The Tatun Volcano Group (TVG) in northern Taiwan is a potentially active volcanic system situated in close proximity to the Taipei metropolitan area and critical global infrastructure, including major semiconductor manufacturing facilities. While previous geophysical investigations have successfully delineated the TVG’s hydrothermal and magmatic reservoirs at the kilometer scale, a significant resolution gap remains regarding the near-surface (meters to hundreds of meters) structures that govern fluid migration and fumarolic activity. This study implements an improved Electrical Resistivity Tomography (ERT) approach to characterize the fine-scale subsurface architecture beneath three prominent hydrothermal sites: Dayoukeng, Matsao, and Xiaoyoukeng.

To overcome the limitations of conventional ERT in rugged volcanic terrains, we utilized a Remote Resistivity Monitoring System (R2MS) equipped with a nontraditional hybrid electrode array and dense electrode spacings of less than 10 meters. A robust bootstrapping resampling workflow was developed to process massive datasets (approximately 180,000 points per line), allowing for the generation of median resistivity profiles and the quantification of model uncertainty through the Quartile Coefficient of Variation (QCV). This statistical framework ensures that identified anomalies are data-constrained rather than inversion artifacts.

Our results reveal distinct electrical signatures associated with varying degrees of hydrothermal maturation. Beneath Dayoukeng, we identified a prominent arch-shaped low-resistivity structure (1 to 10 Ohm-m), featuring a vertical active fluid conduit that facilitates the ascent of magmatic gases. In contrast, the Matsao area exhibits more scattered and diffuse low-resistivity anomalies (2 to 10 Ohm-m), suggesting a less advanced or currently less vigorous hydrothermal pathway compared to Dayoukeng. The Xiaoyoukeng profiles demonstrate a stratified resistivity structure, where high-resistivity shallow layers (300 to 2000 Ohm-m) overlie deeper low-resistivity zones (10 to 30 Ohm-m), showing strong correlation with lithological data from Borehole W1.

A critical scientific finding is the recurring "low-over-high" resistivity pattern observed in the vicinity of active vent holes. This signature, characterized by extremely conductive altered zones overlying more competent andesitic bedrock, provides a diagnostic geoelectrical indicator for identifying subsurface gas migration pathways. Furthermore, the study identifies "immature" conduits beneath certain profiles where fluids appear trapped under impermeable rock layers, potentially increasing internal pressure.

In conclusion, this research provides a high-resolution visualization of the TVG’s shallow plumbing system, offering new insights into the spatial heterogeneity of volcanic degassing. The integration of automated R2MS acquisition with statistical uncertainty quantification establishes a reliable framework for long-term volcanic monitoring. These findings are essential for refining risk assessments and enhancing disaster preparedness for future phreatic eruptions in northern Taiwan.

How to cite: Chen, H.-J., Chen, C.-C., Hong, G.-T., and Lu, W.-C.: Improved electrical resistivity tomography reveals near-surface structures beneath fumaroles at Tatun Volcano Group, Taiwan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10191, https://doi.org/10.5194/egusphere-egu26-10191, 2026.

Urban development in central Saudi Arabia frequently encounters subsurface hazards associated with karstification, weathering, and fracturing of carbonate rocks. These features pose significant geotechnical risks for high-rise structures if not properly identified at the early design stage. This study presents the results of a high-resolution Electrical Resistivity Tomography (ERT) survey conducted at the proposed King Fahad Tower site in Riyadh, Saudi Arabia, aimed at detecting subsurface cavities and weak zones within shallow limestone formations.

The site is located on the Arabian Shelf and is underlain predominantly by sedimentary formations comprising limestone, dolomite, marl, and evaporites of Jurassic to Cenozoic age. A total of 25 ERT profiles were acquired using a multi-channel resistivity imaging system with a dipole-dipole electrode configuration. The survey achieved an investigation depth of approximately 10 m below ground level, providing detailed two-dimensional resistivity images of the shallow subsurface. Inverted resistivity models reveal a wide range of resistivity values, from as low as 2 Ω·m to greater than 600 Ω·m, reflecting strong subsurface heterogeneity. Very low resistivity anomalies (<20 Ω·m) are interpreted as zones of saturated cavities, clay-filled voids, or highly weathered and fractured limestone. Moderate resistivity values (20-150 Ω·m) likely correspond to weathered or partially saturated strata, while higher resistivity zones (>150 Ω·m) are associated with competent limestone bedrock. Several low-resistivity anomalies exhibit vertical continuity and lateral persistence, suggesting potential pathways for infiltration and zones susceptible to collapse.

Based on the integrated interpretation, a subsurface risk map was developed to delineate high-risk zones requiring verification and mitigation. The results demonstrate that ERT is an effective non-invasive tool for mapping shallow karst-related features in carbonate terrains and for optimizing intrusive investigations and ground improvement measures. This study highlights the importance of incorporating geophysical imaging into urban geotechnical site investigations to reduce construction risk and support safe foundation design in karst-prone regions.

How to cite: Akbar, A. and Ali, A.: Imaging Subsurface Cavities in Carbonate Rocks Using Electrical Resistivity Tomography, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16733, https://doi.org/10.5194/egusphere-egu26-16733, 2026.

The hydrogeological setting of the Belgian coastal area near De Panne is characterized by the presence of an upper saline plume within the subterranean estuary. This salinity distribution can be imaged using Electrical Resistivity Tomography (ERT), where the saline plume manifests as a zone of low electrical resistivity. However, a similar low-resistivity response may also arise from specific geological formations, such as clay layers. Therefore, lithological investigations are required to distinguish between low resistivity caused by saline groundwater and by geological heterogeneity, which strongly influences groundwater distribution.

Cone penetration tests (CPT), a direct-push method, are employed to characterize subsurface soil properties. In addition, CPT-based resistivity measurements (CPT-R) are used to discriminate between low-resistivity zones associated with lithology and those related to groundwater salinity. To improve ERT inversion results, CPT-R data are incorporated in several ways: as geostatistical constraints (using correlation lengths derived from geostatistical analysis), through joint inversion, and/or as reference models. This study therefore investigates how these different inversion setups influence the ERT inversion results, with a particular focus on the associated uncertainty.

