compared to articles reporting positive results. To counteract this (perceived) tendency of peer reviewers to be more critical of research without positive results, we provide specific instructions to peer reviewers to only assess the quality of the study without taking into account the outcome. This then also ensures that we only publish research that adheres to the standards of the field rather than sloppy science. Whether submitted research provides informative insights is assed by the editor-in-chief and the handling editor.

Finally, we are constantly evaluating and innovating the types of articles we publish. Various types of errors and failures benefit from differing ways of reporting. For example, recently we introduced serendipity anecdotes, a format where scientists can anecdotally describe instances serendipity which occurred during their research. This format allows researchers to focus on the conditions which allowed for the serendipitous discovery rather than the research itself.    

How to cite: Gaillard, S.: Publishing BUGS: Insights from the Journal of Trial and Error, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18981, https://doi.org/10.5194/egusphere-egu25-18981, 2025.

It is common to perform two-dimensional simulations of mantle convection in spherical geometry. These have commonly been performed in axisymmetric geometry, i.e. (r, theta) coordinates, but subsequently we (Hernlund and Tackley, PEPI 2008) proposed using (r, phi) spherical annulus geometry and demonstrated its usefulness for low-viscosity-contrast calculations. 

When performing scaling studies in this geometry, however, strange results that did not match what is expected from Cartesian-geometry calculations were obtained when high-viscosity features (such as slabs) were present. It turns out that this is because the geometrical restriction forces deformation that is not present in 3 dimensions. Specifically, in a 2-D spherical approximation, a downwelling is forced to contract in the plane-perpendicular direction, requiring it to extend in the two in-plane directions. In other words, it is "squeezed" in the plane-perpendicular direction.  If the downwelling has a high viscosity, as a cold slab does, then it resists this forced deformation, sinking much more slowly than in three dimensions, in which it could sink with no deformation. This can cause unrealistic behaviour and scaling relationships for high viscosity contrasts. 

This problem can be solved by subtracting the geometrically-forced deformation ("squeezing") from the strain-rate tensor when calculating the stress tensor. Specifically, components of in-plane and plane-normal strain rate that are required by and proportional to the vertical (radial) velocity are subtracted, a procedure that is here termed "anti-squeeze". It is demonstrated here that this "anti-squeeze" correction results in sinking rates and scaling relationships that are similar to those in 3-D geometry whereas without it, abnormal and physically unrealistic results can be obtained for high viscosity contrasts. This correction has been used for 2-D geometries in the code StagYY (Tackley, PEPI 2008; Hernlund and Tackley, PEPI 2008) since 2010.

How to cite: Tackley, P.:  Adventures in Modelling Mantle Convection in a Two-Dimensional Spherical Annulus and Discovering the Need for "Anti-Squeeze”, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19890, https://doi.org/10.5194/egusphere-egu25-19890, 2025.

The science question: how can we use hydrological process knowledge to understand the timing and magnitude of seasonal streamflow in snow-influenced catchments.

What was known: in general, catchments with colder climates have later and larger seasonal streamflow peaks, because more snow tends to accumulate in colder catchments, and it melts later because the time when melt can occur is later in the year in colder climates. Numerical models with fine space and time resolution were able to resolve these phenomena, but there was no theory which directly linked long term climate to seasonal streamflow.

In 2009 I published a very simple deterministic theory of snow pack evolution. I tested it against snow observations at 6 locations in the western USA and it apparently worked well (although I later discovered that I'd been lucky).

In 2015 I used the snowmelt derived from this deterministic theory to predict timing and magnitude of seasonal streamflow. It did poorly, and revealed untested assumptions in my theory. I tried making the theory slightly more complicated by considering within-catchment variation in climate. This did not help.

In 2016 I created a stochastic version of the theory (a weakness identified in 2015), and then also considered the within-catchment variation in climate. It did better at reproducing measured snow storage, but did not help in understanding seasonal streamflow.

My next step will be to consider all forms of liquid water input, i.e. not just snowmelt but also rainfall.

What survived: I will continue to use the stochastic version of the theory as it is clearly an improvement. I will continue to examine whether within-catchment climate variability is important, but it seems unlikely after two negative results. But whether introducing liquid water input will be sufficient, who can say? I will also try to examine in more detail how it is that the finely-resolved numerical models can do an adequate job, but the theory cannot - it is in this gap that the answer probably lies.  However the models are very complicated, and it is not easy to get a good understanding of exactly what they are doing, even though we know which equations the are implementing.

How to cite: Woods, R.: Some Perfectly Reasonable Ideas that Didn’t Work: Snow Hydrology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20057, https://doi.org/10.5194/egusphere-egu25-20057, 2025.

Climate models are not only numerical representations of scientific understanding but also human-written software, inherently subject to coding errors. While these errors may appear minor, they can have significant and unforeseen effects on the outcomes of complex, coupled models. Despite existing robust testing and documentation practices in many modeling centers, bugs’ broader implications are underexplored in the climate science literature.

We investigate a sea ice bug in the coupled atmosphere-ocean-sea ice model ICON, tracing its origin, effects, and implications. The bug stemmed from an incorrectly set logical flag, which caused the ocean to bypass friction from sea ice, leading to unrealistic surface velocities, especially in the presence of ocean eddies. We introduce a concise and visual approach to communicating bugs and conceptualize this case as part of a novel class of resolution-dependent bugs - long-standing bugs that emerge during the transition to high-resolution models, where kilometer-scale features are resolved.

By documenting this case, we highlight the broader relevance of addressing bugs and advocate for universal adoption of transparent bug documentation practices. This documentation complements the robust workflows already employed by many modeling centers and ensures lessons from individual cases benefit the wider climate modeling community.

How to cite: Gärtner, J., Proske, U., Brüggemann, N., Gutjahr, O., Haak, H., Putrasahan, D., and Wieners, K.-H.: A case for open communication of bugs in climate models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20866, https://doi.org/10.5194/egusphere-egu25-20866, 2025.

The Kefalonia Transform Fault (KTF), a 150-km long tectonic boundary connecting the Adriatic and Aegean plates, is characterized by slab tearing and significant seismic activity. Earthquake sequences in the region are clustered along the fault, yet the local earthquake catalog records remain sparse.

From April 23 to June 22, 2024, we conducted a two-month Distributed Acoustic Sensing (DAS) experiment on Kefalonia Island using a 15 km dark fiber network—7 km along a roadway and 8 km across the seafloor. By applying the STA/LTA method to marine DAS data, we detected ~10,000 high-frequency (5–20 Hz) events. Event clustering using pairwise correlations, combined with the local earthquake catalog, revealed six spatially distinct seismic clusters, each associated with specific fault segments.

