Geophysical Journal International

SUMMARYIn recent years, machine learning (ML) techniques have emerged as a powerful tool in seismology, enabling the detection of small-magnitude seismic events that typically go unnoticed by traditional methods. Here, we apply ML-based methods to improve the characterization of normal fault systems and aftershock activity in the Central Apennines, using data from the 2009 L'Aquila seismic sequence. By processing data from both permanent and temporary seismic stations, we identified approximately 191 000 events—with a local magnitude range of -1.83 and 5.96, recorded during January-December 2009—nearly ten times more than the standard catalog maintained by INGV. These events were relocated using a combination of absolute and relative location techniques, resulting in a high-resolution catalog of 148 000 earthquakes. This catalog is distinguished by an increased number of S-wave pickings, which significantly reduces localization errors and enhances the accuracy of fault geometry reconstruction. Compared to an existing semi-automatic catalog, we observe a full recovery of seismic events, and a significant improvement of new events identified and well-located by the ML-approach, with a marked increase in the quality and quantity of P- and S-wave arrivals. The refined seismic catalog not only provides a more detailed and accurate definition of the fault architecture but also offers new insights into the distribution of aftershocks, unrolling the complex pattern of faulting that normally remains masked during standard analyses. This work highlights the potential of ML methods in advancing our understanding of complex fault systems and seismic sequences.

AbstractProgressively denser mapping of ocean-floor magnetization has led to detailed reconstructions of past plate motions in the Cenozoic. These reconstructions often reveal rapid kinematic changes that provide crucial information for identifying geodynamic mechanisms that may have caused them, and for quantifying force budgets upon plates. In parallel to these advances, the notion of thin, low-viscosity asthenosphere beneath tectonic plates that facilitates their motions has emerged and consolidated. This weak, mobile layer promotes the formation of the pressure-driven Poiseuille flow that, in turn, generates basal shearing upon plates. In addition, it can be linked to dynamic topography variations due to pulsing plume activity. In this study, we use publicly available finite-rotation compilations of the North American plate (NA) to investigate its kinematic history since Oligocene time. After removing data that are possibly impacted by significant noise, we find that NA experienced a westward speedup near 27 Ma. Next, we explore the role that asthenospheric Poiseuille-type flow caused by increased Canary plume activity may have had in generating this kinematic change. Such plume activity is inferred from the combination of anomalously shallow residual bathymetry and records of past ocean-floor magmatism offshore northwestern Africa. We compare estimates of torque variation upon NA that are (i) required to explain the reconstructed kinematic change, and (ii) predicted by the Poiseuille-type flow associated with the Canary plume activity. Our results indicate that these two torque-variations estimates are in agreement with each other, both in terms of direction and magnitude. This inference suggests that the increased Canary plume activity is a geodynamically-plausible process to explain the Oligocene plate-motion change of NA.

SummarySeismic traveltime tomography represents a popular and useful tool for unravelling the structure of the subsurface across the scales. In this work we address the case where the forward model is represented by the eikonal equation and derive a formalism to solve the inverse problem where gradients are calculated efficiently using the discrete adjoint state method. Our approach provides gradients with respect to both velocity structure and source locations, allowing us to perform a consistent joint inversion. The forward problem is solved using a second-order fast-marching method, which provides a strategy to efficiently solve the adjoint problem. We allow for arbitrary positions of both sources and receivers and for a refined grid around the source region to reduce errors in computed traveltimes. We show how gradients computed using the discrete adjoint method can be employed to perform either deterministic inversion, i.e., solving an optimization problem, or for a probabilistic (Bayesian) approach, i.e., obtaining a posterior probability density function. We show applications of our methodology on a set of synthetic examples both in 2D and 3D using the L-BFGS algorithm for the deterministic case and the Hamiltonian Monte Carlo algorithm for the probabilistic case.

SummaryHigh-quality maps of subsurface temperature and the geothermal gradient are useful when assessing the geothermal potential of a region. However, determining geothermal potential is a challenge when direct measurements of in-situ temperature and thermal property information are sparse and indirect geophysical methods are sensitive to a range of parameters, not just temperature. Here, we produce subsurface temperature maps of Ireland using a joint geophysical-petrological inversion, where seismic and other geophysical and petrophysical data are inverted directly for temperature in 1D columns and are collated into a pseudo 3D temperature volume. Additionally, the inversion produces new models for Moho and LAB depth and for the average crustal radiogenic heat production.To assess the robustness of the resulting temperature model, an uncertainty analysis has been performed by inverting all of the 1D columns for a range of reasonable input parameters applicable to the Irish crust (rather than the ‘best’ input parameters). The resulting uncertainty model suggests temperature estimates at 2 km depth in our model could vary by ± 2 to 5°C with an average of 3.5°C in most locations. The uncertainty model can be used to assess confidence in different regions of the temperature model. In addition, 3D forward modelling was performed to assess the lateral heat flow variations when compared to the purely 1D inversion. The upper-crustal geothermal gradient ranges from 20 to 40°C/km indicating a higher geothermal gradient for Ireland than previously reported with subsurface temperatures at 2 km depth > 60°C everywhere, sufficient for residential and industrial heating purposes. The temperature gradient is typically higher in areas with thinner lithosphere. However, in some locations, the observed geotherms are elevated further due to high radiogenic heat production in granitic rocks. In Northern Ireland, a thin lithosphere, coupled with a weakly conductive basalt layer overlying warm crust, results in elevated temperatures. These are the first temperature maps for Ireland that include uncertainty estimates, providing ranges for the subsurface temperature values, and demonstrate that the maps are comparable to direct independent borehole temperature measurements, which are observed to fall within the model uncertainty. Our new methodology provides workflows for determining the geothermal potential in areas with limited direct temperature measurements. The final temperature model with uncertainty provides useful constraints for geothermal exploration and utilisation on the island of Ireland.