An ensemble deterministic ERT inversion approach is adopted to assess the inversion uncertainty. Key inversion parameters are randomly varied across 100 realizations, which are subsequently combined into a single ensemble. In total, 12 ensembles are generated, representing different inversion strategies and each using the same set of randomly sampled parameters. These parameters include the regularization strategy (constant or optimized), the type of constraint (smoothness or geostatistical), the inversion approach (ERT-only or joint inversion), and the reference model (none, homogeneous, or heterogeneous). Heterogeneous reference models are constructed using sequential Gaussian simulations based on CPT-R data.

By comparing multiple inversion strategies and integrating CPT-R data within an ensemble framework, the uncertainty associated with resistivity models is systematically assessed. This study highlights how choices in ERT inversion setup directly influence model uncertainty, particularly when heterogeneity is neglected, leading to a strong underestimation of uncertainty.

How to cite: Vrancken, E., Paepen, M., and Hermans, T.: A deterministic ensemble inversion framework to assess the uncertainty of electrical resistivity tomography combined with cone penetration tests, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9419, https://doi.org/10.5194/egusphere-egu26-9419, 2026.

Interpretation of self-potential (SP) anomalies is challenging due to the presence of spatially coherent background noise that can obscure or distort the source signal. These systematic background effects, analogous to regional components in gravity or magnetic methods, arise from measurement errors, heterogeneous subsurface conditions, or interactions among multiple anomalous sources. They often exhibit non-linear behavior that cannot be adequately addressed using a constant or linear slope. Earlier approaches attempted to remove such coherent patterns through baseline corrections or linear de-trending.This study presents an incremental algorithmic development for the interpretation of SP anomalies associated with a 2D inclined thin-sheet structure, explicitly accounting for non-linear background contributions while jointly estimating source geometry. Using the metaheuristic technique of Particle Swarm Optimization (PSO), the background field is parameterized as a second-order polynomial, with coefficients representing a constant offset, linear gradient, and quadratic curvature. These coefficients are simultaneously optimized with the source parameters using an L2-norm type misfit. The method is particularly stable with respect to depth and half-width of causative body; however, dip, location, and electric dipole moment can become ambiguous in the presence of noise. Therefore, separation of background trends from the signal is crucial for recovering the actual source parameters accurately. To assess solution stability, the spread of solution ensemble obtained from multiple independent PSO runs under identical conditions is analyzed. To further evaluate parameter sensitivity and interdependence, a correlation matrix is computed and crossplots are plotted. Among the background components, quadratic curvature exhibits the strongest coupling with the recovered source parameters, whereas the constant offset shows minimal influence compared with source-only optimization.When extended to multiple-source SP anomaly data, quadratic background modeling proved inadequate. Using a single quadratic polynomial failed to capture complex regional–local interactions, while assigning separate quadratic backgrounds to individual sources unnecessarily increased the dimensionality of the model space. To address this problem, a residual-based higher-order background modeling approach is implemented. In this approach, source parameters are optimized first, and the resulting residual field is iteratively approximated using a polynomial of the lowest order necessary to capture systematic background effects within the optimization framework, thereby avoiding the enforcement of a fixed polynomial degree.The proposed method is evaluated using synthetic SP anomaly data under both noise-free and noisy conditions and is subsequently validated using field datasets, including a single-source anomaly from the Surda region, India, and a multiple-source anomaly from KTB region, Germany.

Overall, the proposed approach offers benefit in improved recovery of source parameters by effectively decoupling source and background responses. The polynomial background represents long-wavelength, spatially coherent variations of physical origin superimposed on target signal. However, this approach increases model dimensionality, may lead to overfitting if search bounds are not appropriately constrained, and may result in non-unique polynomial representations in multi-source cases.

Keywords: Self-Potential (SP) method, 2D inclined thin-sheet, Non-linear background, Particle Swarm Optimization, Residual modeling.

How to cite: Chakraborty, S. and Sharma, S. P.: Interpretation of Self-Potential Anomalies over 2D Inclined Thin Sheet structure using Particle Swarm Optimization with Non-linear Polynomial Modeling of Background Effects, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2715, https://doi.org/10.5194/egusphere-egu26-2715, 2026.

Nowadays, the preservation of cultural heritage has become a matter of debate, as numerous factors contribute to its deterioration. In this case, water plays a key role, as it can cause significant damage to construction materials over time. Direct measurements of water content (WC) are not feasible in cultural heritage buildings because they are destructive. Therefore, it is essential to apply methods which are non-destructive and also highly sensitive to the presence of water.

Microwave-based moisture instruments utilize the transmission or reflection microwaves (i.e., electromagnetic waves in the frequency range: 0.3-300 GHz) to evaluate WC within materials. However, a major limitation of WC microwave measurements is that they provide cumulative moisture values that integrate the contribution of all material layers from the surface up to the probe’s penetration depth. As a result, previous studies have only relied on displaying cumulative moisture maps instead of the true ones.

This work addresses this limitation by a simple least squares (LS) inversion approach based on an average weighted function, since no information about the actual weighting function implemented in the device is available. The forward model was assumed that the cumulative measurement at each penetration depth is of a weighted linear combination of moisture contributions from successive layers. Then, the LS solution was computed through the Moore-Penrose pseudoinverse to lead to the real WC at discrete depths without physical sampling.

The method was applied to a real dataset acquired at the Certosa del Galluzzo (Florence, Italy), a 14th-century historical complex affected by moisture deterioration. The instrument used in this study was the Moist 350B sensor, designed by HF sensor GmbH (Leipzig, Germany). The device utilizes the transmission and reflection of electromagnetic waves in the range of microwave (approximately 2.45 GHz) to evaluate the WC within materials by measuring their dielectric permittivity. It is equipped with five interchangeable probes, which are used for detecting WC at different penetration depths: 3 cm, 7 cm, 11 cm, 30 cm, and 80 cm.

First, the interest zones were identified by infrared thermography (IRT). Subsequently, the microwave sensor with all probes was applied to acquire cumulative data in these areas. Finally, synthetic Gaussian noise with a standard deviation of 1.5% (on the basis of the manual) was added to the dataset to simulate realistic measurement uncertainty prior to the inversion.