Using these clusters as templates, we developed a new template matching workflow to expand the earthquake catalog to ~20,000 events, significantly increasing the detection of small-magnitude earthquakes. Each cluster highlights seismic activity on distinct fault segments and reveals foreshock and aftershock sequences for ML >2.5 events.

The refined catalog, with 100 times more events than the original local catalog, provides unprecedented temporal and spatial resolution of seismicity along the KTF. These results offer new insights into fault segment interactions and the processes of stress accumulation and release in the KTF system.

How to cite: Wang, Y. and Fichtner, A.: Mapping fault dynamics: Very high seismicity detected along the Kefalonia Transform Faul with DAS and template matching, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2886, https://doi.org/10.5194/egusphere-egu25-2886, 2025.

Understanding volcanic processes is fundamental for anticipating impacts of eruptive activity on human activities and environment. Deformation and seismicity often precede and accompany volcanic eruptions. Models of magma emplacement and ground deformation associated with eruptions are obtained from GNSS and InSAR observations and associated seismic source mechanisms from seismic observations. While satellite sensing techniques benefit from a large spatial coverage but coarse temporal resolution and accuracy (mm range), seismometer networks acquire dense temporal data but are sparsely distributed and suffer from spatial aliasing. Dynamic models of sources during the event at the minute scale are challenging to obtain because they are in most cases too small or too slow to be observed accurately with conventional instrumentation. Here, we demonstrate that distributed fibre optic sensing using phase optical time domain reflectometry (Φ-OTDR) allows us to retrieve dynamic and static deformation processes associated to diking events prior to volcanic eruptions, at the minute time scale. Since November 2023, we are continuously monitoring an existing telecom fibre optic cable with a conventional iDAS interrogator, set-up on western Reykjanes Peninsula, SW Iceland. Reykjanes Peninsula is the onshore expression of the Atlantic mid-oceanic ridge, where a series of magmatic intrusions and eruptions have occurred since 2020. The used cable spans across locations from a large inflation/deflation area near dyke outbreaks at its eastern end, to a remote area where little signatures from eruptions are observed at its western end. In-situ down-sampled strain-rate data from 1000 Hz to 200 Hz is transferred continuously via internet to our computing center at the GFZ in Germany. We further down-sample data to 2 minutes and perform time integration in order to analyse long period strain signals both spatially and temporally. Here we present resulting distributed dynamic strain observations and their source inversions associated with a series of several eruptions and intrusions (18.12.2023 22:17; 14.01.2024 07:57; 08.02.2024 06:03; 16.03.2024 23:14; 22.08.2024 21:26; 20.11.2024 23:14 – all times UTM). Our inversions comprise a Mogi source and an Okada model, and we test several inversion methods. For each recorded eruption, we invert the distributed spatial strain taken every 2 minutes, allowing us to follow magma progression prior to each eruption with time. Inverted dimensions and dyke locations match rather well with observed eruption locations. We also analyse links between seismic velocity changes from distributed dynamic strain ambient noise records and the eruptions. These results show that fibre optic distributed sensing is capable of simultaneous seismological and geodetic observations in a volcanic context opening the path for a better understanding and potentially improved real-time monitoring of volcanic processes.

How to cite: Jousset, P., Gudnason, E. Á., Currenti, G., Wollin, C., Holstein, L., Maaß, R., Diaz-Meza, S., Hersir, G. P., Sigurðarson, D., and Krawczyk, C.: Sources of Ultra-Slow Dynamic and Static Deformation of dyke eruptive events revealed by telecom fibre optic cable sensing on western Reykjanes Peninsula, SW Iceland., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5200, https://doi.org/10.5194/egusphere-egu25-5200, 2025.

Distributed Acoustic Sensing (DAS) is a fiber-optic sensing technology that transforms optical fiber telecommunication cables into arrays of thousands of broadband strain meters. The emergence of DAS has spurred significant advancements across various scientific domains, including geophysics, seismology, hydrology, and engineering. Modern DAS systems offer spatial resolutions of several meters, ranges extending tens of kilometers, sensitivities below 1 nε, and sampling rates of up to several kHz.

Focusing on systems utilizing chirped pulse distributed acoustic sensing (as implemented by High-Fidelity Distributed Acoustic Sensing (HDAS) from Aragon Photonics Labs), these techniques demonstrate enhanced performance, achieving an optimal balance between range and sensitivity, particularly at low frequencies. In seismology, these capabilities enable the high-resolution detection of seismic waves originating from events such as local and teleseismic earthquakes, as well as micro-seismic vibrations induced by trains or vehicles.

DAS has proven effective in railway monitoring, enabling vehicle tracking and rail condition assessments. Its spatial and temporal density makes it especially promising for monitoring and control in high-speed railway systems. This work applies methods adapted from array seismology to visualize seismic surface waves generated by trains and other vehicles near a trackside dark fiber. These relatively simple methodologies efficiently extract and characterize surface waves propagating along the railway superstructure.

The DAS data collected from trackside fibers provide substantial information about terrain features and the condition of railroad superstructures. These findings highlight the potential of DAS systems for monitoring seismic surface waves and superstructure conditions using pre-installed fibers. Moreover, the evolution of this information over time can offer valuable insights for infrastructure owners, particularly in critical scenarios such as high-speed railway systems. Additionally, the local dispersion relation for surface waves reveals further details about the superstructure, which could support preventive maintenance efforts.

How to cite: Preciado-Garbayo, J., Canudo, J., Gonzalez-Herraez, M., F. Martins, H., Schaedler, M., Gaite-Castrillo, B., Bravo-Monge, J. B., de Maria, I., and Rodriguez-Plaza, M.: HDAS (High-Fidelity Distributed Acoustic Sensing) as a seismic surface wave monitoring tool along trackside dark fibers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5267, https://doi.org/10.5194/egusphere-egu25-5267, 2025.

Geothermal productivity strongly depends on reservoir performance, which is regularly monitored. This work presents the results from Distributed Dynamic Strain Sensing (DDSS or DAS) measurements during the restart of injection and production in deep geothermal wells. This technology's high spatiotemporal resolution enables monitoring relative strain and temperature changes along the entire sensing cable. We monitored 3.7 km of a producer and approximately 4.1 km of an injector. Both cables were installed post-borehole completion and reached up to 1 km into the reservoir. Distributed sensing was achieved using a commercial DDSS acquisition system sampling the boreholes at 1 m spatial interval and 2000 Hz.

