Geophysical Journal International

SummarySeismic signals generated by near-surface explosions, with sources including industrial accidents and terrorism, are often analysed to assist post-detonation forensic characterisation efforts such as estimating explosive yield. Explosively generated seismic displacements are a function of, amongst other factors: the source-to-receiver distance, the explosive yield, the height-of-burst or depth-of-burial of the source and the geological material at the detonation site. Recent experiments in the United States, focusing on ground motion recordings at distances of <15 km from explosive trials, have resulted in empirical models for predicting P-wave displacements generated by explosions in and above hard rock (granite, limestone), dry alluvium, and water. To extend these models to include sources within and above saturated sediments we conducted eight explosions at Foulness, Essex, UK, where ∼150 m thicknesses of alluvium and clay overlie chalk. These shots, named the Foulness Seismoacoustic Coupling Trials (FSCT), had charge masses of 10 and 100 kg TNT equivalent and were emplaced between 2.3 m below and 1.4 m above the ground surface. Initial P-wave displacements, recorded between 150 and 7000 m from the explosions, exhibit amplitude variations as a function of distance that depart from a single power-law decay relationship. The layered geology at Foulness causes the propagation path that generates the initial P-wave to change as the distance from the source increases, with each path exhibiting different amplitude decay rates as a function of distance. At distances up to 300 m from the source the first arrival is associated with direct propagation through the upper sediments, while beyond 1000 m the initial P-waves are refracted returns from deeper structure. At intermediate distances constructive interference occurs between P-waves propagating through the upper sediments and those returning from velocity-depth gradients at depths between 100 and 300 m. This generates an increase in displacement amplitude, with a maximum at ∼800 m from the source. Numerical waveform modelling indicates that observations of the amplitude variations is in part the consequence of high P- to S-wave velocity ratios within the upper 150 m of saturated sediment, resulting in temporal separation of the P- and S-arrivals. We extend a recently developed empirical model formulation to allow for such distance-dependent amplitude variations. Changes in explosive height-of-burst within and above the saturated sediments at Foulness result in large P-wave amplitude variations. FSCT surface explosions exhibit P-wave displacement amplitudes that are a factor of 22 smaller than coupled explosions at depth, compared to factors of 2.3 and 7.6 reported for dry alluvium and granite respectively.

SummaryIntermittent fluid injection aims at inferring and steering hydraulic transmissivity and has become an integral part of reservoir stimulation techniques. Modeling the poroelastic response of such pumping operations poses new challenges with respect to the hydromechanical coupling. This is because when a fluid pressure perturbation is introduced in the pore space of a deformable porous rock, it will induce a stress perturbation in the solid phase and this is accompanied by pore boundary motion. Within the limits of quasi-static linear poroelasticity, we analyze the macroscopic signatures of pore boundary motion during injection, i.e., when the rock frame is mechanically loaded, and after injection stop, i.e., when pore boundaries tend to relax back into equilibrium. We show that there is a pumping sequence that allows to harness the energy associated with pore boundary motion accumulated during the frame-loading cycle. Our results foster the need to distinguish how pressure diffusion in poroelastic solids proceeds: either fluid transport is of compressible or incompressible nature and the respective diffusion constant depends on undrained or drained poroelastic moduli.

SummaryThermal properties such as thermal conductivity (TC), thermal diffusivity (TD), and specific heat capacity (SHC) are essential for understanding and modelling the subsurface thermal field. In sedimentary basins, these parameters play a key role in characterizing the present-day thermal state and predicting its evolution, for example, in response to future geo-energy utilizations. Given the wide range of potential geo-energy utilizations and the frequent lack of sufficient sample material, many studies have focused on developing accurate prediction approaches. Machine learning (ML) offers promising non-linear statistical methods to enhance the mapping of interrelations between standard geophysical well-log readings and thermal rock properties. In this study, we introduce an open-access tool for computing profiles of thermal rock properties from standard geophysical borehole logging data, building upon and extending previous petrophysical studies. The tool employs various machine-learning approaches trained on large, physically modelled synthetic datasets that account for mineralogical and porosity variability across major sedimentary rock groups (clastic rocks, carbonates, and evaporates). It establishes functional relationships between thermal properties and different combinations of standard well-log data, including sonic velocity, neutron porosity, bulk density, and the gamma-ray index. We trained four different models including linear regression, AdaBoost, Random Forest, and XGBoost using 80 per cent of the synthetic group data for model development, including training and hyperparameter tuning through cross-validation. The remaining 20 per cent was held out as an independent test set for statistical validation, feature recognition, and input variable importance analysis. A total of 15 input log combinations (including all one, two, three, and four well-log configurations) were evaluated across four machine learning models (linear regression, AdaBoost, Random Forest, and XGBoost), resulting in 180 trained models. The model's predictive accuracy and reliability were further evaluated against independent laboratory drill-core measurements reported in recent studies. Our results indicate that the best-performing predictive models vary depending on the available log-combinations. However, XGBoost frequently outperforms other models in sedimentary rocks. When at least two well logs are provided as input variables, the best-performing models predict thermal conductivity with an uncertainty below 10 per cent relative to borehole validation data (with laboratory-measured thermal conductivity). In most tested model cases and for most input log combinations, predictive errors for thermal conductivity range between 10 and 30 per cent at the (point measurement) sample scale (cm to half a meter). However, when averaged over geological formations or borehole intervals (tens to thousands of meters), the accuracy of thermal-conductivity predictions improves significantly, with uncertainties of the interval mean conductivity dropping below 5 per cent for large intervals. For specific heat capacity, prediction accuracy for the best-performing models at the measurement scale is typically better than 5 per cent. Thermal diffusivity reflects a larger variation, accumulating the uncertainties from conductivity and heat capacity. The presented log-based Python prediction tool provides an automated means to compute thermal parameters using the most suitable ML model for given well-log inputs, facilitating enhanced thermal characterization in sedimentary settings. This has practical relevance for geothermal or hydrocarbon exploration, or subsurface storage projects.