The inverted data for the superficial layer (at 3 cm) reveal good agreement with the IRT results, whether the area is wet or dry. In addition, the results indicate that when a layer is highly saturated, the layer below will be significantly affected so that its moisture amount is lower compared to the acquired data. In fact, the presentation of raw data, especially in a highly saturated layer, causes the layer below to be considered overestimated compared to the real values. In summary, the proposed approach can effectively reconstruct the real distribution without any physical sampling.

How to cite: Yousefi, M., Innocenti, A., Marchetti, E., Fanti, R., and Pazzi, V.: From apparent to real moisture index in masonry through inversion of microwave data: a first attempt, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11031, https://doi.org/10.5194/egusphere-egu26-11031, 2026.

Magnetic gradient tensors obtained from multiple magnetic sensors have been increasingly applied in various near-surface explorations. Accurate interpretation of high-resolution magnetic gradient tensor data requires analytical expressions due to simple geometric bodies. In this study, analytical expressions for the magnetic field and magnetic gradient tensor responses due to an elliptical cylinder are derived. An elliptical cylinder is geologically relevant, as igneous intrusions such as kimberlite pipes commonly exhibit elliptical cross-sections with axial symmetry and anisotropic radial extents in the strike and transverse directions. The magnetic responses are obtained by transforming the previously derived gravity gradient tensor of an elliptical cylinder using Poisson’s relation. The gravitational potential, defined as a triple integral, is differentiated twice with respect to each coordinate axis to obtain the gravity gradient tensor. And the gravity gradient tensors are then converted into magnetic responses in the real domain. The magnetic gradient tensor expressions in the real domain are integrated along the symmetry axis (z-direction) to reduce them to double integrals. By introducing complex variables, the real double integrals are transformed into complex integrals. Finally, using the complex form of Green’s theorem, the magnetic gradient tensors due to the elliptical cylinder are expressed as a one-dimensional line integral evaluated along the elliptical boundary.

 Acknowledgements: This work was supported in part by research project from KIGAM and G-LAMP project based on a National Research Foundation of Korea grant from the Ministry of Education (No. RS-2023-00301938), S. Korea. 

How to cite: Rim, H.: Closed-form expressions of the magnetic and magnetic gradient tensor due to an elliptical cylinder, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3204, https://doi.org/10.5194/egusphere-egu26-3204, 2026.

This presentation summarizes a project focused on the seismic imaging and time-lapse monitoring of dams, embankments, and landslides using seismic and complementary geophysical methods. These structures are particularly sensitive to environmentally induced changes, such as variations in water saturation driven by climate variability and human activity, which can significantly affect their stability and long-term performance.

This study presents the results of repeated geophysical surveys conducted in 2023 and 2024 to investigate seepage and under-seepage processes in critical infrastructure related to the Rybnik water reservoir in southern Poland. To analyse the state of the embankment and dam, we used a combination of seismic, CCR, and ERT methods, supported by observations with a DAS system and an innovative Spectral GPR. Using repeated surveys, we were able to image not only spatial inhomogeneities but also changes in the structure related to different water tables and water saturation in the studied Earth-filled objects. Moreover, between the two surveys, maintenance works were performed to limit excessive seepage in the embankment. This action reduced seepage by 30%, but geophysical data enabled a spatial evaluation of the works and identified areas that require future monitoring.

Besides standard analysis in the form of ERT and seismic tomography, we utilised high-resolution seismic data recorded at a 2 m horizontal spacing for reflection imaging. This allowed us to recognise geological structures below man-made structures and the effects of the old river bed located beneath the construction. An additional 3C geophone was used for the seismic survey, allowing for precise analysis of P and S waves. This resulted in Vp/Vs analysis of the objects. Moreover, the combination of P and S wave reflection images provides insight into detailed structures that cannot be recognised with standard methods.

Finally, we utilised the DAS system to further increase the spatial resolution of the seismic data. A comparison of DAS and horizontal geophone data shows that DAS provides long-term monitoring capabilities, essential for ongoing structural health assessments and geohazard detection. For example, the multichannel analysis of surface waves (MASW) using DAS data clearly identifies S-wave velocities down to 13 m with an RMS error of 3.26%, compared to an RMS error of 6.2% for geophone data.

In addition, seismic tomography was applied to the Cisiec landslide (Żywiec district, southern Poland), where time-lapse velocity models are used to track hydrologically driven changes in subsurface properties associated with slope instability.

This research was funded by National Science Centre, Poland (NCN) project number 2022/45/B/ST10/00658.

How to cite: Roshdy, E., Majdanski, M., Oryński, S., Marciniak, A., Kowalczyk, S., Mieszkowski, R., Gołebiowski, T., Trześniowski, Z., Długosz, S., Bednarz, B., and Popielski, P.: Geophysical Monitoring of Environmentally Induced Changes in Dams, Embankments, Landslides, and Their Subsurface: Repeated Multi-Method Surveys, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7503, https://doi.org/10.5194/egusphere-egu26-7503, 2026.

The increasing frequency of extreme climatic events, from protracted droughts to high-intensity precipitation, necessitates robust frameworks for monitoring soil hydrological dynamics and associated geological risks. Hydrogeophysical methods, particularly when integrated with multi-sensory environmental arrays, offer a powerful means of capturing the spatiotemporal evolution of pore pressure, a key driver of slope instability. This study presents a multi-parametric, multi-scale monitoring strategy deployed at an open-air laboratory situated on a slow-moving peri-urban landslide in the southern Apennines (Basilicata, Italy) developed as part of the ITINERIS project (PNRR M4C2 Inv.3.1 IR EU’s Next Generation program).

We combine time-lapse Electrical Resistivity Tomography (tl-ERT) with a diverse hydrological sensor suite, including tensiometers, piezometers, soil temperature probes, and a non-invasive Cosmic Ray Neutron Sensing (CRNS) station for area-wide moisture estimation. To address the complexities of hydrogeological scaling, e.g., the dynamic nature of soil moisture patterns and their scale-dependent manifestations, we developed a customized laboratory framework designed to replicate field-scale coupled ERT and hydrological measurements. This dual-scale approach enables the derivation of site-specific petrophysical relations and facilitates the calibration of 2D/3D dynamic thermo-hydro-geophysical model.