Here, we focus on the low-frequency subsurface dynamics captured during the restart phase. We extracted the low-frequency content (<0.1 Hz) by applying a cascading Finite Impulse Response (FIR) filter and a large decimation factor. As fiber optic strain measurements are sensitive to strain and temperature changes along the fiber optic cable, the low-frequency signal is influenced by subtle temperature changes induced by fluid motion.

The results provide unprecedented accuracy in determining deep geothermal inflow zones because we can detect flow velocities magnitudes lower than conventional flow meter measurements. Further, the activation time of the inflow from different depths can be distinguished. Fluid-flow velocity, strain, and their respective temporal derivatives can be used as a proxy for the heat contribution of different depths and the associated inflow zones.

These findings are part of the GFK-Monitor project (https://gfk-monitor.de/en/), demonstrating the ability of DDSS to detect and interpret complex reservoir processes in deep geothermal systems. This research advances downhole monitoring technologies, offers an improved understanding of subsurface processes, and informs strategies to optimize geothermal energy production.

How to cite: Hart, J., Wollin, C., Andy, A., Ledig, T., Reinsch, T., and Krawczyk, C.: Low Frequencies of Distributed Dynamic Strain Sensing enable unprecedented profiling of deep geothermal fluid production and injection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5864, https://doi.org/10.5194/egusphere-egu25-5864, 2025.

Distributed Acoustic Sensing (DAS) transforms optical fibers into extensive arrays of sensing points, making it particularly well-suited for seismic array processing techniques. Compared to arrays of seismometers, DAS has the potential for significantly higher spatial density of measurement points. A limitation of DAS is, however, its directional sensitivity, where the response of individual sensing points is influenced by the orientation of the fiber optic cable. In this study, we use a fiber optic cable that was installed by design with multiple cable orientations to record and analyze the seismic wavefield in three dimensions. This work investigates the capabilities of the DAS station, installed in the Munich region (Germany), for seismic monitoring of a nearby geothermal field. The DAS station consists of two controlled fiber-optic cable sections: a near-surface loop providing various azimuthal strain-rate measurements, which is extended into a 250-metre-deep vertical monitoring well for vertical sensing. The setup is complemented on the surface by a 3C-broadband seismometer for the validation of the results. In this study, we describe the design, installation and characterization of the DAS station, as well as the seismic event processing workflow. We demonstrate the ability of the 3D-DAS to analyze wavefield directionality, including back-azimuth, incidence and slowness components. In addition, we highlight the role of the vertical borehole in converting DAS strain-rate data into acceleration, which allows estimating source characteristics such as moment magnitude and stress drop estimation. These capabilities are demonstrated for a local seismic event relevant to the monitoring objective. Quality control procedures confirm the consistency and reliability of the DAS station measurements in comparison to 3C seismometer results. Extending the analysis to a broader event catalogue reveals spatial resolution limitations inherent to the station's array geometry. These results highlight the potential and challenges of using DAS for seismic monitoring in geothermal contexts.

How to cite: Azzola, J. and Gaucher, E.: Three-dimensional Distributed Acoustic Sensing to monitor geothermal fields in Munich, Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6036, https://doi.org/10.5194/egusphere-egu25-6036, 2025.

Long-term environmental monitoring of the deep ocean environment is crucial for better understanding the feedback processes between the oceans and Earth’s climate in the face of global warming. However, obtaining in-situ observations from the deep seafloor is difficult and costly. Use of laser reflectometry in optical fibers using existing submarine telecommunication cables can help bridge this knowledge gap. We performed distributed fiber-optic sensing using the BOTDR (Brillouin Optical Time Domain Reflectometry) technique, which is sensitive to mechanical strain (elongation/ shortening) and temperature changes, on a network of commercially operating telecom cables connecting the islands of the Guadeloupe archipelago in water depths of 10 - 700 m. Monitoring at regular 6 month intervals over the past 2.5 years reveals a seasonally adjusted two-year temperature change (delta T) of about +1.5°C  between 2022 and 2024 on the shallow carbonate platform (10 - 40 m water depth) south of Grande-Terre (Saint François), Guadeloupe. These sea-floor measurements are corroborated by satellite observations of the Sea-Surface-Temperature (SST) during the past three years, which document an identical temperature increase at the sea surface, in the same location (offshore Saint François). CTD measurements performed in 5 locations along the cables reveal well-mixed waters and no temperature stratification. A smaller temperature increase (0.2 - 1.0°C) is observed in deeper waters (300 - 700 m) between the islands over the same period (2022 - 2024). A new measurement campaign is planned in mid-March 2025 with BOTDR on the submarine cables and XBT (eXpendable Bathy Thermograph)  measurements at sea. Together with an additional field campaign performed (at a 3-month interval) in Sept. 2024 (during the maximum annual water temperature) the new campaign (performed during the minimum annual temperature and again at a 3-month interval) will fully constrain the seasonal variations in water temperature. These results can open the path for widespread use of submarine cables for long-term environmental monitoring of the seafloor.

How to cite: Gutscher, M.-A., Cappelli, G., Quetel, L., Philippon, M., LeBrun, J.-F., Nativelle, C., Quillin, J.-G., and Autret, E.: Monitoring long-term bottom water temperature changes using fiber-optic sensing in submarine telecommunication cables, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6368, https://doi.org/10.5194/egusphere-egu25-6368, 2025.

Understanding the full wave field is imperative for seismic data analysis, as the different components induce errors in the sensors. Recent development of rotational seismometers allows for detailed measurements of the wave field gradients. Providing additional information that was previously unattained. However, it is well-known from navigation solutions that rotational data requires proper processing to be physically meaningful. In this study, we focus on investigating and quantifying two errors affecting recording of rotations: 1) misorientation of sensor to local system called misorientation of rotations and 2) changing projection of the Earth’s spin in the recordings - Earth spin leakage. Using 6-component datasets, including 3C translation and 3C rotation, from near-field events at the Kīlauea Caldera in Hawai‘i and the Mw 7.4 Hualien event on 2024-04-02, we find that the Earth spin leakage is negligible, while the misorientation of the rotations increases with ground motion amplitude, potentially becoming significant for large earthquakes in the near-field. While these errors do not significantly affect acceleration corrections in our dataset, they may be relevant for high-amplitudes or in highly sensitive applications. This work offers the first quantification of these errors in seismology and provides guidance for assessing the need for corrections in future studies.

How to cite: Rossi, Y., Bernauer, F., Lin, C.-J., Guattari, F., and Pinot, B.: Quantifying Rotation-Induced Errors in Near-Field Seismic Recordings, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6411, https://doi.org/10.5194/egusphere-egu25-6411, 2025.

