We invite submissions spanning the full spectrum of near-surface geophysical imaging, from methodological innovation to diverse applications at different scales. Contributions on combining complementary methods, machine learning, and process monitoring are particularly encouraged.
Orals: Fri, 8 May, 14:00–17:55 | Room D2
Hydrothermal eruptions are a type of volcanic explosion that are less well known but not less important. They constitute a group of eruptions where no magma is expelled at the surface, and are characterized by the ejection of liquids and gasses, and possible fragments of host rock. Compared to the better-known, magmatic eruption, classic pre-eruptive signals like increased seismicity and ground deformation are not clearly present, making hydrothermal eruptions very unpredictable and hence extremely destructive. Some pre-eruptive signals have been defined, but they are very case-specific, and they can be inconsistent between eruptions. Hence, we want to explore the feasibility of geo-electrics as an additional tool to understand the dynamics of hydrothermal eruptions and better predict them in the future. Specifically, we explore the potential of Electrical Resistivity Tomography (ERT) because of its sensitivity to temperature and saturation, the main parameters we expect to change prior to an eruption. Since volcanic processes can span over long time periods, we consider geysers as a natural laboratory to study hydrothermal eruptions. In this context, a geyser is essentially a mini-volcano that goes through a repeated cycle of boiling, gas accumulation, and over-pressurisation culminating in an eruption. Monitoring this with ERT contains some significant challenges compared to the slow-changing volcanic systems; the eruption cycle can be as short as a few minutes (e.g. Strokkur, Iceland), can take up to an hour (El Tatio Geysers, Chile), or even multiple hours to a few days (Yellowstone Geysers, USA).
Here we present data from two geyser monitoring campaigns: Strokkur, Iceland, and El Tatio, Chile, constituting a wide range of eruption dynamics. The main goal of our study is to capture the changes in temperature and saturation using ERT. From a monitoring perspective, each phase of the eruptive cycle needs to be imaged sufficiently to capture the system dynamics. Since a single ERT measurement can be time intensive, measurement protocols had to be designed that weigh time and resolution in an appropriate way tailored to the specific field conditions. We performed characterisation and monitoring using different configurations, including a traditional linear array and novel (concentric) circular arrays. The reservoir geometry can be well constrained due to a high contrast in temperature and salinity of the geothermal fluids and the surrounding host rock. Changes in the monitoring data are hypothesised to be related to the saturation and thus filling and emptying of the shallow reservoirs. To the author's knowledge, this is the first study using ERT to monitor geyser dynamics with a high temporal resolution. Survey design remains an obstacle due to tough meteorological conditions and quick subsurface dynamics, but the first results show there is great potential for ERT as a geyser, and by extension volcano monitoring tool.
How to cite: Vanhooren, L., Caudron, C., and Hermans, T.: High Resolution Geyser Monitoring using Electrical Resistivity Tomography (ERT) – cases from Chile (El Tatio) and Iceland (Strokkur) , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3948, https://doi.org/10.5194/egusphere-egu26-3948, 2026.
Probabilistic geophysical inversion methods increasingly provide ensembles of subsurface physical property models, offering valuable insight into data-driven uncertainty. However, the geological interpretation of such inversion results remains challenging, as uncertainty is typically quantified in terms of the physical parameter space rather than in terms of geological structure. Translating probabilistic inversion outcomes into consistent geological models is therefore still often performed in an ad hoc or deterministic manner.
We present the probabilistic data assimilation framework GeoBUS for the geological interpretation of geophysical inversion results. GeoBUS operates on scalar-field-based implicit geological models and treats geological structures as uncertain quantities that can be updated using observational constraints. The framework is independent of the specific geophysical inversion algorithm used and can assimilate probabilistic inversion results alongside other sources of geological information.
We test GeoBUS using a synthetic case study. A reference geological model is defined and used to generate corresponding electrical resistivity tomography data, which are subsequently inverted using a probabilistic inversion scheme to obtain an ensemble of resistivity models. These inversion results are then assimilated in GeoBUS through petrophysical consistency relationships, yielding posterior ensembles of geological scalar fields that can be directly compared to the known reference model for validation of the workflow.
In a second step, we extend the study by sequentially assimilating additional geological information. In this example, borehole interface depths are incorporated to illustrate how GeoBUS naturally accommodates heterogeneous observations and progressively reduces structural uncertainty. This demonstrates the flexibility of the framework and its potential for bridging the gap between probabilistic geophysical inversion and geological modeling in applied geophysics.
How to cite: Bobe, C., von Harten, J., and Wellmann, F.: GeoBUS: A Probabilistic Data Assimilation Framework for Geological Interpretation of Geophysical Inversion Results, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19498, https://doi.org/10.5194/egusphere-egu26-19498, 2026.
The subsurface is complex and heterogeneous, making its investigation very challenging. In this context, geophysical imaging methods can provide new insights on the spatial distribution of Earth physical properties. However, the imaging capacity of geophysical methods is limited by their indirect nature and the non-unicity of the solution of the inverse problem. In other words, the interpretation of geophysical data sets remains limited by their intrinsic uncertainty. On the one hand, deterministic methods fail to properly account for uncertainty. On the other hands, probabilistic approaches allowing uncertainty quantification, such as Markov chain Monte Carlo (McMC) methods, are both time-consuming and difficult to tune to convergence in complex subsurface systems with many parameters. Some alternatives, such as Bayesian Evidential Learning (BEL), providing an approximation of the posterior distribution have been proposed. BEL learns a statistical relationship between data and model parameters from a training set sampled from the prior distribution. This prevents the use of forward models during the prediction of the posterior distribution. The applications of probabilistic approaches to geophysical imaging often supposes simplifications in the distribution of model parameters to reduce the number of parameters.