This work focuses on the development of a robust data mining and processing workflow designed to harmonize heterogeneous geophysical, hydrological, and meteorological datasets. In this study, we present the validation of laboratory protocols alongside the preliminary setup of multi-scale field monitoring and field acquisition systems. By proposing inversion strategies and automated quality control, we aim to minimize interpretative ambiguity and move towards a more geologically consistent representation of vadose zone mechanisms. This integrated approach is establishing a preliminary foundation for future predictive modelling while offering a scalable solution for monitoring hydrogeological hazards in complex environments.

How to cite: Martino, L., Calamita, G., Lacava, T., Satriani, A., Uhlemann, S., Canora, F., and Perrone, A.: Multi-Scale Hydrogeophysical Integration: From Lab Calibration to Field Mapping of Unsaturated Soil Moisture in Peri-urban Slope Instabilities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16643, https://doi.org/10.5194/egusphere-egu26-16643, 2026.

The uranium mineralization has been reported at multiple locations within the South Purulia Shear Zone (SPSZ), as well as in its north-eastern extension near Bari village, located approximately 19 km from the central SPSZ. To delineate the structural framework, depth extent, and potential uranium-bearing zones in this region, a comprehensive resistivity and induced polarization (IP) survey was conducted at different location in the vicinity of Bari region. The study involved 2D electrical resistivity tomography (ERT) with multi-electrode Schlumberger and dipole–dipole arrays, induced polarization (IP) surveying using a multi-electrode Schlumberger array, and gradient resistivity profiling (GRP) along multiple parallel profiles perpendicular to the regional strike. This setup enabled detailed imaging of the subsurface resistivity distribution and identification of chargeable zones. Interpretation of the ERT and IP and GRP data revealed a prominent resistive body located at a depth of approximately 30–35m, extending laterally in the east-west direction. The high-resistivity zone observed in the ERT and GRP sections corresponded spatially with high-chargeability anomalies in the IP data, suggesting the presence of disseminated uranium mineralization zones. The coincident high resistivity and chargeability anomalies are indicative of potential uranium-bearing alteration zones hosted within muscovite quartz schist units. The integrated application of geophysical methods in this study significantly enhances the accuracy of target identification and facilitates the effective delineation of subsurface geological structures dimensions.

How to cite: Bar, H.: Integrated Electrical Resistivity and Induced Polarization Investigation of Uranium Mineralization in the North-Eastern Extension of the South Purulia Shear Zone, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7014, https://doi.org/10.5194/egusphere-egu26-7014, 2026.

The physical conditions at the base of the Greenland Ice Sheet (GrIS) fundamentally dictate ice dynamics and its response to climate change. However, due to the limited spatial coverage of ice-penetrating radar surveys and borehole drilling observations, as well as the heavy reliance of thermo-mechanical ice-flow models on prescribed parameters, the shallow internal structure of the ice sheet and its basal environment remain insufficiently. Seismology provides an opportunity to constrain the depth of the ice–bedrock interface through the characterization of subsurface shear-wave velocity (Vs) structures.The study area is located northeast of Kangerlussuaq in Southwest Greenland. Previous studies show that long-term seismic velocity variations in this region are minimal, limiting the ability to infer basal frozen or thawed conditions from temporal changes alone. In addition, most investigations of the basal thermal state of the GrIS have focused on central and northern Greenland, leaving Southwest Greenland relatively under-explored. Although three-dimensional thermomechanical ice-flow models consistently predict thawed basal conditions here, these results rely primarily on numerical simulations and indirect constraints, highlighting the need for independent seismological validation.To address this, we analyze continuous ambient seismic noise recorded by 80 temporary seismic stations deployed across the study area. We apply the degree of polarization–ellipticity (DOP-E) method to measure Rayleigh-wave ellipticity and invert for shallow Vs structures within the priori knowledge of ice properties by using the neighborhood algorithm.This study provides seismological constraints on the internal and basal conditions of the GrIS in Southwest Greenland that complement existing radar observations and thermodynamic models, thereby establishing a new observational framework for investigating basal material properties and ice-dynamic processes in this region.

How to cite: Cai, C.-R., Chao, W.-A., Gimbert, F., and Paris, N.: Constraining Basal Conditions of the Greenland Ice Sheet Using Rayleigh-Wave Ellipticity From Ambient Seismic Noise Records, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15649, https://doi.org/10.5194/egusphere-egu26-15649, 2026.

We present a 2-D ambient noise full waveform inversion technique based on noise

cross-correlation sensitivity kernels. These kernels are constructed through the adjoint state

method, using a time-domain finite-difference solver to simulate both forward and

adjoint acoustic wavefields. Both the ambient noise source distribution and velocity structure

are treated as unknown. The inversion for source, and then structure parameters is

carried out sequentially. This sequential inversion is based on waveform energy misfit in

the case of noise source and cross-correlation travel time misfit in the case of velocity

structure. The present study focuses on applying this ambient noise full waveform inversion

methodology at local scales.

We use this approach to image the velocity structure beneath the Lonar crater in India.

This basaltic impact crater has close geological analogs on the Moon, and its internal structure

provides an important benchmark for assessing geometric models of crater formation.

We compare the results from our inversion with those obtained using conventional ambient

noise interferometry, which relies on Green’s function retrieval.

How to cite: Mahesh, A. and Datta, A.: 2-D Acoustic Full Waveform Ambient Noise Inversion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1162, https://doi.org/10.5194/egusphere-egu26-1162, 2026.

Extreme climate events and increasing geohazard risks require high-resolution near-surface seismic imaging to better characterize subsurface structures. Ambient seismic noise provides a cost-effective alternative to active-source surveys and has been widely used for S-wave velocity imaging through dispersion-based ambient-noise tomography. However, these approaches rely on accurate Green’s function retrieval, which assumes isotropic and uncorrelated noise sources—conditions rarely satisfied in real field environments. As a result, waveform distortions and resolution loss are common, limiting the quantitative interpretability of conventional ambient-noise imaging.