We report preliminary results from a first-of-its-kind academic wireline deployment of a joint DAS-seismometer in an abandoned well. We designed a custom-made 1.5 km-long cable that includes both optical fibers and copper wires, and attached a seismometer at its bottom. The apparatus was lowered to a depth of approximately 750m in a vertical borehole, abandoned and plugged about 40 years ago. We also deployed broad-band seismometers near the wellhead. We show data from an active vertical seismic profiling (VSP) experiment in which we also had surface geophones, low-frequency DAS, and earthquake monitoring from the nearby Dead Sea Fault, including comparisons to surface stations. Coupling is obtained from the fluid filling the borehole below a depth of approximately 380 m. Despite very strong tube waves in the upper sections of the well and complicated phase behavior in the deeper parts, we were able to obtain useful data.

How to cite: Lellouch, A. and Wetzler, N.: Joint wireline DAS-seismometer deployment in an abandoned well: lessons and insights, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6458, https://doi.org/10.5194/egusphere-egu25-6458, 2025.

Seismic surveys commonly use sensor arrays to record wave signals, with methods like noise cross-correlation function and spatial autocorrelation to analyze wave propagation features. However, these multi-station methods require many sensors, leading to high costs and deployment complexities. In contrast, single-station methods utilize constraints among different seismic components at a single location to extract dispersion curves and invert subsurface structures. However, these methods face theoretical limitations, including a lack of a generalized model to determine which components should be used, and problems related to the non-unique inversion of dispersion curves. To address these limitations, we propose the single-point interference method, using a six-component seismometer including an interferometric fiber-optic gyroscope to record rotational motions. This method models wave propagation as a two-port network system, and uses a transfer function to describe wave interference at the measurement point, where specific input-output pairs correspond to distinct subsurface structures. Assuming a single-source plane wave, when the transverse acceleration component serves as the input and the vertical rotation component as the output, the transfer function defines local parameters of Love waves: its amplitude represents the local phase velocity, and its phase represents the back azimuth of the incident wave. By adjusting input-output combinations, this method obtains the subsurface velocity structures for different wave types. For example, with the vertical acceleration component as the input and the transverse rotation component as the output, the local phase velocity of Rayleigh waves can be derived. This method provides an opportunity to cross-validate the inversion results. The term single-point emphasizes the locality of the method, rather than limiting its application to just one measurement point. By applying a six-component seismometer to obtain inversion results at multiple points along a line and interpolating these results, a continuous subsurface profile can be constructed. Expanding the line to a grid of points enables 3-D modeling of the subsurface structure within the grid area. Experiments demonstrate that this method can estimate velocity structures with a single-station seismometer, reducing the costs and deployment complexities of multi-station methods. Experimental seismic records and corresponding local velocity structure estimations will be presented to demonstrate this method’s advantages in seismic surveys.

How to cite: Zhou, Z., Chen, Y., and Li, Z.: A Single-Point Interference Method for Subsurface Structure Estimation Using a Six-Component Seismometer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6645, https://doi.org/10.5194/egusphere-egu25-6645, 2025.

Ground motion signals acquired through Distributed Acoustic Sensing (DAS) provide unprecedented spatial resolution over kilometric distances, particularly in environments traditionally difficult to reach, such as the ocean bottom. Although this is a substantial upside on its own, DAS experiments come with data storage costs that translate into processing costs that have to be addressed. Whether we choose to adapt former tools or develop new tools, we often leverage computational infrastructures that may not be readily available or easily accessible to every researcher, and thus there is still a need for tools that can reliably run on the average workstation.

This presentation introduces a multiscale, kurtosis-based picking algorithm designed for detection on arbitrary-length traces. Using this newly developed picker, we propose pick scatter maps as a novel method for visualizing DAS data. These maps combine individual picks from traces to reveal patterns and facilitate the interpretation of signals recorded by DAS. It is infeasible to reliably plot a full-day 2D strain map due to resolution and memory issues, but it is possible to plot a full-day scatter map where overlapping points (picks) appear to form lines that correspond to individual signals, with their corresponding apparent velocities. Examples include extremely vertical lines (large apparent velocities) spanning the whole cable, which are expected for earthquakes, or localised lines with a visible slope (lower apparent velocities), which will usually correspond to vehicles. Scatter maps from some environments may feature signals of interest to other fields of research, such as marine life on ocean bottom cables.

Scatter maps provide a way to highlight specific segments within month-long strain recordings. In the particular case of earthquakes, curve fitting in pick clusters produced by P- and S-wave arrivals lets us obtain phase picks by keeping those close enough to the fitting curve, discarding the rest to reduce delayed or consecutive triggers. These phase picks can be used for location purposes, sometimes combined with traditional stations to ensure proper azimuthal coverage. For other types of signals, each specific application will determine how its corresponding picks can be used. Speed tracking or near-cable activity monitoring are examples of such applications.

How to cite: Latorre, H., Ventosa, S., Ugalde, A., Cubas Armas, M., Rodríguez, T., Villaseñor, A., Martins, H. F., Vidal-Moreno, P., Bozzi, E., Bartolomé, R., and Ranero, C. R.: Efficient signal detection and visualisation for fibre-optic seismology: exploring multiple environments and applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6920, https://doi.org/10.5194/egusphere-egu25-6920, 2025.

DAS (Distributed Acoustic Sensing) systems have become widely used tools in Geophysics and Seismology. While most DAS users are familiar with the basic principles of the technology and the typical performance of commercially available interrogators, there is often a limited understanding of the fundamental performance limits of DAS systems.

In this talk, I will review the foundational principles of DAS and will provide insight into the physical limitations of conventional technology. By exploring these constraints and strategies to overcome them, I will introduce two innovative systems currently under development in my lab that demonstrate significantly different capabilities compared to standard DAS systems.

The first system achieves centimeter-scale gauge lengths over a sensing range of approximately 1 km. The second system, while offering conventional performance (meter-scale resolution over tens of kilometers), eliminates 1/f instrumental noise entirely, making it well-suited for very long-term monitoring of processes with timescales ranging from days to months.

How to cite: Gonzalez-Herraez, M.: Beyond conventional DAS systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6942, https://doi.org/10.5194/egusphere-egu25-6942, 2025.