In this contribution, we acknowledge that geophysical imaging is often not the objective of geophysical data acquisition. Geologists are often more interested in some specific features such as the depth of the bedrock, the location and geometry of a fault, or the spatial variability of the fresh-saltwater interface. We therefore define the prior distribution of model parameters in a hierarchical way, where the feature of interest is defined first with hyperparameters, explicitly included during posterior inference. This approach allows to decouple the imaging process from any pre-defined inversion grid. We use BEL to calculate the posterior distribution. To deal with the strong non-linearity of the data-model relationship, we use a mixture-density network with two hidden layers allowing to estimate the posterior distribution of model parameters.
We demonstrate the approach on a synthetic electrical resistivity tomography (ERT) example in a saline context. The fresh-saltwater interface is characterized using a third degree polynomial (4 parameters) separating a saltwater aquifer from an overlying freshwater lens, both with uncertain electrical resistivity. 10000 models are sampled from the prior distribution to train the BEL-MDN model between the ERT pseudo-section and the model parameters. PyGimLi is used to solve the ERT forward problem. During MDN training, the first epochs use noise free data; noisy data are only introduced later in the training process, allowing to maximize learning efficiency. Comparison with McMC shows that BEL-MDN is successful in identifying the depth and shape of the interface at a fraction of the cost of McMC. However, BEL-MDN tends to overestimate the uncertainty when the interface lies at shallow depth, which requires further research. The method holds great potential to image specific (hydro-)geological features, especially for complex cases where McMC are too computationally expensive.
How to cite: Hermans, T. and Mejer Hansen, T.: Fast stochastic inversion of geological interfaces from geophysical data using Bayesian Evidential Learning with Mixture Density Network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11676, https://doi.org/10.5194/egusphere-egu26-11676, 2026.
Electrical Resistivity Tomography (ERT) is widely used in archaeo-geophysics due to its capability to non-invasively detect buried structures and stratigraphic heterogeneities. However, when ERT is applied to shallow, small-scale archaeological targets requiring high spatial resolution, the finite geometry of electrodes and the reduced inter electrodes distance can introduce systematic errors that may be misinterpreted as cultural features. This study investigates the impact of the electrode spacing to diameter ratio (a/φ) on apparent resistivity measurements, with particular attention to conditions typical of archaeological prospection, where electrode spacing is often limited by site constraints and preservation requirements. Six electrode types with diameters ranging from 4 to 16 mm were tested using dipole–dipole and pole–dipole arrays at four electrode spacings (10, 30, 50, and 100 cm), generating 48 ERT datasets acquired over a mainly homogeneous test area, characterised by the presence of a higher resistive target. The analysis focused on apparent resistivity values to avoid uncertainties introduced by inversion procedures.
Results demonstrate that electrode material does not significantly influence resistivity measurements; instead, geometric factors dominate. In particular, when a/φ il lower than 25 (φ/a ≥ 4%), corresponding to dense electrode setups frequently used in archaeological ERT, apparent resistivity is strongly affected, particularly at shallow depths and in the presence of resistive anomalies (e.g., conditions typical of buildings, voids, pavements, foundation remains, or anthropogenic stratigraphy). When a/φ is higher than 31.5 (φ/a ≤ 3.2%), electrode diameter has negligible impact, confirming that standard ERT configurations at meter-scale spacing are generally robust. Increased variability and systematic deviations were observed up to approximately three times the electrode spacing, potentially generating artificial resistive highs that could be erroneously interpreted as archaeological features. Intermediate spacing values (30–50 cm) show transitional behaviour, with distortions decreasing progressively with depth.
The study provides an operational framework for archaeo-geophysical practice: (1) electrode diameter becomes critical when small electrode spacing is required; (2) reliable shallow imaging in archaeological contexts demands maintaining sufficiently high a/φ ratios or explicitly modelling electrode geometry; and (3) resistive archaeological targets are most susceptible to artifact generation under inadequate a/φ conditions. These findings support improved survey design, more reliable interpretation of near-surface ERT data, and reduced risk of false positives in cultural heritage investigations.
How to cite: Innocenti, A., Patrizi, G., Ciani, L., and Pazzi, V.: The influence of the Electrodes-Spacing-to-Diameter-Ratio (ES2DR) on ERT measurements: an operational approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9460, https://doi.org/10.5194/egusphere-egu26-9460, 2026.
Deterministic inversion of electromagnetic (EM) data yields a single best fitting resistivity model of the subsurface, which can be used to interpret the geological subsurface. Such a model fails to capture the uncertainty in both resistivity and geology. This limitation is critical, as multiple, geologically dissimilar subsurface configurations can yield equivalent EM responses, meaning a single model representation can be inaccurate or even misleading. Probabilistic inversion of the EM data provides a principled solution by characterizing the range of subsurface models consistent with data, and thereby explicitly quantifying uncertainty in both the geophysical and geological models.