Ambient-noise full waveform inversion (FWI) offers a promising pathway to overcome these limitations by directly fitting cross-correlation waveforms and fully exploiting waveform information. Nevertheless, its application remains challenging due to strong trade-offs between subsurface structure and unknown noise source characteristics, severe nonlinearity and cycle-skipping, and the lack of reliable constraints on noise source distributions. These issues have so far hindered the practical implementation of ambient-noise FWI in complex near-surface settings.

To address these challenges, we develop a physics-informed generative adversarial network (PIGAN) framework for ambient-noise waveform inversion to accurately estimate physically consistent velocity models in a distributional sense. The wave-equation-based cross-correlation operator is embedded into the generator to ensure physical consistency, while a neural-network discriminator evaluates the mismatch between observed and simulated data. A one-dimensional Wasserstein distance is adopted to enhance robustness to noise and phase uncertainties. The proposed method organically integrates wave-equation constraints, deep learning, optimal transport metrics, and a minimax game formulation, combining the strengths of physics-informed modeling and data-driven representation. This framework enables joint inversion for subsurface velocity structure and ambient noise source characteristics, effectively mitigating source–structure trade-offs. Moreover, it does not require labeled datasets or network pretraining; therefore, the framework is flexible and enables inversion with minimal user interaction. Synthetic tests and field applications in the Qinghai–Tibet Engineering Corridor demonstrate improved resolution and deeper illumination, providing new constraints on fault zone structures and implications for geohazard assessment.

How to cite: Ma, Y., Xia, J., Cheng, F., and Guan, J.: Ambient Noise Full Waveform Inversion with Physics-Informed Generative Adversarial Networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4723, https://doi.org/10.5194/egusphere-egu26-4723, 2026.

The shallow subsurface media in urban areas are closely related to human life. Near-surface media generally exhibit characteristics such as lower seismic wave velocity, lower density, stronger absorption of seismic wave energy, and significant heterogeneity both laterally and vertically. When seismic waves propagate upward from the deeper high-impedance bedrock to the low-impedance loose overburden near the surface, influenced by energy conservation and strong impedance contrasts, significant ground motion amplification occurs, characterized by increased amplitude and prolonged vibration duration. This site effect can trigger resonance phenomena, exacerbate the destructive power of strong earthquakes, and lead to severe disasters. Therefore, acquiring high-resolution shallow shear-wave velocity structures through advanced imaging techniques is crucial for site response evaluation and seismic hazard risk prevention and control. As an ultra-dense seismic observation method, Distributed Acoustic Sensing (DAS) technology offers a sensor spacing of 1–10 meters, enabling higher-resolution imaging of near-surface structures at a lower cost. The Wenyu River area in Beijing features complex geological structures, including potential geological hazards such as ground fissures and land subsidence, which significantly impact urban planning and underground space construction in Beijing. This study utilizes data collected from a DAS system deployed in the Wenyu River area to conduct near-surface imaging research, obtaining a high-resolution two-dimensional shear-wave velocity structure within a depth of 80 meters. The results reveal significant vertical stratification in shear-wave velocity from 0 to 80 meters depth: a low-velocity zone with Vs < 150 m/s at 0–10 meters depth, likely caused by backfill during fiber optic installation; a gradual increase in shear-wave velocity from 150 m/s to 300 m/s at 10–40 meters depth; and increased medium stiffness at 40–80 meters depth, with shear-wave velocities reaching approximately 450 m/s, reflecting a lithological transition from loose fill and silty clay to dense sand-gravel layers. Local low-velocity anomalies observed in channels CH036 and CH131 are likely attributed to the cavity effect of underground drainage channels and reduced soil shear modulus due to water infiltration from an artificial lake, as confirmed by field investigations.

How to cite: Yu, X., Yang, J., and Zhang, W.: Ambient noise shallow structure imaging with distributed acoustic sensing: A case study in Wenyu River area, Beijing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6349, https://doi.org/10.5194/egusphere-egu26-6349, 2026.

Marine seismic reflection experiments typically use a towed streamer and impulsive acoustic sources (such as airguns, sparkers or boomers) to image sub-seafloor acoustic reflectivity. These sources emit high amplitude, short period signals designed to achieve good resolution and penetration. This causes correspondingly high Peak Sound Levels that can in some cases adversely affect marine fauna and contribute to background ocean noise. In recent years this has led to increasing environmental restrictions on seismic surveying, in addition to the existing operational complexity and cost of using active sources. 

We propose an alternative method, suited for shallow sub-seafloor characterisation, that uses the broadband noise generated by the acquisition vessel as the seismic source – eliminating the need for a dedicated active source. We use multichannel streamer recordings to estimate the vessel-generated acoustic wavefield, and cross-correlate this with the raw continuous recordings to produce virtual common shot gathers that are regularly sampled in space, compatible with conventional seismic imaging workflows. Here we present preliminary results of the SLIPSTREAM project, a pilot study conducted in September 2025 in the Port of Trieste, north east Italy, in shallow water (<20 m) using a multi-channel streamer (24 hydrophones spaced at 1 m). The project aimed to assess the feasibility of the source-less approach for research-scale, high-resolution 2-D seismic acquisition. We do this by assessing the quality of geophysical imaging and quantifying the reduction in impact to specific marine fauna that are common in the area, compared to a conventional active source “Boomer” acquisition. We demonstrate that the source-less data is able to image the boundaries and internal structure of several different shallow geological units, with a maximum penetration of around 40 m below the seafloor.

Future experiments will focus on improving the resolution and depth of investigation by controlling the vessel speed, as well as exploring the application of this method with larger vessels in deeper water. The overall goal is to acquire seismic images with sufficient quality for geological interpretation in locations where active sources may be restricted for environmental reasons.

How to cite: Affatati, A., Baradello, L., Meneghini, F., Busetti, M., and Ford, J.: Source-less marine seismic imaging using vessel noise: a feasibility study in the Port of Trieste, north east Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11601, https://doi.org/10.5194/egusphere-egu26-11601, 2026.