The Source Scanning Algorithm (SSA) was introduced (Kao and Shan, 2004; 2007) as an automated approach to detect and locate seismic events without the need for phase picking. This algorithm works by stacking observed waveforms based on theoretical arrival times derived from a velocity model, under the assumption of a potential point source. It conducts a grid search across multiple candidate source locations, identifying those that produce the highest stacking values (i.e., brightness) as the most probable hypocenters. Extensive use of this approach and its subsequent variations to continuous data streams has shown success even under challenging station geometries, noisy data, and complex seismic source conditions. Recently, Distributed Acoustic Sensing (DAS) has emerged as a powerful tool for seismic monitoring, employing standard fiber‐optic cables as dense arrays of virtual sensors. By measuring strain rate at meter‐scale intervals, DAS can offer continuous seismic coverage over large distances, including remote or hard‐to‐reach areas such as offshore regions, where deploying conventional seismometers can be impractical. With suitable processing, these strain rate measurements can be converted into particle motion, allowing the application of standard seismological methods. However, cable geometry and orientation may introduce azimuthal ambiguities, complicating the use of these methods. In this study, we evaluate the performance of the SSA for locating seismic events using exclusively DAS data. By analyzing both synthetic and real offshore datasets from diverse global regions (e.g., Chile, Greece), we systematically assess the effectiveness of SSA applied to continuous DAS measurements in accurately determining the locations of seismic events. This investigation raises new questions regarding the computational challenges involved—particularly the large volume of DAS data, the selection of appropriate characteristic functions, the integration of DAS data with local seismic networks, the velocity models used for travel-time calculations, and the necessary time corrections. Our results show that SSA can quickly and consistently detect seismic occurrences in DAS data without explicit phase picking, thereby offering a viable method for continuous monitoring even in the presence of the complicated geometries related with DAS installations. This study demonstrates the feasibility of combining DAS and SSA for high-resolution earthquake detection and highlights the potential for expanding its use in real-time seismic networks worldwide.

This research work was supported by the "SUBMarine cablEs for ReSearch and Exploration - SUBMERSE" EU-funded project (HORIZON-INFRA-2022-TECH-01, Grant Agreement No. 101095055).

How to cite: Fountoulakis, I. and Evangelidis, C.: Towards Rapid and Accurate Seismic Event Detection and Localization Using DAS Data: Exploring the Source-Scanning Algorithm Method., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7242, https://doi.org/10.5194/egusphere-egu25-7242, 2025.

Extreme climate events and geological hazards have underscored the urgency of advancing multi-scale seismic imaging to accurately characterize the Earth system. Despite the widespread application of seismic surface wave methods based on dispersion analysis, which have been used for S-wave velocity imaging across scales from the critical zone (meter-scale) to the crust and mantle (kilometer-scale), high-resolution integrated imaging across different scales remains underexplored due to limitations in observational configurations and inversion techniques. In this study, we fully exploit the potetial of Distributed Acoustic Sensing (DAS) for cross-scale ultra-high-density observations and develop a compatible and pragmatic multi-scale surface wave imaging strategy based on Voronoi tessellation with grid cells adapted to dispersion data sensitivity kernels. A 2D dispersion curve inversion kernel and multigrid constrained update strategy are also integrated to this strategy to improve inversion accuracy and computational efficiency. More importantly, the strategy allows for the quantitative assessment of inversion result uncertainties and resolution, enhancing model reliability and guiding interpretation. The efficacy of this framework is validated through synthetic tests and applied to a seabed DAS field study in Monterey Bay, California. We demonstrate a refined S-wave velocity model with higher resolution and deeper illumination depth, offering new insights into the fault system and paleogeographic history of the region, especially paleochannel evolution. The results contribute to reconstructing past geological processes and understanding their influence on contemporary geohazards and subsurface dynamics. Our findings emphasize the necessity for multiscale imaging in large-scale geophysical studies.

How to cite: Guan, J., Cheng, F., and Xia, J.: Multi-Scale Surface-Wave Imaging Using Distributed Acoustic Sensing and Voronoi Tessellation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7809, https://doi.org/10.5194/egusphere-egu25-7809, 2025.

One of the major challenges in earthquake localization in the Aeolian Archipelago, particularly on Vulcano Island, arises from the significant coverage gap caused by the lack of seismic stations installed in marine areas, which are terrestrial zones where installation is challenging or impossible. Deploying stations offshore is logistically and financially demanding, limiting the spatial resolution of seismic monitoring networks in the region.

To address this gap, we explored the potential of using submarine fiber optic cables, traditionally deployed for telecommunication purposes, as seismic sensors. These cables were interrogated by a Distributed Acoustic Sensing (DAS) device, effectively creating a virtual seismic network in previously inaccessible areas. This innovative approach allows the DAS-derived signals to emulate conventional seismic recordings, enabling their integration into existing monitoring frameworks.

We processed the DAS data to generate seismic signals comparable to those obtained from traditional seismic stations. These signals were converted into SUDS files, which were subsequently analyzed using INGV-OE localization software, Seismpicker. Combining these DAS-derived seismic records with data from the permanent monitoring network, we re-evaluated the localization of several seismic events that occurred on Vulcano Island in January-February 2022. The inclusion of DAS data significantly enhanced the accuracy of hypocenter estimations, demonstrating its potential to fill critical observational gaps in the region.

Future work will focus on refining the DAS signal processing pipeline to improve the fidelity and reliability of seismic waveforms. Additionally, expanding the use of DAS technology across other submarine cables in the Aeolian Archipelago could further enhance seismic monitoring capabilities, providing a cost-effective solution to address the limitations of traditional networks. This approach underscores the transformative potential of leveraging existing telecommunication infrastructure to advance geophysical research and hazard mitigation efforts.

How to cite: Aliotta, M. A., Currenti, G., Prestifilippo, M., and Ferrari, F.: Improving earthquake localization in Vulcano island through DAS technology , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8248, https://doi.org/10.5194/egusphere-egu25-8248, 2025.

Distributed fiber-optic sensing is becoming increasingly topical because of its potential for recording a wide range of frequencies, at a high spatial sampling and over long distances. Distributed Acoustic Sensing (DAS) is one example of such an emerging technology. The lowest frequencies observed on marine DAS data are complex temperature signals at tidal periods (Ide et al, 2021). This presentation shows tidal period signals recorded on four DAS interrogators connecting Ny-Ålesund and Longyearbyen, Norway.