Here we invert by rejection sampling of pre-computed geophysical and geological 1D prior models. This allows for fast and efficient probabilistic inversion of large-scale EM surveys containing thousands of soundings. An added benefit of using pre-computed geological models is the possibility to encode geological expert knowledge into the models as direct information. In this context expert knowledge can be many things, for example the resistivity-lithology relationship, the chronological sequence of geological units, or the relative occurrence of various lithologies to name a few.
In the presentation, we demonstrate how such a probabilistic inversion workflow can be set up and applied on towed transient EM data from geophysical surveys in varying geological settings. The required inputs are (i) a geophysical dataset consisting of EM soundings, and (ii) an expert-based assessment of the plausible geological subsurface architectures in the survey area. Optionally, geophysical and lithological well logging can be used to further constrain the inversion. We will highlight the tool/software (GeoPrior1D) we have developed to construct prior ensembles with encoded geological knowledge, especially suited for such a workflow. GeoPrior1D is an open-source tool for generating ensembles of one-dimensional geological and geophysical models that explicitly represent prior models for probabilistic inversion problems.
Finally, we present key outcomes of the probabilistic modelling. This includes resistivity models with uncertainty, lithological models with uncertainty (entropy), class probabilities, and various themed maps. The produced models and maps, always accompanied by rigorously quantified uncertainties, enable better and more reliable decision-making across applications such as geohazard risk assessment, resource volume estimates, groundwater modelling, and much more.
How to cite: Nørgaard, J., Hansen, T. M., Madsen, R. B., Møller, I., and Høyer, A.-S. and the INTEGRATE working group: Probabilistic modelling and mapping with electromagnetic data using pre-computed geological look-up tables as prior information, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7561, https://doi.org/10.5194/egusphere-egu26-7561, 2026.
Groundwater modelling relies on three-dimensional (3D) geological models as structural input to predict subsurface processes such as groundwater flow and contaminant transport. However, model uncertainty in the geological domain, originating from sparse data coverage and the inherent non-uniqueness of geophysical inverse problems, propagates into hydrological predictions and affects the model outcome. Accounting for this uncertainty is therefore essential. This requires methods that not only characterize the subsurface but also quantify and propagate uncertainty through the entire modelling workflow.
Probabilistic inversion of transient electromagnetic (TEM) data addresses the non-uniqueness of the inverse problem by yielding posterior samples containing hundreds of plausible one-dimensional (1D) models at each measurement location. This captures the range of subsurface structures consistent with the geophysical data and enables quantitative assessment of subsurface uncertainty. However, a critical challenge emerges: How do we transform these independent 1D posterior models into spatially coherent 3D subsurface realizations that preserve geological continuity? A simple approach would be to use the mode model, showing the most probable values. However, the mode is merely a statistical summary of the posterior, not an actual sample from it, and does not capture the uncertainty in the subsurface structure either. Alternatively, randomly selecting one posterior model at each location would result in geologically implausible 3D realizations due to lacking spatial structure and lateral correlation. Generating multiple internally consistent realizations is essential to capture the full range of plausible subsurface scenarios and quantify uncertainty. Yet, no standard algorithm currently exists to generate 3D realizations that both sample the posterior distribution and ensure geological continuity.
Existing geostatistical simulation methods are not well suited for this task. Gaussian-based approaches (e.g., sequential indicator simulation) cannot fully exploit the non-Gaussian posterior distributions from probabilistic inversion. Multiple-point statistical methods require training images that are difficult to obtain and may conflict with the prior information used in the inversion.
Here, we present a novel geostatistical simulation algorithm that generates spatially coherent 3D subsurface realizations directly from independent 1D posterior models. The algorithm directly combines 1D posterior realizations at data locations with 1D prior realizations elsewhere, using spatial correlation to generate coherent 3D structures without Gaussian assumptions or training images. We demonstrate the method using a TEM dataset, showing that the resulting realizations reproduce realistic spatial geological patterns and variability consistent with the underlying posterior information. The algorithm is computationally efficient, enabling generation of multiple realizations that in combination quantify subsurface uncertainty and provide a direct basis for propagating geological uncertainty into hydrological flow and transport simulations.
How to cite: Nielsen, S., Bødker Madsen, R., Damsgaard, A., Mejer Hansen, T., Lajer Højberg, A., Vincent Henri, C., Hansen, B., Kim, H., Nørgaard, J., and Møller, I.: From 1D Independence to 3D Coherence: Geostatistical Simulation of Probabilistic TEM Inversions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9161, https://doi.org/10.5194/egusphere-egu26-9161, 2026.
Systematic modeling errors arising from incomplete forward modeling theory in transient electromagnetic (TEM) inversion, such as using a 1D forward model to interpret data from inherently 3D subsurface structures, bias inversion results. It is difficult to identify these errors as erroneous 1D models often fit the data within the assumed noise level. A solution is to perform the inversion in a full 3D framework. However, 3D inversion is constrained by several challenges, such as a high demand for computational resources and increased regularization requirements. These challenges typically result in simplistic or smooth inversion models, the exclusion of probabilistic approaches, and an inability to handle complex prior information. We present an algorithm that iteratively refines a nonlinear estimate of the 3D modeling error, enabling the continued use of flexible 1D TEM inversion schemes, such as Bayesian inversion with complex priors, even in the presence of 3D effects. The algorithm iteratively refines an estimate of the error by projecting the inversion model onto a coarse 3D mesh and simulating a 3D response. By simulating data for the corresponding laterally homogenous (1D) model, the 3D error can be estimated and used to correct the data. We test the algorithm on synthetic 3D TEM data, inverted using a 1D probabilistic framework while using the median posterior model for the error estimate. We also present a test on a real airborne TEM dataset from Denmark, and in both synthetic and real tests we use the residual between the observed data and the 3D response of the projected inversion model as a quantitative performance measure. The results show that the algorithm consistently improves the agreement between observed and simulated 3D data while also either removing or significantly dampening 3D artifacts in the final 1D inversion model. This iterative approach provides a solution that is otherwise typically provided by full 3D inversion, while preserving the advantages of 1D frameworks and with promising implications for improved interpretability of 3D structures in 1D inversion frameworks.
