Mark van der Meijde

Gas extraction from the Groningen field has induced a substantial number of earthquakes that, despite their typically low magnitudes, produce notable ground motions at the surface due to their shallow depths of approximately 3 km. These ground motions pose risks to society and infrastructure. Therefore, an accurate ground motion simulation is essential for seismic hazard assessment. Previous studies have demonstrated that near-surface unconsolidated layers significantly influence ground motion amplification. However, less attention has been devoted to understanding the role of deeper structures. In the Groningen region, significant amplification and de-amplification effects are anticipated due to the complex subsurface, thickness variations across relatively short lateral distances, compositional heterogeneity within sedimentary sequences, and the presence of the Zechstein salt layer overlying the reservoir formation.This study investigates how subsurface heterogeneity, both shallow and deep, affects seismic wave propagation and the corresponding ground motion observed at the surface. To analyze this, we employ 3D full waveform modeling using the spectral element method (SEM). First, to optimize mesh resolution, determined by the local S-wave velocity and the target design frequency, we conduct simulations across a range of frequencies and corresponding spatial resolutions to analyze their impact on wavefield accuracy and computational cost. Second, we simulate seismic wave propagation through a synthetic velocity model representative of the Groningen subsurface and compute Peak Ground Acceleration (PGA) for different earthquake scenarios using various Centroid Moment Tensor (CMT) source solutions. Since amplification effects are highly location-dependent, we evaluate multiple earthquake scenarios with varying source characteristics and locations. We then compare these results with PGA values computed for a homogeneous half-space model that preserves the bulk elastic properties of the realistic heterogeneous model, using identical earthquake sources. This comparison produces amplification factor maps that reveal distinct spatial patterns of amplification and de-amplification across the study region. To isolate the contributions of individual factors, we examine the influence of source frequency, the depth and thickness of velocity layers, the presence of velocity inversions within the stratigraphic sequence, and subsurface interface topography.These tests allow us to identify how each parameter contributes to the resulting amplification and de-amplification patterns. This framework can provide physical explanations for the spatial distribution of observed ground motion variations, offering valuable insights that are instrumental for current and future seismic hazard assessments in areas of subsurface resource exploitation throughout the Netherlands.

Multi-Observable Thermochemical Tomography (MTT) is a simulation-driven, joint probabilistic inversion framework designed to estimate the thermochemical state of the Earth’s lithosphere by integrating geophysical datasets with complementary sensitivities. By jointly inverting observables such as gravity and geoid anomalies, surface heat flow, seismic dispersion, body-wave data, and magnetotelluric responses, MTT directly estimates primary thermodynamic variables, including temperature, pressure, and bulk composition, from which all secondary physical properties are derived through internally consistent thermodynamic models. This bottom-up approach provides physically-consistent constraints on lithospheric structure across regional to prospect scales.Within this framework, MTT offers a powerful basis for characterizing lithospheric architecture and compositional domains that are commonly examined in mineral systems studies. In particular, MTT can help delineate major crustal- and lithospheric-scale structures, identify metasomatized/altered domains, and map thermochemical contrasts that serve as lithospheric-scale proxies commonly associated with specific classes of magmatic and hydrothermal mineral systems.Despite recent advances incorporating ray-based seismic tomography solvers (Fomin, I., Afonso, J. C., Gorbatov, A., Salajegheh, F., Dave, R., Darbyshire, F. A., et al. (2026). Multi-observable thermochemical tomography: New advances and applications to the superior and North Australian cratons. Journal of Geophysical Research: Solid Earth, 131, e2025JB031939. https://doi.org/10.1029/2025JB031939 ), the integration of full-waveform seismic solvers within the MTT framework has not yet been achieved. Full-waveform inversion (FWI) offers enhanced sensitivity to both seismic velocity and density and the potential for improved spatial resolution relative to traditional tomography approaches. However, the computational cost of FWI remains prohibitive, particularly in probabilistic or ensemble-based inversion settings required for uncertainty quantification.This contribution presents a computational strategy aimed at reducing the cost of full wavefield simulations to enable probabilistic seismic FWI within the MTT framework. We extend reduced-order modeling (ROM) techniques to the spectral element method (SEM), which is widely used for accurate time-domain seismic wave propagation in complex geological settings. Specifically, we consider projection (Galerkin)–based ROMs in which the SEM wavefield is approximated in a low-dimensional reduced basis constructed from representative high-fidelity solutions. While ROM approaches are well established for simpler formulations, their application to SEM-based elastic wave simulations remains challenging due to the method’s high dimensionality and complex operator structure. Beyond MTT, such reductions are also relevant to SEM-based workflows that require large numbers of forward simulations, including ground motion studies and FWI with many sources at regional-to-global scales.We develop and test a reduced-order SEM formulation using synthetic benchmark models relevant to lithospheric-scale imaging. Results demonstrate computational speed-ups of up to two orders of magnitude relative to full SEM simulations, while retaining sufficient accuracy in simulated wavefields for inversion purposes. These results represent a first proof of concept toward incorporating probabilistic FWI into multi-observable thermochemical tomography and reducing a key computational barrier to uncertainty-aware, physics-based lithospheric imaging.