Active seismic methods are widely used in near-surface geophysics for subsurface characterization, but existing processing softwares often present significant limitations. Proprietary packages are expensive, lack transparency in their processing algorithms, and often include extensive features unnecessary for near-surface applications, while open-source alternatives frequently suffer from limited file format support, slow performance when handling large datasets, complex installation procedures, and dependence on specific configuration files. We present PyCKSTER, an open-source PyQt5-based graphical user interface designed to address these challenges by providing an efficient and user-friendly solution for active seismic data workflows.

PyCKSTER uses ObsPy (Beyreuther et al., 2010; https://www.obspy.org) to handle standard seismic file formats (SEG2, SEGY, Seismic Unix), and relies on pyqtgraph (https://www.pyqtgraph.org) for optimized visualization. The software provides comprehensive data editing capabilities including batch header modification (source and trace coordinates,, delay, topography integration), trace manipulation (move, swap, mute, delete), and interactive quality control. Intuitive mouse-driven picking tools with multiple visualization options (source/geophone diagrams, hodochrones) facilitate traveltime analysis. Picked traveltimes are saved in pyGIMLi's unified format (Rücker et al., 2017; https://www.pygimli.org), enabling direct velocity model reconstruction through the integrated pyGIMLi inversion module or advanced processing using pyGIMLi's extended capabilities.

PyCKSTER also includes a surface wave processing module, addressing a common gap in near-surface seismology where body wave and surface wave analyses are typically performed separately due to limited tool availability and specialized expertise requirements. While both wave types are recorded in the same dataset, their joint processing enables comprehensive characterization through combined Vp and Vs analysis, ultimately allowing investigation of Vp/Vs and Poisson's ratio for improved lithological and hydrogeological interpretation. The software computes dispersion images using phase-shift transform developed in PAC (Cunha Teixeira et al., 2025) and offers interactive picking capabilities with windowing options. The tool also supports importing dispersion curves from the MATLAB package SWIP (Pasquet & Bodet, 2017; https://github.com/spasquet/SWIP), facilitating integration with existing workflows. Advanced dispersion windowing, stacking, and inversion capabilities are currently under development.

PyCKSTER is distributed under the GPLv3 license and available via PyPI, requiring no configuration files for standard use. We demonstrate the software's capabilities through field data examples and discuss ongoing developments.

Beyreuther, M., Barsch, R., Krischer, L., Megies, T., Behr, Y., Wassermann, J., 2010. ObsPy: A Python Toolbox for Seismology. Seismological Research Letters 81, 530–533. https://doi.org/10.1785/gssrl.81.3.530

Cunha Teixeira, J., Burzawa, A., Bodet, L., Hallier, A., Decker, B., Lin, F., Dangeard, M., Boisson Gaboriau, J., Dhemaied, A., 2025. Passive and Active Computation of MASW (PAC). https://doi.org/10.5281/zenodo.17639980

Pasquet, S., Bodet, L., 2017. SWIP: An integrated workflow for surface-wave dispersion inversion and profiling. GEOPHYSICS 82, WB47–WB61. https://doi.org/10.1190/geo2016-0625.1

Rücker, C., Günther, T., Wagner, F.M., 2017. pyGIMLi: An open-source library for modelling and inversion in geophysics. Computers & Geosciences 109, 106–123. https://doi.org/10.1016/j.cageo.2017.07.011

How to cite: Pasquet, S.: PyCKSTER: An open-source Python tool for interactive processing and analysis of active near-surface seismic data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5021, https://doi.org/10.5194/egusphere-egu26-5021, 2026.

The detection of underground cavities is important for geotechnical safety, groundwater assessment, and subsurface characterization. Fluid-filled cavities in karst areas can strongly influence seismic wavefields due to the contrast between the cavity contents and the surrounding rock. Interaction between seismic waves and cavities can lead to partial trapping of energy, producing reverberations and resonant signals that extend beyond the primary arrivals and appear as characteristic spectral peaks in the frequency domain.

In this study, we investigate the potential of seismic reflection data to identify deep (200m-800m) water-filled cavities (conduits) based on these characteristic responses observed along a seismic profile. Numerical simulations of seismic wave propagation are used to examine the development of cavity-induced resonances and their sensitivity to cavity properties and subsurface conditions. The results indicate that although cavity-induced resonance signatures are strongest at traces located directly above the cavity, they can still be used to determine the lateral position of the cavity along a seismic profile. We are using these numerical studies as a prelude to investigating deep ground water resources in Ireland’s extensive limestones, many of which exhibit karstification. In particular, we are developing spectral and other templates based on numerical simulations for expected deep conduit structures. These templates will be matched with real observations by re-examining existing deep reflection seismic data bases, in the search for deep water-bearing karst structures.

How to cite: bayat, S., Henry, T., and Bean, C. J.: Assessing the Feasibility of Detecting Water-Filled Cavities Along Seismic Reflection Profiles: A Synthetic Study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19932, https://doi.org/10.5194/egusphere-egu26-19932, 2026.

Sediment thickness is a key parameter in seismological studies, influencing seismic wave propagation, ground-motion amplification, and the interpretation of crustal and upper-mantle structure. Thick sedimentary cover, characterized by low seismic velocities, can strongly bias tomographic inversions if not properly accounted for. While receiver-function–based approaches have proven effective for estimating sediment thickness in Australia and North America, their systematic application across Europe has remained limited.

Here we test and refine a P-to-S receiver function (PRF)–based method for estimating sediment thickness in Central and Eastern Europe, focusing on major sedimentary basins including the Pannonian Basin, where sediment thickness locally exceeds 8 km. Additional analyses are carried out in the Vienna and Transylvanian basins to capture a wider range of geological settings. This method is controlled and compared to data derived from reflection seismic profiles and deep borehole data from the basins.

Using teleseismic PRFs, we measure the delay time of the P-to-S converted phase at the sediment–basement interface and relate it to sediment thickness through empirical fitting. The fitting accounts for multiple controlling parameters, including PRF delay time, basin-specific seismic velocity characteristics, and regional geological context. The study region represents an exceptional natural laboratory, as dense temporary and permanent seismic networks (e.g., AlpArray, PACASE, and AdriaArray) have been operating for nearly a decade, providing unprecedented data coverage.