How to cite: Falk, F. A., Hansen, T. M., and Christiansen, A. V.: An iterative algorithm for estimating and accounting for 3D TEM modeling errors in 1D inversion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18528, https://doi.org/10.5194/egusphere-egu26-18528, 2026.
Historical unexploded ordnance (UXO) contamination is a widespread environmental challenge, leading to human casualties and chemical contamination. Electromagnetic induction (EMI) methods are commonly used to detect unexploded ordnance in both terrestrial and marine settings. Using traditional advanced geophysical classification, UXOs can be discriminated from other metallic clutter via a physics-based inversion approach that matches obtained polarizability curves from EMI data with a library of common UXO polarizabilities. This workflow requires identifying dipoles in the acquired dataset. Non-dipolar anomalies can complicate the identification of targets of interest. Some geological conditions, for example, in areas with strong magnetic soil responses and areas with metallic clutter, make it hard to discriminate between dipolar and non-dipolar anomalies.
In this work, we build on our previously developed convolutional neural network (CNN) that classifies UXO directly from EMI data [1]. Our CNN outputs a probability map that preserves the spatial dimensions of the input. We train the CNN using synthetic data generated with a dipole forward model that considers relevant UXO and clutter objects, and train it to discriminate those dipoles in field data. A key novelty is the interplay between (1) training the CNN to handle the expected noise levels in the field and (2) transferring the CNN to field sites with potential new or "unseen" types of (geological) noise. We demonstrate test procedures required to build trust in machine learning approaches for UXO classification, where false negatives can have a significant impact.
[1] Heagy, L., Lopez-Alvis, J., Oldenburg, D., Song, L.-P., and Billings, S.: Using convolutional neural networks to classify unexploded ordnance from multicomponent electromagnetic induction data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13722, https://doi.org/10.5194/egusphere-egu25-13722, 2025.
How to cite: Deleersnyder, W., Lopez-Alvis, J., Beran, L., and Heagy, L.: Discriminating dipole signals from geologically noisy electromagnetic induction data with convolutional neural networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3956, https://doi.org/10.5194/egusphere-egu26-3956, 2026.
Among the different near-surface geophysical methods, ground-penetrating radar (GPR) is particularly promising for investigating small-scale (centimeters to meters) and fast (seconds to minutes) subsurface flow processes. Technical developments in GPR systems and data acquisition, such as real-time signal digitization, real-time positioning techniques, and multichannel GPR systems, enable repeated 2D or 3D GPR measurements (2D/3D GPR monitoring) in various environments with a temporal resolution on orders of minutes and a spatial resolution of centimeters to decimeters. Another advantage of GPR is the ability to link temporal changes in the GPR signal to variations in soil water content, because the propagation velocity of the GPR signal depends on the dielectric permittivity and, thus, water content.
However, GPR monitoring experiments must be carefully designed to collect high-quality datasets. The experimental setup must provide accurate positioning, consistent high spatial and temporal sampling, and minimal variations in GPR antenna coupling. Furthermore, following a special data processing and data quality analyses schedule is important to obtain reliable interpretation results. Such a data analysis must focus on identifying, evaluating, and suppressing amplitude fluctuations and time shifts in the recorded GPR data that are unrelated to changes in the subsurface (time-lapse noise).
To provide a GPR monitoring strategy that incorporates all the aforementioned points, we present two field examples of GPR monitoring in combination with irrigation experiments to image subsurface flow processes: a 2D hill-slope scale experiment and 3D plot-scale experiment. These examples demonstrate our general measurement setup and schedule for repeatable GPR data collection and data analysis, as well as a first-order, attribute-based data interpretation. We also highlight important practical points to consider for performing and analyzing such GPR monitoring experiments.
Our field examples demonstrate the great potential of GPR monitoring to image and investigate subsurface flow processes. We also provide a practical guide for successfully performing GPR monitoring experiments to promote the application of GPR monitoring to study hydrological subsurface processes.
How to cite: Stephan, S., Jackisch, C., Allroggen, N., and Tronicke, J.: Imaging small-scale, fast subsurface flow processes: field examples and practical guidelines for GPR monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17678, https://doi.org/10.5194/egusphere-egu26-17678, 2026.
Rigid boom frequency-domain electromagnetic induction (FD-EMI) sensors based on a double magnetic dipole (loop–loop) geometry allows to rapidly characterize subsurface electrical and magnetic properties. Recent advances in instrumentation (including multi-configuration and multi-channel systems), high-resolution kinematic acquisition strategies, and fast 3D inversion algorithms allows the reconstruction of 3D subsurface models of electrical and magnetic properties with unprecedented detail.