Passive seismic networks play critical roles in both seismicity monitoring and subsurface imaging. The seismic network in the Netherlands has continuously been expanded, both to monitor induced seismicity related to gas exploitation in the north and to monitor natural (tectonic related) earthquakes in the south of the country. Aside monitoring seismicity, the data delivered by the seismological networks is required for imaging the subsurface to improve our knowledge of the composition and structure. This serves as a crucial layer of information for decision-makers in regional planning, locating resources, and risk assessment regarding current and future projects utilising the Dutch subsurface. Over the years, there has been an increase in the number of geophones and accelerometers for monitoring mostly the high frequency seismic signals (induced seismicity) and imaging the near surface structure. However, the network of broadband seismometers for measuring tectonic events (usually lower frequency signals) and imaging deep subsurface is still limited. We describe an overview of previous temporary and permanent broadband seismometer deployments in the Netherlands, with a focus on the latest NARS-DICTUM array of 25 broadband seismometers, underscoring its design, instruments, installation, and some preliminary subsurface information delivered by the network.

SNAP (Sentinel Application Platform) is an ESA open-source package distinguished by its stability and user-friendly interface, especially while conducting interferometric SAR (InSAR) processing. However, SNAP-ESA is limited by the lack of a flexible algorithm to generate InSAR time series stacks for both Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques. Moreover, another limitation is the computational requirement to generate InSAR time series interferometric stacks for the available data time span over large areas. In this research, we introduce an innovative automated Python Workflow built upon SNAP-ESA, namely SNAPWF. SNAPWF integrates the capabilities of open-source ASF-search and SNAP-ESA software, enabling network graph generation for PSI and SBAS. The generated network graphs are then utilized to generate the InSAR stacks using SNAP-ESA flexible Graph Processing Framework (GPF) through the Graph Processing Tool (GPT). SNAPWF has the capability to export the interferometric stacks to different file formats that enable further analysis in other available software packages. We implemented and tested SNAPWF on a dedicated geospatial cloud computing platform (GCP). The results demonstrated its capability to generate complete interferometric stacks for Sentinel-1 scenes for PSI and SBAS implemented for a study area across Kenya and Tanzania in 6 hours for one year of data. Moreover, the performance test results showed the possible utilization of the variable resources to accelerate the processing steps.

Despite 160 years of probing the world crust, due to lack of seismic and ground gravity observations, there are still white spots in the worlds' crustal thickness map. The crustal structure in those regions is among the least understood of the Earth's continental areas, and variations in basic but fundamental parameters - such as crustal thickness - are still poorly constrained over large areas. Recent research has shown that satellite gravity-based crustal modeling in regions with limited seismological coverage can provide unique insights in crustal thickness and underlying geodynamical processes.In almost all of these cases the gravity signal related to crustal structure is isolated by applying 3 different corrections: topography, sediments, and upper mantle structure. Of these three, the upper mantle correction is least well addressed. It doesn’t account for any lateral inhomogeneity upper mantle composition close to the crust-mantle boundary. As a result, satellite gravity data reductions for upper mantle structure are a source of uncertainty. Our new model includes a new state-of-the-art upper mantle correction. By combining satellite gravity and seismic tomography, we have formulated a new methodology to integrate potential field data inversions, tomographic modelling, and petrolophysics into a single inversion scheme. Our crustal thickness model ECM24 has therefore more accurate crustal thickness values, is seismically fitting better than previous models, and is also very consistent with gravity observations.