Our results demonstrate that PRF-derived delay times reliably capture first-order variations in sediment thickness across structurally complex European basins. Our long-term goal is to extend this approach to the entire European continent, enabling a consistent, low-cost framework for mapping sediment thickness across diverse tectonic environments.

How to cite: Kalmár, D., Fodor, G., Marignier, A., and Balázs, A.: Sediment Thickness of European Basins Inferred from P-to-S Receiver Functions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3855, https://doi.org/10.5194/egusphere-egu26-3855, 2026.

P-wave polarization analysis provides valuable constraints on near-surface S-wave velocity by measuring the polarization angles of P-wave arrivals at seismic stations. However, the depth sensitivity of this method has not been well quantified, limiting its broader application to velocity structure estimation. In this study, we investigate the depth sensitivity of P-wave polarization angles across multiple frequency bands using numerical approaches based on the reflectivity method. Using the resulting frequency-dependent sensitivity kernels, we perform multi-frequency inversion of P-wave polarization angle measurements to estimate the shallow crustal S-wave velocity structure from the near-surface to the mid-crust. We apply this method to three-component seismic data recorded at hundreds of stations across the southern Korean Peninsula. The resulting velocity structures from 1 to 20 km depth show good agreement with receiver function results, while shallow structures within the upper 1 km are consistent with local site survey measurements. These results demonstrate that multi-frequency P-wave polarization angle inversion provides a complementary constraint on near-surface to mid-crustal S-wave velocity structure and can enhance the characterization of near-surface seismic properties.

How to cite: Kim, K. and Chang, S.-J.: Multi-frequency P-wave polarization angle inversion for shallow crustal structure, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4767, https://doi.org/10.5194/egusphere-egu26-4767, 2026.

In seismic exploration, subsurface structures are commonly investigated using dense sensor arrays. While effective, array-based observations require a large number of sensors, resulting in complex field deployment. Single-station seismic methods offer an attractive alternative by leveraging constraints among different seismic components at a single site. Nevertheless, existing single-station seismic methods do not fully utilize the complete six-component information provided by seismometers, and the extraction of dispersion curves is affected by instrument response, which limits the accuracy of single-station methods.

To overcome these limitations, we propose a single-station six-component Horizontal-to-Vertical Spectral Ratio method (6-C HVSR). This method considers both translational HVSR and rotational HVSR, enabling it to eliminate curve distortion caused by instrument response. The proposed 6-C HVSR forward model can be directly constructed using existing surface-wave forward modeling frameworks, enabling subsurface structure inversion without introducing additional assumptions. Unlike traditional HVSR methods, the 6-C HVSR applicable to subsurface structure inversion has a clearer physical meaning.

By integrating both translational and rotational HVSR, the proposed method fully utilizes all six-component data and improves constraints on subsurface structures under single-station conditions. Moreover, it reduces requirements for measurement instruments and enables further improvements in measurement accuracy. Borehole comparison experiments demonstrate that the method can estimate subsurface structures using single-station observations.

How to cite: Huang, Z., Zhou, Z., Chen, Y., and Li, Z.: Single-Station Six-Component Horizontal-to-Vertical Spectral Ratio Method for Subsurface Structure Inversion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7868, https://doi.org/10.5194/egusphere-egu26-7868, 2026.

    On 5 September 2022, an M_s6.8 earthquake occurred in Luding County, Garze Prefecture, Sichuan Province, which broke a "quiet period" of large earthquakes in the southeast section of the Xianshuihe fault and caused a major natural disaster. The seismogenic structure, seismicity and stress state in the epicenter area of the Luding earthquake plays an important role in understanding the seismogenic mechanism of strong earthquake. In this paper, based on the seismic waveform recorded from 50 short-period seismic stations deployed in the Luding area before the earthquake and the seismic travel time data collected from the regional seismic networks, we investigated high-resolution S-wave velocity structure, spatial earthquake distribution and b-value variation images of shallow crust in the Luding area before the earthquake by ambient noise tomography, double-difference relocation and improved b-value imaging method, respectively. The results show that the mainshock rupture of the Luding earthquake initiated from an asperity with high-velocity anomaly and high stress characteristics in the Moxi segment of the Xianshuihe fault. On the west side of the mainshock, we revealed a hidden normal fault resulted from the largest M5.0 aftershock, which is concomitant branch fault within Xianshuihe Fault system. The mainshock ruptured  both a dominant asperity  and another smaller southeastern asperity with high-velocity, and caused clustered aftershocks there. These results indicate that the high-velocity "rivet" structures cross fault and high stress accumulation before the earthquake in the source area controlled the occurrences of the Luding mainshock as well as strong aftershocks in general. Identifying these special "rivet" structures through high-resolution structure imaging as well as high stress situation through b-value imaging can effectively evaluate the seismogenic capacity of faults, which is of great significance to seismic hazard assessment in key areas. 

How to cite: Hua, Q., Pei, S., Xue, X., Li, L., Li, J., and Liu, H.: The seismogenic mechanism of the Luding MS6.8 earthquake revealed from preseismic S-wave velocity structure and b-value distribution of the epicenter area, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4143, https://doi.org/10.5194/egusphere-egu26-4143, 2026.

Urbanization-driven demand for high-resolution near-surface imaging and monitoring has promoted the application of distributed acoustic sensing (DAS) technology. However, DAS-based surface-wave velocity imaging faces challenges from low signal-to-noise ratios, strong lateral heterogeneity, and scale-dependent surface-wave sensitivity. To address these issues, we propose a multi-scale surface-wave inversion framework tailored for DAS observations, which inverts multi-scale and multi-mode sub-array dispersion curves simultaneously for the two-dimensional (2D) shear-wave velocity model. The method integrates three core technical components: multi-scale overlapping sub-array selection, frequency-Bessel (F-J) transform-based dispersion extraction (enabling reliable capture of both fundamental and higher-mode surface-wave energy), and a Poisson-Voronoi (PV) tessellation inversion strategy for dimensionality reduction. By integrating dispersion information across multiple frequency bands and multiple modes via multi-scale sub-arrays, the framework achieves complementary sensitivity to shallow and deeper subsurface structures. The PV tessellation stabilizes the inversion and avoids artificial lateral velocity variations inherent in conventional 1D inversion approaches. Synthetic tests confirm the method’s ability to reliably recover low- and high-velocity anomalies with improved lateral continuity and depth resolution. Application to urban DAS ambient noise data from Hefei, China, yields a geologically plausible 2D shear-wave velocity model. This study provides a robust methodological foundation for high-resolution near-surface imaging in complex urban environments using DAS technology.