In this study, we evaluate the potential of FD-EMI in-phase data for high-resolution 3D reconstruction of magnetic susceptibility (or permeability) using the recently developed 3D multi-channel deconvolution (MCD) approach. We tested the 3D MCD method on multiple data sets acquired in diverse igneous environments and with different FD-EMI systems in the context of archaeological prospection. Compared to conventional qualitative interpretation of FD-EMI in-phase data maps, the 3D MCD method significantly enhances the interpretability of the data by (1) enabling clear separation of subsurface features at different depth levels, (2) significantly improving lateral resolution and (3) revealing archaeological structures that remain invisible in the original measurements. These results highlight MCD as a key processing step that unlocks the full imaging potential of FD-EMI in-phase data.
How to cite: Guillemoteau, J., De Smedt, P., Simon, F.-X., Vauthier, A., Tronicke, J., and Dousteyssier, B.: 3D magnetic susceptibility imaging using EMI in-phase data: selected 3D inversion examples using small- and large-scale data sets, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14747, https://doi.org/10.5194/egusphere-egu26-14747, 2026.
We applied an integrated drone- and ground-based magnetometry workflow to map shallow subsurface fractures and cavities inside the town of Grindavík, during the 2023-2025 volcano-tectonic crisis on the Reykjanes Peninsula SW Iceland. Since10 November 2023 a total of 12 dike intrusions have occurred under the Sundhnúkur crater row from a shallow magma reservoir at Svartsengi, 9 of which resulted in fissure eruptions. Three of these dikes propagated underneath the town of Grindavík triggering widespread fault reactivation, fracturing, and surface deformation. Two main grabens formed above intrusions in the west and east of the town, with a maximum measured vertical displacement of 1.5 m. Short-wavelength linear and elliptical magnetic lows delineated open or partially open fractures and localized cavities hidden beneath the surface. These open fractures and cavities or void spaces were then verified with field observations, LiDAR surface deformation, and targeted shallow excavations. Comparison with historical aerial photographs indicates several anomalies correspond to reactivated, and further opened, pre-existing fractures along the main graben that cuts through the town. This integrated drone and ground-based approach enabled rapid mapping of the major fracture networks in inaccessible terrain, maintaining operator safety. In turn this guided near-real time hazard assessments, and supported stakeholder decision-making, by revealing fracture continuity, areas of sinkhole development within the fracture lineaments, as well as aperture variability and branching patterns along the fractures. Forward magnetic modelling shows that the anomaly shapes and amplitudes are compatible with fracture and void sources within the upper ~10-20 m of bedrock. This study demonstrates the first combined application of drone and ground magnetometry for rapid real-time fracture mapping in an urban post-volcano-tectonic crisis event setting which has affected the >3,700 local residents of the town of Grindavík.
How to cite: Piispa, E., Gallagher, C., Bernholt, S., Einarsson, G., Erlendsson, Ö., Karlsdóttir, K., Sigurgeirsson, M., Askew, R., Ben-Yehoshua, D., Óskarsson, B., Gunnarson, S., Guðmundsson, M. T., and Björnsson, G.: Rapid drone and ground magnetic mapping of subsurface fractures during a volcano-tectonic crisis in Grindavík, on the Reykjanes Peninsula SW Iceland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21694, https://doi.org/10.5194/egusphere-egu26-21694, 2026.
Effective groundwater exploration is fundamental to the identification and assessment of subsurface water resources, particularly in challenging geological conditions. In such cases, conventional drilling-based approaches are costly and provide only limited one-dimensional information for characterizing the three-dimensional subsurface. Hydrogeophysical techniques offer efficient, non-invasive means of imaging the subsurface and can significantly reduce the need for extensive drilling campaigns. Among these, Electrical Resistivity Tomography (ERT) is one of the most widely used geophysical methods for investigating groundwater systems. However, conventional smoothness-constrained ERT inversion often fails to resolve sharp stratigraphic boundaries or represent internal heterogeneity, limiting its effectiveness in complex geological settings. Structural and geostatistical constraints have each been proposed as enhancements, but when applied separately, they often struggle to simultaneously achieve structural accuracy and inversion stability.
We introduce a novel inversion strategy that integrates seismic-derived structural horizons with unit-specific geostatistical constraints within the open-source PyGIMLi framework. This approach balances structural alignment and internal heterogeneity by enforcing sharp boundaries and applying region-specific spatial continuity models.
This hybrid strategy is evaluated at two glacially influenced groundwater study sites in northern Germany, where complex Quaternary deposits include interbedded sands, tills, and clays. Results demonstrate enhanced delineation of aquifers and aquitards, improved agreement with borehole resistivity logs, and a reduction in inversion artifacts such as over-smoothing or artificial layering. Compared to conventional and single-constraint inversions, the integrated method more effectively resolves thin confining units, anthropogenic disturbances, and laterally variable aquifer geometries with enhanced structural clarity.
This framework offers a transferable solution for hydrogeophysical characterization in heterogeneous environments, particularly where seismic or borehole data are available to guide inversion.
How to cite: Alaei, N., Günther, T., Eckardt, T., Stiller, B., Scheihing, K., Pechnig, R., and Gabriel, G.: A Novel Structural Geostatistical Constrained ERT Approach for Hydrogeophysical Characterization in Glacial Sedimentary settings, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16753, https://doi.org/10.5194/egusphere-egu26-16753, 2026.