DAS records the phase changes in Rayleigh backscattered light from inherent impurities along the fiber to detect changes in the optical cable length. DAS data contain contributions from both temperature variation and elastic deformation. Interrogators normally record this as time-differentiated phase-change data which is linearly related to strain rate. At high frequencies (> 0.01 Hz, Sladen et al., 2019) the strain rate is dominated by elastic deformation, while at tidal frequencies (< 0.01 Hz) it is believed to primarily be generated from slow temperature variation, e.g., from internal tides (Williams et al., 2023). However, there are examples of strain measurements of tides (Roeloffs, 2010) and DAS signals that are proportional to the barotropic tidal pressure (Williams et al., 2023). Therefore, interplay between temperature and strain effects generated by tidal waves is not yet fully understood and remains a challenge.

During a field test in the summer of 2022 four interrogator units were installed in Svalbard, two in Ny-Ålesund and two in Longyearbyen. These recorded DAS data simultaneously for almost one month and covered two 260 km long fiber cables (see Rørstadbotnen et al., 2023, for more information). After the conclusion of the four-interrogator experiment, one of the interrogators was left recording in Ny-Ålesund. From this data, we have studied tidal period signals at selected channels over longer time periods (>14 days) and along the fibers for shorter periods (~4 days).

The results of the analyses will be presented, and we will demonstrate how the tidal signal varies along, and between, the two fiber cables. Additionally, the long-term signals will be compared to the water level data from Ny-Ålesund to validate the long-term trend in the data.

References:

Ide, S., Araki, E., Matsumoto, H., 2021, Very broadband strain-rate measurements along a submarine fiber-optic cable off Cape Muroto, Nankai subduction zone, Japan, Earth, Planets and Space, DOI: https://doi.org/10.1186/s40623-021-01385-5

Sladen, A., et al., 2019, Distributed sensing of earthquakes and ocean-solid Earth interactions on seafloor telecom cables, Nature Communications, https://doi.org/10.1038/s41467-019-13793-z

Williams, E. F., et al., 2023, Fiber-optic observations of internal waves and tides, Journal of Geophysical Research: Oceans, https://doi.org/10.1029/2023JC019980

Roeloffs, E., 2010, Tidal calibration of Plate Boundary Observatory borehole strainmeters: Roles of vertical and shear coupling. Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2009JB006407

Rørstadbotnen, R. A., et al., 2023. Simultaneous tracking of multiple whales using two fiber-optic cables in the arctic, Frontiers in Marine Science, https://doi.org/10.3389/fmars.2023.1130898

How to cite: Rørstadbotnen, R. A. and Landrø, M.: Tidal period signals observed on DAS data from two 260 km fiber cables in Svalbard, Norway, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8409, https://doi.org/10.5194/egusphere-egu25-8409, 2025.

Distributed acoustic sensing (DAS) on submarine fibre optic cables complements seismic networks by providing real-time information from the seabed over long range. The seismology community has demonstrated impressive new capabilities and processing techniques with DAS seismic recordings, and collaborations have been established to systematically deploy the instrumentation. We focus on the integration with existing earthquake monitoring systems and show that edge computing and streaming from the DAS interrogation of submarine cables enhances rapid earthquake location when processed in conjunction with data from terrestrial seismometer stations.

The widely used SeedLink protocol enables data transfer in near real-time and has support for many acquisition and analysis systems. The protocol is optimized for applications in seismology and for use with standard seismometer sensors. Recently, SeedLink streaming for DAS systems has been developed in the SeisComP framework. Since the DAS instrument data rate can be orders of magnitude larger than a typical seismometer, we introduce the DAS virtual station concept to expose pre-processed data in a sampling compatible with that of traditional seismic networks. To benefit from the dense spatial sampling along the cable, we implement array processing as edge computing on the DAS instrument server. This enables noise suppression and wave analysis techniques that would not be possible if a sparse subset of the DAS single component channels were streamed directly. For example, a 150 km cable can be exposed as 15 virtual stations spaced 10 km apart, with each station representing the denoised and decomposed landward and seaward propagating wave phases in different velocity intervals. The associated data rate will be comparable to 15 seismometers and suitable for streaming even over low bandwidth connections from the cable landing station.

Streaming of DAS virtual stations via SeedLink seamlessly facilitates the use of any analysis tools and workflows using existing standard seismological formats and services; this also facilitates data handling and curation in seismological data centres. The integration with real-time data from seismic network stations of similar spatial sampling can also be conducted within standard frameworks. To understand the impact of the integration, we consider using the virtual stations in near real-time processing as a complement for rapid earthquake location. The location accuracy enhancement from incorporating the sparsely sampled virtual stations along the submarine cable can be significant due to the extended azimuthal coverage of stations in the ocean. For early detection and location this achieves the most important benefit of the DAS cable sensing, and with the noise suppression possible from the dense sampling operating as edge computing. Further details might be required in post event analysis, in which case the full DAS dataset can be transferred.

How to cite: Morten, J. P., Heinloo, A., Evangelidis, C., Strollo, A., and Tilmann, F.: Enhancing rapid earthquake location by integrating streaming DAS virtual stations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9133, https://doi.org/10.5194/egusphere-egu25-9133, 2025.

Distributed Acoustic Sensing (DAS) has become very popular for recording seismic waves in recent years as it provides dense spatial sampling along an optical fiber with only one single interrogator unit (IU) needed for thousands of channels. Fibers can be several tens of kilometers long and in some applications so-called dark-fibers can be used, which were deployed for telecommunication purposes, but are currently not in use. This greatly reduces the necessary effort for field deployment.
Most applications rely only on the phase information in the recorded data. Use cases which rely on amplitude are less frequent, though DAS is very attractive in studies in which strain needs to be measured directly. While the IU is calibrated to record ’fiber strain’ or strain-rate, the properties of the cable and its coupling to the rock control the ’strain transfer rate’ and hence how much of ’rock strain’ is represented in the recorded signal. The ’strain transfer rate’ can be significantly smaller than 1, which also goes along with a reduction of signal to noise ratio, as instrumental noise levels do not depend on the coupling.
At Black Forest Observatory (BFO) we cemented eight optical fibers into a 250 m long groove in the concrete floor of the gallery. The fibers are made up of a 9 µm thick core, 116 µm thick cladding, 125 µm thick coating, and a 650 µm thick tight buffer adding up to a total thickness of 0.9 mm. This type of installation shall provide the best achievable coupling of the fiber to the rock.
We use this installation as a test-bed for DAS IUs and show results from a huddle test run in spring 2024 with four types of IUs. The ’strain transfer rate’ becomes close to 1 for all IUs, making ’fiber strain’ almost equal ’rock strain’ as expected. A comparison with data recorded by the Invar-wire strainmeter at BFO and the intercomparison of IUs lets us further characterize the signal quality and coherent and incoherent background noise.