The Nile Delta represents the most critical part of Egypt, hosting more than 50% of the population and approximately two-thirds of the nation’s agricultural lands. During the last decades, the Nile Delta has suffered from significant surface deformation that has led to damage to transport networks and infrastructures, thus becoming a significant risk in the area. This deformation is mainly due to environmental changes and anthropogenic activities such as the over-extraction of groundwater for different purposes and the building of dams inside and outside Egypt along the river Nile. These activities have led to shortage of water inflow, changing the discharge rates, reduced sedimentation in the delta and changes in the water recharge rates of the Nile, the primary source of water for the Nile Delta aquifers. Regarding the environmental changes, the shifts in climate patterns, including variations in precipitation, rising temperatures, and rising sea levels, have all altered the hydrological balance of the Nile Delta, consequently leading to variations in surface deformation patterns. Many studies have studied the rate and patterns of land deformations in the Nile Delta based on geodetic tools such as Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR). But still, there is a gap in understanding the relationship between surface deformation rates and the causative factors. Such understanding could potentially enable the estimation of system response for future scenarios.In this research, we present the results of a system that uses Sentinel-1 SAR data characterized by VV polarization, with ascending and descending orbital directions, acquired between 2015 and 2023 along the Nile Delta. We utilized open-source LiCSBAS tools to analyze the surface deformation rates from InSAR Sentinel-1 data. Then, we calculated the vertical deformation velocity over time by decomposing the ascending and descending LOS data. Then, we analyzed the surface deformation results obtained with the present methodology against the freely available geospatial data, which represents the possible causative factors (such as rainfall, water body change, total terrestrial water storage, land use-landcover, temperature, etc.), to understand their relations and their impact. By linking the surface deformation to its causative factors through machine learning techniques such as Random Forest, our research aims to provide a better understanding of the system dynamics and an appropriate model for prediction. This model can be utilized by decision-makers to consider and manage risks associated with severe surface deformation for possible future scenarios. The results should enable the design of future mitigation actions to protect Egyptian society and make it more resilient to the consequences of land deformation over the Nile Delta.

Geophysical surveys, a means of analyzing the Earth and its environments, have traditionally relied on ground-based methodologies. However, up-to-date approaches encompass remote sensing (RS) techniques, employing both spaceborne and airborne platforms. The emergence of Unmanned Aerial Vehicles (UAVs) has notably catalyzed interest in UAV-borne geophysical RS. The objective of this study is to comprehensively review the state-of-the-art UAV-based geophysical methods, encompassing magnetometry, gravimetry, gamma-ray spectrometry/radiometry, electromagnetic (EM) surveys, ground penetrating radar (GPR), traditional UAV RS methods (i.e., photogrammetry and LiDARgrammetry), and integrated approaches. Each method is scrutinized concerning essential aspects such as sensors, platforms, challenges, applications, etc. Drawing upon an extensive systematic review of over 435 scholarly works, our analysis reveals the versatility of these systems, which ranges from geophysical development to applications over various geoscientific domains. Among the UAV platforms, rotary-wing multirotors were the most used (64%), followed by fixed-wing UAVs (27%). Unmanned helicopters and airships comprise the remaining 9%. In terms of sensors and methods, imaging-based methods and magnetometry were the most prevalent, which accounted for 35% and 27% of the research, respectively. Other methods had a more balanced representation (6–11%). From an application perspective, the primary use of UAVs in geoscience included soil mapping (19.6%), landslide/subsidence mapping (17.2%), and near-surface object detection (13.5%). The reviewed studies consistently highlight the advantages of UAV RS in geophysical surveys. UAV geophysical RS effectively balances the benefits of ground-based and traditional RS methods regarding cost, resolution, accuracy, and other factors. Integrating multiple sensors on a single platform and fusion of multi-source data enhance efficiency in geoscientific analysis. However, implementing geophysical methods on UAVs poses challenges, prompting ongoing research and development efforts worldwide to find optimal solutions from both hardware and software perspectives.

Subduction-related geodynamic processes significantly influence plate tectonics and Earth’s evolution, yet their impact on the continental crust remains poorly understood. We investigated the Sardinia-Corsica continental block, situated in the Mediterranean Sea, which has experienced intense subduction-driven geodynamic events. By analyzing P-wave receiver functions from our LiSard seismic network and publicly available stations, we aimed to understand crustal structure and composition. We inferred the Moho depth and examined the P-wave to S-wave velocity ratio (V_P /V_S ). We interpret our findings considering petrological data, heat flux measurements, and other geophysical information. We found that the Variscan granitoid batholith has the greatest Moho depths in both Sardinia and Corsica. V_P /V_S ratios (ranging from 1.65 to 1.70) are consistent with average crustal values of SiO₂ between 65 % and 70 %. However, in central Corsica, two stations have exceptionally high V_P /V_S values (>1.80), suggesting the possible presence of serpentinite throughout the crust. In Alpine Corsica, a station exhibited similar high V_P /V_S values but a shallower Moho depth of 21 km. The western part of Sardinia, where Cenozoic volcanism occurred, also showed a shallower Moho depth (20–25 km) and high V_P /V_S values. The highest V_P /V_S value (1.91) is recorded in an area where surface-wave dispersion curves from ambient noise identified the lowest average S-wave velocity and where the highest heat flux has been reported, indicating elevated crustal temperatures and possible presence of melt within the crust. Overall, our results indicate that the recent geodynamic processes have left the granitoid batholith almost intact, with minimal alteration to its composition.