The long-term stability and reactivation potential of large landslides are primarily controlled by their deep internal structure, the geometry and connectivity of shear planes, fault systems, and subsurface hydraulic pathways. Despite their importance, these features remain difficult to investigate, and geophysical methodologies capable of resolving those at sufficient resolution and depth are still not fully established. This study addresses this challenge through a comprehensive, multi-method geophysical investigation of the Cisiec landslide, located in the Żywiec district of southern Poland.
The study area comprises a forested clearing surrounded by meadow terrain, with the landslide moving predominantly east–northeast and exhibiting an elevation difference of approximately 100 m between its crown and toe. Annual monitoring campaigns, including drone-based photogrammetry and laser scanning, as well as geophysical measurements like seismic or resistivity tomography, conducted between 2018 and 2022, provided valuable insights into the general geometry and kinematics of the landslide. Still, process-understanding of the complex and non-linear landslide behaviour could not be fully obtained. These datasets allowed the construction of a preliminary structural models and indicated temporal variability in displacement patterns; however, the nature of movement along individual slip surfaces remained unresolved. In particular, it was unclear whether deformation occurred as a coherent, uniform displacement or as a progressive, sequential sliding process involving multiple layers or discrete blocks. Furthermore, the role of groundwater circulation within the landslide body and its influence on mechanical stability could not be conclusively determined.
To overcome these limitations, we conducted advanced geophysical surveys during dedicated field campaigns in October 2024 and April 2025. The investigation integrated high-resolution seismic imaging based on Distributed Acoustic Sensing (DAS) with Spectral Ground Penetrating Radar (SGPR) and a suite of electrical and electromagnetic methods, including Electrical Resistivity Tomography (ERT), Frequency Domain Electromagnetics (FDEM), Time Domain Electromagnetic (TDEM) soundings, and Spectral Induced Polarization (SIP). Each technique was carefully selected and optimized to resolve complementary aspects of the landslide architecture, ranging from shallow deformation features to deeper structural controls and subsurface hydraulic pathways.
The combined dataset provided a detailed, multi-scale image of the landslide, revealing significant spatial heterogeneity in both mechanical and hydrogeological properties. Seismic imaging resolved fine-scale structural and geomechanical variations, while electrical and electromagnetic methods highlighted zones of enhanced moisture content and groundwater flow. The results confirm a division of the landslide into three distinct kinematic zones and reveal previously unresolved shallow slip surfaces and groundwater-related effects. Notable observations include focused groundwater discharge at the lower slope and an anomalous signal attenuation zone near the crown, interpreted as evidence of microseismic activity and the development of a potential new failure zone. These findings demonstrate the value of integrated, high-resolution geophysical approaches for improving conceptual models of complex landslide systems and for supporting long-term hazard assessment.
How to cite: Marciniak, A., Oryński, S., Kowalczyk, S., Flores-Orozco, A., Aigner, L., Górszczyk, A., Gajek, W., Uhlemann, S., Cader-Marciniak, J., Roshdy, E., Karamuz, E., Nawrot, A., and Majdański, M.: Investigating Landslide Behaviour Under Varying Environmental Pressure: A Multi‑Method Geophysical Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13360, https://doi.org/10.5194/egusphere-egu26-13360, 2026.
Ponds are small water bodies that play a crucial role in biodiversity conservation and act as key elements of blue landscape connectivity. Under increasing climate pressures, groundwater-dependent ponds can serve as ecological shelters for wildlife, livestock, and agriculture. Understanding their origin and hydrodynamics is essential for improving their management and protection strategies.
This study focuses on an aquifer–pond system located on the granite pediment of the South Albera Massif (Eastern Pyrenees, NE Spain), where a clustered network of weathering basins has developed, some of them hypogenic in origin. The bedrock of the pond system is formed by the Palaeozoic basement of the Pyrenees Range: metasedimentary schists, orthogneisses, and plutonic granodiorites and tonalites, as well as leucogranite dikes. The structure of the area is dominated by the presence of NW-SE trending shear zones, dipping NE 50° to 70°.
Here, we propose a multidisciplinary approach coupling hydrogeological, geomorphological, edaphological and ecological techniques, together with near-surface geophysics, for the characterisation of the pond system, aiming at advancing knowledge on such groundwater-dependent ecosystems and their resilience under climate change scenario.
We applied Electrical Resistivity Tomography (ERT) to characterize the geo-electrical structure beneath temporary ponds and associated depressions, while Seismic Refraction Tomography (SRT) served to decipher the seismic velocity distribution in the subsurface. Independent and structurally coupled joint inversions were performed and compared to analyse the best data treatment method for detecting bedrock structure and the geometry of the pond sediments. The combination of these techniques has led to the 3D reconstruction of the subsurface pond features integrating surface and subsurface data, providing insights on its geomorphological origin and hydrodynamics. Data interpretation, inversion and geomodelling is based on opensource python-based programs and libraries such Refrapy, pyGIMLI and Gempy.
How to cite: Mencos, J., Zarroca, M., Roqué, C., Casamitjana, M., Madaula, E., Gonzalvo, G., and Menció, A.: Geophysical imaging of a groundwater-dependent pond system: a combined electrical resistivity and seismic refraction tomography approach in the South-Albera Massif (Eastern Pyrenees, Spain), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11780, https://doi.org/10.5194/egusphere-egu26-11780, 2026.