SummaryThis paper investigates the seismic activity and velocity structure in the Three Gorges Reservoir (TGR) region using high-quality travel time data from an extensive seismic observation network. The primary goal is to understand the relationship between the three-dimensional velocity structure and seismicity within the reservoir area. We employed advanced inversion techniques to develop detailed 3-D models of the P- and S-wave velocities and analyzed the focal mechanisms of significant seismic events. Our results reveal that there are substantial lateral variations in the upper crustal velocity structure, with high-velocity zones in the northeastern region of Badong and lower velocities in the Zigui Basin (ZGB). The sedimentary layers in the ZGB are 6–8 km thick, and low S-wave velocity anomalies extend from this depth and are correlated with the Triassic formations. The seismic activity patterns show that the earthquakes in the Badong region were concentrated along three east–west trending belts within the core of an anticline. These patterns suggest that the geological structures and fluid infiltration significantly influence the seismicity. In particular, the M5.1 Badong earthquake occurred at the boundary of a high-velocity zone and was associated with a seismic belt extending from shallow to deeper depths. The results of this study highlight the complex interactions between rock heterogeneity, fault dynamics, and fluid effects, providing a comprehensive analysis of reservoir-induced seismicity. This work provides a better understanding of the physical mechanisms driving seismic activity in large reservoir systems and provides insights relevant to seismic hazard assessment and reservoir management.

SummaryCharacterizing ore deposits or mining dumps in terms of mineral content and grain size remains a challenge. Since the 1950s the Induced Polarization (IP) method has been successfully applied in ore prospecting. However, reliably interpreting field survey data requires comprehensive laboratory studies to establish a link between the IP parameters from empirical or phenomenological models and the type and quantity of ore minerals. In this study, we use numerical electrical networks to replicate the complex electrical resistivity spectra observed in experiments on sand-pyrite-water mixtures. A network consists of a 3D assembly of resistors, representing the saturated pore space, and leaky capacitors simulating the electrical behaviour of ore minerals. A sophisticated fitting procedure enables the precise determination of resistor and capacitor parameters, ultimately leading to strong agreement between measured and synthetic IP spectra. The results obtained from the 3D network align well with the classical Pelton model, which is based on a simple equivalent circuit. Our findings indicate that the network's chargeability depends on the fraction of capacitors in the system (i.e. the number of capacitors divided by the number of capacitors and resistors), and that the Pelton time constant of the measured spectra is closely related to the resistor and capacitor parameters. We argue that a 3D approach offers a more realistic framework, paving the way for future studies on the effects of ore grain size distribution, and the spatial arrangement of ore grains.

SummaryThe most widely used method to derive global geomagnetic field models for historical and longer timescales has long been regularized non-linear least squares inversion. It is based on spherical harmonics for the spatial part and cubic B-splines for the temporal dynamics. Recently, different versions of Bayesian inversion have been applied for this purpose. Early literature on the traditionally used formalism states the inverse problem in a Bayesian setting and discusses uncertainty estimation via the posterior covariance, but this view was lost in subsequent studies in the geomagnetic community. Here we aim to provide both geomagnetic field modellers and users of such models with a comparative view of the methods to enable them to better evaluate strengths and weaknesses of different models. We first describe the connection between regularized least squares and Bayesian inversion in general form in a linear, one-dimensional setting. A fully Bayesian perspective allows interpreting the regularization term as a form of prior and offers new ways of comparing models from both approaches. We then discuss the particular case of geomagnetic field modelling. We find that in comparison to Bayesian modelling approaches the prior corresponding to the widely used regularization does not imply reasonable field properties and does not lead to meaningful uncertainty estimates.

AbstractThis study presents a refined interpretation of the Jeokjung-Chogye Basin (JCB), a confirmed meteorite impact structure in South Korea, by integrating high-resolution gravity data, microtremor measurements, and borehole information. A total of 1 700 gravity stations including 1 000 newly acquired in 2023, were used alongside horizontal-to-vertical spectral ratio (HVSR) analysis and well-log constraints to characterize subsurface structures. To isolate the impact-induced deformation from overlying sedimentary effects, gravity stripping was applied to remove the signal from post-impact ejecta deposits. The residual gravity field was analysed using dip-curvature mapping and Euler deconvolution, which revealed concentric ring structures with displaced centres. These asymmetries, corroborated by 3D forward gravity modelling using IGMAS+, suggest a northeast-to-southwest impact trajectory with an oblique incidence angle of approximately 45°, contrasting with earlier estimates of ~55° from east to west. The final 3D density model achieves a strong correlation with observed anomalies (R ≈ 0.95) and successfully resolves variations in the autochthonous and basement layers.