How to cite: Zou, H. and Yao, H.: A Multi-Scale Poisson-Voronoi Inversion Framework with Joint Multi-Mode Surface Wave Dispersion for DAS-Based Near-Surface Imaging, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7597, https://doi.org/10.5194/egusphere-egu26-7597, 2026.

Two developments of this century have allowed for a greatly increased potential for monitoring of the near surface in geotechnical applications. First, Distributed Acoustic Sensing (DAS) allows glass fibre cables attached to an interrogator to be utilised for sensing seismic vibrations with dense spatial sampling. This allows for robust, permanent recording installations that cover large spreads. 

Second, the theory of seismic interferometry shows that with the use of ambient noise, under certain conditions, any recorded trace can be turned into a virtual-source position. In the most common application, the same event, recorded at multiple positions that share a travelpath, must be crosscorrelated to obtain a virtual shot. For the full response, this must be repeated for all sources on a surface effectively surrounding the medium of interest and the results stacked.

The combination of DAS and seismic interferometry for real-time monitoring require large amounts of passive data to be collected. This does mean that subsequent processing workflows have to be adapted according to computational capabilities. Even for relatively simple workflows, high-performance computing concepts must be applied to keep processing speed aligned with data collection.

Given the large amounts of recorded data, it becomes tempting to adopt the mindset that better results are obtained by simply stacking more data. However, for seismic interferometry, a proper selection of useful noise is essential in retrieving good results.

One of the proposed monitoring applications of seismic interferometry on DAS data is for monitoring the subsurface under railway lines. The shear modulus is used to monitor the strength of the soil. As such, surface-wave analysis methods are the seismic investigation method of choice. The advantage of monitoring close to active railway lines is that passing trains provide strong noise sources. When a train passes directly past the sensors, the wavefield is very complex, but waves generated by the train propagate both backwards and ahead. These waves can be used for seismic interferometry. As different trains generate different source spectra for the wavefield, multiple different trains must be included in the data and stacked after seismic interferometry to obtain a broader frequency band.

The dataset that we use is from an 8-km-long straight section of DAS cable along a rail line between Rotterdam and Delft in the Netherlands. We estimate the passage of a train along the DAS line with the envelope of the energy of the data. Then, we select windows ahead and behind the train that capture the generated waves. As the location of the train is known, we can use the trace closest to the train as a master trace and only the causal parts of the result are summed with the total stack. Finally, the dispersion spectrum is computed from the virtual shots to extract dispersion information along the line.

Together with intermediate results, we show that consideration of the noise sources that are present and how to utilise these leads to improved results. This requires more preprocessing but also finally decreases the amount of data that must be crosscorrelated.

How to cite: Hassing, S., Draganov, D., Verschuur, E., van 't Schip, J., Duijm, E., Crone, S., and Mas, C.-J.: Extracting dispersion characteristics of the subsurface under a railway line from passively recorded DAS data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18453, https://doi.org/10.5194/egusphere-egu26-18453, 2026.

The Weisweiler region in North Rhine-Westphalia, Germany, offers promising potential for geothermal energy use. The respective geology in the Carboniferous and Devonian is rather unexplored, so seismological and geological exploration of the region was started recently. To create a shallow seismic velocity profile with high depth resolution that can serve as a basis for further research, we used a fiber optic cable in a 500 m deep exploration well in the center of the area to perform a vertical seismic profiling (VSP) campaign.

A VSP campaign with four dropped weight shot points was carried out in which we recorded the deformation along the cable caused by the seismic waves with a distributed acoustic sensing (DAS) interrogator. With a spatial resolution of approximately 0.8 m along the cable, we were able to resolve a total of 618 depth-averaged measurement points over the 500 m borehole depth. The gauge length was set to 10 m.

Interval velocity profiles were determined by manually detecting wave arrivals. P wave velocities were compared to sonic log velocities. In some depth sections, shear wave arrivals could be identified and shear wave velocity profiles and v_P/v_S ratios could be derived.

We show the measurement setup and processing steps, as well as the processed data, and present the resulting velocity profiles in comparison to previously available data sets of the region. Here, the measurement methodology also reveals its limitations, as the strain per measurement point in DAS is measured over a depth range of one gauge length, which limits the depth resolution. Nevertheless, the results correspond very well with previously known geological models and also coincide with the sonic log, while supplementing previous findings with a S wave velocity profile and a v_P/v_S ratio.

How to cite: Dietl, M., Finger, C., Reinsch, T., Hohage, S., Ritzmann, O., and Oswald, T.: Vertical seismic profiling using distributed acoustic sensing in Weisweiler, Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11326, https://doi.org/10.5194/egusphere-egu26-11326, 2026.

Drilling operations can unexpectedly encounter hard stringers-thin, high-strength inclusions/rock layers in a softer background-which cause drilling problems including stick-slip vibrations, near-bit inclination changes, and severe damage to drill-bit and bottomhole assembly (BHA), all leading to inefficient and costly drilling. Early detection of hard stringers allows drilling personnel to adjust parameters proactively, enhancing operational stability. In this study, we propose a methodology referred to as ahead-of-the-bit prediction (ABP) using drill-bit-generated noise as a seismic source and BHA-mounted sensors for cross-correlation analysis of recorded signals. We compute sensitivity kernels in a realistic borehole environment to identify the contributions from direct arrivals, stringer reflections, mud-induced guided waves, and to better understand the physics of the elastic wavefield. The results from this work will enable further development of our methodology for real-time early detection of hard stringers during drilling.

How to cite: Ileri, S., Orsvuran, R., Pavlov, A., and Sangesland, S.: Using sensitivity kernels in realistic borehole conditions for informing ahead-of-the-bit prediction of hard stringers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12921, https://doi.org/10.5194/egusphere-egu26-12921, 2026.