Glacio-seismology, which investigates the dynamics and processes of the cryosphere using seismic observations and methods, has strongly grown in interest over the past two decades in the context of global warming. To study the Argentière Glacier (French Alps), a dense array of 98 3-component seismic sensors was deployed in spring 2018 for 35 days. This period coincided with a temperature increase, which enhances the glacier’s seismic activity. The recordings bear the imprint of several thousand icequakes associated with ice-fracturing phenomena such as crevassing. We build a catalog of icequakes and their location using Matched Field Processing (MFP), which is a beamforming based approach.
Then, we jointly reconstruct icequake source mechanisms and the 3D glacier structure by exploiting the full waveform of the recorded 3-component data. The reconstruction relies on elastic wave modelling through numerical solution of the 3D elastodynamic equations using the Spectral Element Method (SEM). Accounting for the glacier surface topography is essential in order to correctly model surface waves, which mainly dominate the icequake data. We apply an alternating optimisation strategy that iterates between two sub-problems: estimating source mechanisms and updating glacier model parameters. The estimation of an icequake mechanism is formulated as the solution of a bi-quadratic minimisation problem depending on the moment tensor and the source wavelet. The model-parameter update is based on the application of a classical elastic Full Waveform Inversion (FWI).
This joint inversion strategy is applied to a set of selected icequakes with a high signal-to-noise ratio. We are able to reconstruct average model heterogeneities that align with the orientation of surface crevasses in several areas of the glacier. Some heterogeneities are reconstructed down to 100 m below the surface, enabling us to estimate the depth of crevasse fields surrounding the sensor network. Finally, we note a clear improvement in the reconstruction of the SH-wave in the updated model compared to what is obtained in a homogeneous medium. In the homogeneous approximation, the Rayleigh wave is reconstructed accurately, whereas the SH-wave is less well recovered. This improvement suggests that the SH-wave is strongly impacted by surface heterogeneities, more than the Rayleigh wave, and mainly drives the reconstruction of the structure. The estimated icequake source mechanisms do not appear to change significantly between the homogeneous model and the updated model obtained during the alternating strategy. This suggests a relative decoupling between source parameters and model parameters in the joint reconstruction problem, mediated by the Rayleigh and SH- waves. Such a decoupling is generally not observed in classical seismology, and therefore seems to be rather specific to the glacial context.
How to cite: Grange, A., Brossier, R., and Métivier, L.: Joint reconstruction of icequake source mechanisms and 3D glacier structure from dense seismic array data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7411, https://doi.org/10.5194/egusphere-egu26-7411, 2026.
Modern French railway network is equipped with optical fibers dedicated to telecommunication purposes, among which some remain unused. These so-called dark fibers can be exploited using Distributed Acoustic Sensing technology (DAS) to provide an effective tool for rapid assessment and long-term monitoring of site conditions along railways tracks. We present a methodology applied to a 20 km long DAS array operating under normal railway traffic conditions, highlighting the capability to perform continuous spatial analysis at kilometer scale with measurements every 5 meters. Despite the limited coupling associated with the on-conduit installation, corresponding to the standard operational conditions without any modification to the existing infrastructure, time windows selected before and after train passages allow the extraction of the resonance frequencies at each DAS channel, overcoming the low signal to noise ratio of the installation setup. Variations of resonances frequencies along the railway reflect changes in near surface soil conditions, related either to shear wave velocity or to variation in impedance contrast depth, with rapid spatial variation observed in karstic areas over only a few tens of meters. The novelty of this work lies in the use of resonances frequencies as a stable and repeatable site parameter derived from DAS data on a large scale. While this information does not quantify site amplification, they provide direct information on the frequency ranges that may be preferentially amplified. This makes them well suited for long term monitoring and for tracking temporal or spatial changes in site conditions under linear infrastructures, and for supporting future strategies to manage infrastructure evolution and time dependent variability.
How to cite: Grand, J., Stutzmann, E., and Bonilla, L.-F.: Large scale assessment of railway site conditions using broaDband resonance frequencies from DAS data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5634, https://doi.org/10.5194/egusphere-egu26-5634, 2026.
Aged railway embankments, constructed in the late 19th and early 20th centuries without modern engineering standards, are increasingly vulnerable to failure due to ageing, climate change, and rising transportation demands. Extreme weather events, particularly periods of prolonged wetting and drying, pose a significant risk to the structural resilience and serviceability of these earthworks. This study explores a novel passive monitoring framework at the Withy Bed site near London, UK, utilising Distributed Acoustic Sensing (DAS) to capture the long-term seismic response of a live railway embankment. By leveraging train-induced vibrations as a continuous ambient seismic source, we provide a non-invasive and high-resolution assessment of the slope's condition. The site is further equipped with a suite of geotechnical sensors, including volumetric water content (VWC) and suction sensors, to provide direct measurement of the embankment's internal state. We present time-lapse results derived from a 350m fibre-optic cable buried within the embankment, with processed data spanning every month of the year. The findings demonstrate a clear correlation between shear wave velocity (Vs) and geotechnical sensor data, specifically, Vs decreases during periods of high water content and low suction, reflecting a reduction in soil stiffness during wet seasons. Conversely, during dry periods, the data indicate a significant increase in Vs as the water content decreases and soil suction increases, resulting in a measurable rise in the overall stiffness of the embankment. The results show clear month-to-month changes in dispersion trends and Vs, with significant percentage decreases in Vs during wetter months and a progressive recovery of stiffness during drier periods. These temporal changes are spatially coherent along the embankment and repeatable across successive train events, demonstrating the robustness of the passive approach. The time-lapse analysis confirms that train-induced seismic waves provide sufficient energy and consistency to resolve seasonal variations in near-surface stiffness without repeated active surveys. This work demonstrates that passive DAS provides a practical, non-intrusive, and scalable solution for continuous monitoring of railway embankments, supporting the early identification of condition changes and enhancing infrastructure asset management.