How to cite: Forbriger, T., Münch, F., Widmer-Schnidrig, R., Hillmann, L., Xiao, H., Rietbrock, A., Rodriguez Tribaldos, V., Strollo, A., and Jousset, P.: Cemented fibers as a test-bed for distributed acoustic sensing (DAS), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9229, https://doi.org/10.5194/egusphere-egu25-9229, 2025.

The Istanbul Natural Gas Distribution Company has started monitoring seismic activity within the Sea of Marmara using a fiber-optic (F/O) cable integrated with a Distributed Acoustic Sensing (DAS) system in order to mitigate secondary disasters that may occur after earthquakes and to protect critical infrastructures, such as pipelines. The monitored F/O cable, originally designed for telecommunications purposes, extends over a length of 60 kilometers beneath the Sea of Marmara. In 2022 October, this cable is integrated with a DAS system through an interrogator unit, installed at Tavşantepe Metro Station. The system consists of an analyzer that allows detection up to 40 kilometers, operates with a spatial channel spacing of 10 meters, in total 3910 channels, and a sampling rate of 200 Hz, enabling high-resolution seismic data acquisition. The cable’s route follows several critical regions: it enters the Sea of Marmara, traverses Büyükada, runs behind the Princes' Islands parallel to the Marmara Fault, intersects the fault at multiple locations, and ultimately terminates on land at Ambarlı. This strategic placement provides extensive coverage for monitoring seismic activity along this geologically active region.

Since the beginning of 2023, more than 500 earthquakes, with magnitudes ranging from 0.7 to 7.8, have been recorded using the F/O cable. Our analysis reveals that the quality of recorded seismic signals is strongly influenced by two factors: the incidence angle of wave on the cable and the cable's coupling with the ground. Poor coupling reduces the energy transfer from the ground to the cable, leading to weaker or distorted signals, while unfavorable incidence angles of wave, affect the strain response detected by the DAS system. These findings highlight the importance of optimizing cable placement and ensuring effective coupling for reliable seismic monitoring.

The developed algorithms have enabled the real-time automatic detection of earthquakes occurring within and around the Sea of Marmara using the F/O cable, and the initial results have been promising. The first real-time detection is accomplished for the M3.9 Çanakkale earthquake occurred on 19 November 2024 at 07:46:15 UTC. The F/O cable detects the earthquake 33 seconds following its occurrence, and the system sent an automatic detection notification approximately 1 second later after detection.

As part of our project, at the beginning of January 2025, a vessel-based survey is conducted to determine the submarine position of the F/O cable passing beneath the Sea of Marmara. This study contributes to improving the application of DAS in submarine seismic observation and highlights potential challenges in data acquisition from F/O cables.

How to cite: Coşkun, Z., Koç, B., Özgür, H. G., Aslan, K., Özkan, E., Şahin, R. C., Erkorkmaz, T., Tunç, S., Pınar, A., and Özener, H.: Observations of Seismic Activity Using Fiber Optic DAS in the Sea of Marmara , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10070, https://doi.org/10.5194/egusphere-egu25-10070, 2025.

The Swedish National Seismic Network (SNSN) in collaboration with the Swedish Transport Administration (STA) and Luleå University of Technology is currently studying how to incorporate DAS data from the railway system into the processing at the SNSN. A test data set consisting of both DAS and nodal data was collected in August 2024 along 15 km of railway passing the Kirunavaara underground iron mine in northernmost Sweden. During a three day period, the cable and instruments recorded blasts and induced events from the mine, as well as a more distant earthquake occurring along the Pärvie fault. Here we present analyses of the events using DAS data, a comparison of DAS and nodal data and various strategies to add DAS data to the SNSN processing. We will also discuss how data sharing between the Transport Administration and SNSN can be realized in future near real-time operations as the railway implements DAS technology.

How to cite: Lund, B., Papadopoulou, M., Kaslilar, A., and Rantatalo, M.: Incorporating Distributed Acoustic Sensing data in seismic network processing, an example from Sweden, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10198, https://doi.org/10.5194/egusphere-egu25-10198, 2025.

The interest in distributed acoustic sensing (DAS), using different implementations of Phase-Sensitive Optical Time-Domain Reflectometry (ΦOTDR), has dramatically increased in the last decade, particularly in the context of seismic signals, due to the possibility of high spatial coverages over >100km with a single fibre interrogator. However, while the possibility for high sensitivity measurements up to the vicinity of 1Hz regime have been extensively researched, potential of this technique for long-term (>24h) measurements has been so far largely unexplored.

In this paper Large Chirped-Pulse ΦOTDR (LCP-ΦOTDR) employing large chirps (8GHz) and assisted by distributed Raman amplification was used to extend traditional DAS bandwidth to day long high-sensitivity (mK) measurements over 50km of fibre.

With the use of large chirps in Chirped-Pulse ΦOTDR (extended almost one order of magnitude from the previously researched ≈1GHz to 8GHz, while retaining 10m spatial resolution), the stability of a fibre reference is greatly increased, allowing for long-term measurements without 1/f noise accumulation. Therefore, the intrinsic DAS sensitivity (sub-mK, sub-nε) observed over high frequencies (>1Hz) is maintained over much longer periods (>24h) even for several temperature variations of several ºC in the fibre.

Long range operation was achieved via distributed Raman amplification, which ensured high optical SNR over 50km of a standard single-mode fibre. The system’s nonlinearities were characterized both in the optical domain and in the dynamic strain sensing results thus ensuring an operation regime with sensor linearity and distortion free DAS measurements.

At the end of the 50km fibre (the worst point of optical SNR), calibrated dynamic strain signals (0.5Hz, 3000nε peak-to-peak) were applied to verify the system's response for traditional DAS operation.  Then, temperature variations of several ºC were tracked over 72h, with an upper bound error of a few millikelvin. Coherency was maintained even after several hour-long measurement interruptions.

Finally, it should be noted that the LCP-ΦOTDR upper bound error of a few millikelvin over 72h is a conservative estimation, since an accurate assessment of the system’s noise floor was not experimentally possible (as the LCP-ΦOTDR sensitivity exceed our own experimental capability of applying or measuring mK temperature variations in the fibre).

How to cite: Canudo, J., Hernandez-Martín, L., Ania-Castañón, J. D., Preciado-Garbayo, J., and F. Martins, H.: Extending DAS operation bandwidth to LonG rAnge Millikelvin distriButed fIbre Thermometry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10998, https://doi.org/10.5194/egusphere-egu25-10998, 2025.

Etna volcano (Italy) is one of the most active volcanoes in the world with a great variety of events leading to effusive and/or explosive eruptions. The eruptive events are usually preceded and accompanied by ground deformation, that is often so tiny that only high-precision borehole strainmeters are capable to capture. So far, the analysis of more than 10 years of records from the Etna strainmeter network has shown the importance of continuous strain monitoring both for surveillance purposes and research advancements. Despite their valuable contribution, the number of deployable borehole high-precision strainmeters is limited by costs, logistics and challenges in the installation. Here, we demonstrate that fiber optic sensing is a valid alternative for measuring ultra-small slow volcano deformation.

In 2024 an innovative Distributed Fiber Optic Sensing prototype has been set up to interrogate a fiber optic cable installed behind casing in a 200-m deep borehole in Serra La Nave (on the southern Etna flank at ca. 5 km away from the summit craters). The new interrogator uses a reference-based Rayleigh backscatter correlation method which allows for precise long-term strain measurement. The quality of the fiber optic data is assessed by comparing the signals against well-known rock deformation responses and against strain time series recorded by the Etna strainmeter network. Thanks to the accuracy better than few nanostrain in the low frequency range, we clearly observe Earth tidal components in the fiber optic strain data. Peaks in the M2 (period 12.42 h) and O1 (period 25.82 h) tidal constituents emerge well above the background noise. The reliable detection and extraction of tidal components provide the opportunity to characterize and quantify the coupling between the fiber and the rock formation. Moreover, strong correlations with atmospheric pressure changes and rainfall events are observed. These evidences demonstrate a good coupling between the fiber optic cable and the surrounding rocks, although the degree of coupling is highly variable along the cable. The analyses show a long-term stability of the interrogator capable to record volcano deformation.

On the morning of 10^th November 2024 Etna experienced a weak lava fountain preceded by a short and small seismic swarm. Despite the tiny deformation induced by the volcano activity, the fiber optic interrogator was able to discern strain variations on the order of 125 nanostrain over 2 h (0.02 nanostrain/s). The first analyses and interpretation of the signal related to the volcano activity will be presented.

How to cite: Currenti, G., Jousset, P., Liehr, S., Carleo, L., Pulvirenti, M., Pellegrino, D., Kirchner, J., Hahn, M., Bonaccorso, A., and Krawcyz, C.: Ultra-small volcano deformation recorded at Etna by fiber optic sensing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11343, https://doi.org/10.5194/egusphere-egu25-11343, 2025.

Distributed Acoustic Sensing (DAS) data often involve large volumes but may be characterized by low Signal-to-Noise Ratios (SNR) compared to traditional seismic point sensors when terrain conditions hinder appropriate sensor coupling. While the vast amount of data increases analysis capabilities, the low SNR may bury signals of interest under the high environmental noise level emitted by a multitude of near-surface processes - in turn hindering detection and analysis capabilities. Effective denoising techniques are therefore the essential preliminary to uncover buried signals of interest and further to significantly increase the number of event detections.

We deployed a DAS system on Rhône Glacier, Switzerland, using a 9-km-long fibre-optic cable spanning the entire glacier from its ablation to its accumulation zone. With the intention to detect and analyse icequakes, we collected continuous records during one month in July 2020, comprising 14 TB of strain rate data at 1000 Hz sampling rate. We define signals of interest to be coherent signals originating from either surface events (e.g., crevasse formation) or basal events (e.g., stick-slip motion), while noise originates from incoherent sources like water flow or meteorological forcings. 

We demonstrate that a self-supervised machine learning denoising technique can significantly improve the SNR of signals of interest, facilitating event detection. By comparing our approach to traditional denoising methods and other self-supervised machine learning techniques, we highlight its advantages in achieving significantly enhanced SNR values. Further, we quantify the impact of comprehensive denoising by evaluating the event detection performance of the classical STA/LTA triggering algorithm applied to both raw and denoised datasets. Our results underscore the critical role of denoising in improving data interpretability and enhancing event detection capabilities in cryoseismological DAS studies.

How to cite: Zitt, J., Paitz, P., Fichtner, A., Walter, F., and Umlauft, J.: Enhancing Event Detection in Distributed Acoustic Sensing Data through Comprehensive Denoising, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11352, https://doi.org/10.5194/egusphere-egu25-11352, 2025.

Urban areas are very vulnerable to the effects of geohazards, with a high potential for human life and financial loss due to their high population density and advanced infrastructure. Obtaining high-resolution, subsurface information in urban areas is critical to understand and mitigate the effects of these hazards. In these densely populated centers, the shallow Earth is most stressed, as human activities continuously change their properties and interfere with natural processes. Geophysical surveys often face limitations in urban environments due to logistical constraints (anthropogenic activities, legal restrictions, and risks of equipment theft, among others). Repurposing unused, existing telecommunication optical fibers (so-called dark fibers) as sensing arrays offers a promising alternative to traditional geophysical methods, enabling subsurface imaging at high spatial and temporal resolution in densely populated areas. 

The Megacity of Istanbul (Turkey), situated in one of the most tectonically active regions in the World, represents an ideal case for exploring the potential of using dark fibers for subsurface investigations in urban areas. Since May 2024, we have been using Distributed Acoustic Sensing (DAS) to continuously record passive seismic data along a 17 km-long dark fiber crossing the Kartal district in the metropolitan area of Istanbul with the aim to explore the potential of this technology for seismic hazard assessment. Our objective is to apply passive seismic interferometry approaches to DAS ambient seismic noise data to identify hidden faults and generate high-resolution urban-scale subsurface velocity models that can contribute to a better understanding of structures and material properties and their association with seismic risk. Using dark fibers in urban contexts presents multiple challenges, including vast data volumes, a complex noise environment, and unconventional geometries. To address these issues, we develop tools to maximize the potential of dark fibers by effectively utilizing opportunistic ambient noise sources. We evaluate valuable sources, such as train tremors and other traffic, and assess their effectiveness for DAS-based passive seismic interferometry in a complex array setting. Our final objective is to develop efficient approaches to achieve imaging at high spatial and temporal resolution, providing insights that could help mitigate geohazard risk in Istanbul and other similar urban areas.

How to cite: Pinzon-Rincon, L., Rodríguez Tribaldos, V., Martínez-Garzón, P., Hillmann, L., Bohnhoff, M., Kartal, R. F., Kılıç, T., and Krawczyk, C.: High-Resolution Subsurface Imaging for Hazard Assessment in Urban Areas Using Ambient Noise and Dark Fibers: a Case Study in Istanbul, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11688, https://doi.org/10.5194/egusphere-egu25-11688, 2025.