How to cite: Saqlain, M., Trafford, A., Shrivastava, S., Khan, Q., and Donohue, S.: Time-Lapse Seismic Monitoring of a Railway Embankment Using Train-Induced Distributed Acoustic Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13946, https://doi.org/10.5194/egusphere-egu26-13946, 2026.
Detailed imaging of shallow architectural structures in sedimentary basins is critical for seismic hazard assessment, especially in regions with complex tectonic environments. The Handan area, located at the junction of the Taihang Mountain Piedmont Fault and the Yongnian-Cixian Fault, features significant variations in Quaternary sediment thickness and frequent seismicity. However, thick sedimentary cover often obscures basement faults, necessitating high-resolution geophysical methods for structural characterization. In this study, we utilize continuous waveform data from a dense array of 500 short-period seismometers (approx. 1 km spacing) deployed in the Handan region from August to September 2024. We present a joint investigative approach combining the Horizontal-to-Vertical Spectral Ratio (HVSR) method and higher-mode surface wave analysis. First, the HVSR method was employed to extract fundamental resonance frequencies (f0), which reveal a strong spatial correlation with local topographic and tectonic features. Second, we implemented the subarray-based frequency-Bessel (F-J) transform method to extract higher-mode Rayleigh wave dispersion curves from ambient noise cross-correlation functions. The inclusion of higher modes significantly enhances the imaging resolution and provides stronger constraints on the velocity structure compared to fundamental-mode methods. By inverting these dispersion curves, we obtained a high-resolution 3D S-wave velocity (Vs) model extending to a depth of 1.5 km. The results reveal pronounced velocity contrasts across major fault zones, particularly the Taihang Piedmont Fault. Furthermore, by integrating the f0 data with the Vs model, we derived a precise regional sedimentary thickness map. The estimated thickness ranges from tens of meters in the western mountainous areas to over 800 meters in the eastern basins, aligning well with existing borehole data and geological frameworks. These findings provide quantifiable constraints for earthquake hazard assessment, urban planning, and the identification of concealed faults in the North China Plain.
How to cite: Zhang, Y., Miao, W., Li, Y., and Wang, X.: Unveiling sedimentary architecture and concealed faults in the Handan region through HVSR and high-mode surface wave analysis based on a dense array, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2654, https://doi.org/10.5194/egusphere-egu26-2654, 2026.
Dispersion imaging is a key step in Multichannel Analysis of Surface Waves (MASW) for estimating the shear-wave velocity structure of the subsurface. High-resolution linear Radon transform (HRLRT) was introduced to improve spectral resolution; however, it is known to suffer from model-incompatibility and near-field effects. In this study, we show that even in the absence of near-field effects, the inner and outer iterative structure of HRLRT systematically modifies the low-frequency portion of the dispersion image. While high-frequency spectral energy in the wavenumber domain is improved, the low-frequency energy is distorted, leading to a shift in the extracted dispersion curves when compared with beamforming and other wavefield-transformation methods. This behaviour can introduce bias in phase-velocity picking and subsequently in shear-wave velocity inversion, particularly for deep layers controlled by low-frequency data. Our results highlight a trade-off between resolution and physical fidelity when using HRLRT for MASW dispersion analysis.
How to cite: Adari, S. and Naskar, T.: When High Resolution Goes Wrong: Low-Frequency Distortion in Linear Radon-Based Dispersion Imaging, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4390, https://doi.org/10.5194/egusphere-egu26-4390, 2026.
Posters on site: Fri, 8 May, 10:45–12:30 | Hall X1
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.
Long-term monitoring of slopes is of significance for engineering geology research and geo-disaster prevention. There is a growing need to develop fast, nondestructive and affordable techniques that can detect gradual and cyclic changes inside slopes. For this purpose, we propose a fast and low-cost computational framework based on processing the single-point ambient seismic noise recordings by the horizontal-to-vertical spectral ratio (HVSR) method. To test the efficiency of the proposed framework, we conduct a demonstration study in a road-cut slope in colluvium deposits in Southwest China. First, we carry out short-term ambient seismic noise surveys on the slope, delineate the shear wave velocity (Vs) structure of the slope, verify and establish this structure as the reference Vs model for the slope. Then we conduct the long-term monitoring of the ambient seismic noise on two profiles of the slope, calculate the HVSR curves and observe the time-dependent variations of the predominant peaks that reflect temporal changes of subsurface interfaces. Finally, we perform the Vs inversion to investigate changes in the Vs structure with rainfall. Through the monitoring, we identify the rainfall-induced slope failure and discover that both predominant frequency and shallow subsurface Vs of slope are negatively correlated with rainfall. The HVSR calculation, the predominant peak identification and the Vs inversion can all be implemented in minutes, which is much faster than the array-based surface wave method. The theoretical analysis and the demonstration application show that the framework we proposed in this study has great potential for monitoring changes in performance of slopes.
How to cite: Guo, Z.: Monitoring performance of slopes via ambient seismic noise recordings, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15710, https://doi.org/10.5194/egusphere-egu26-15710, 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 Ms6.8 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 vP/vS 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 vP/vS 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.