SummaryThe role of pre-existing lithospheric heterogeneities in rifting processes remains unclear. The Eastern and Main Ethiopian rifts lie within the same geodynamic province and are kinematically connected through the Turkana Depression, but they transect heterogeneous lithosphere: Pan-African accreted terranes, failed Mesozoic-Paleogene rift systems, zones of Eocene-Oligocene flood magmatism. Rifting in these pre-extension heterogeneities offers the opportunity to evaluate their relative importance in Oligo-Miocene to Recent stretching and magmatism. We use 3D Rayleigh shear-wavespeed (Vs) models inverted from ambient noise signals recorded on a temporary seismic network to image heterogeneities in lithospheric structure, and to evaluate their influence on syn-rift faulting and magmatism. Crustal feeder zones for Eocene-Oligocene flood magmatism in southwestern Ethiopia are marked by ≤ 50 km-wide, 10-15 km-thick mid-lower crustal fast wavespeed (Vs ≥ 3.8 km/s) anomalies that are localized rather than widespread. Evidence for active magma intrusions only occurs beneath aligned chains of Quaternary eruptive centers in Lake Turkana and ≤ 1 Ma shield volcanoes east of the Turkana rift having localized low Vs (≤3.4 km/s) at 0-20 km depth. Evidence for widespread lower crustal intrusions, however, is lacking. Pan-African oceanic accreted terranes in southern Ethiopia have high Vs anomalies of 3.6 km/s throughout the crust and overlay previously imaged high wavespeed lithospheric mantle that has been interpreted as cold and strong Proterozoic accreted terrane. The integrated strength of this lithospheric-scale pre-existing mechanical heterogeneity resisted Oligocene-Miocene stretching and subsequently contributed to the unusual breadth of this East African rift sector lying north of the Turkana Depression.

SummaryThe Eastern Continental Margin of India (ECMI) is a classic passive margin formed during the Mesozoic breakup of the supercontinent Gondwanaland. Since its formation, the margin has undergone complex post-rift thermal subsidence, magmatic activity, and interactions with adjacent tectonic plates. Extensive shipborne magnetic data have been acquired over the years, providing substantial coverage of the area. However, knowledge about the regional thermal structure and magnetic nature of the upper mantle of the ECMI and adjacent deep offshore Bay of Bengal is not understood uniformly throughout the region. In this study, we estimate Curie depth from shipborne magnetic data using a power spectrum inversion technique within a Bayesian framework, incorporating fractal source distribution and a priori sediment thickness to constrain the top depth of magnetic slab. The Curie depth is a proxy for the 580°C isotherm, providing insight into the regional thermal structure and crustal rheology that controls post-rift thermal evolution and mantle magnetization. The obtained Curie depths range from 16 km to 28 km with corresponding surface heat flow values varying between 55 and 85 mW/m². A shallower Curie depth and higher heat flow are observed in northern part of the offshore Krishna-Godavari (KG) basin and the southern part of Mahanadi basin, linked to rift-related magmatic intrusions and mantle plume activity. Conversely, deeper Curie depths and lower heat flow characterize the Cauvery basin and the southern ECMI. Our results show, across much of the region from the Continent-Ocean Transition (COT) zone to deep offshore areas, the Curie depth lies below the Moho, suggesting that magnetic sources extend into the upper mantle. This suggests the presence of serpentinised upper mantle and exhumed mantle peridotite which provides the secondary magnetization. The obtained thermal lithosphere thickness varies from 50 to 90 km, shallower in the Krishna-Godavari and Mahanadi basins and deeper towards the Cauvery and central basins. Geotherms intersects mantle adiabat at 50 km depth in the KG and Mahanadi basins, signifying these are thermal overprint basins linked to magmatic activity. The obtained thermal lithosphere deepens from north to south, mirroring trends in the Lithosphere-Asthenosphere Boundary (LAB). Finally, a positive correlation between Curie depth and effective elastic thickness (Te) reflects the regional variation in crustal strength and tectono-magmatic processes controlling the margin evolution.

SummaryThe stress and load path dependencies of elastic properties and their evolution under varying damage states is of critical interest to a multitude of communities, such as geophysicists understanding rock properties for subsurface engineering as well as both civil and geological engineers interested in fundamental damage mechanics of materials. Here, we perform a set of laboratory experiments on a Dakota Mahogany granite to understand the dependence of stress path, orientation, and magnitude on static and dynamic properties as well as dynamic evolution under varying states of damage. Localized strain and ultrasonic velocity, axial and radially aligned with respect to the sample, are recorded along four distinct load paths with varying ratios of mean and differential stress. Differential stress is found to be the predominant factor for variations in static Youngs modulus, while undamaged axial dynamic Youngs modulus is primarily a factor of increasing mean stress. Radial dynamic Young’s modulus demonstrates an overall positive correlation with increasing mean stress and negative correlation with differential stress. A novel relationship is constructed to predict phase velocity and orientation/polarization as a function of stress and load path. The effect of damage within the material is analyzed by subjecting the sample to increasing stresses along a single load path, after which the multipath testing is repeated. Ultrasonic velocity and thus dynamic moduli become less sensitive to increases in differential stress for wave propagation parallel with the maximum principle stress. For P-wave velocity aligned parallel, the contribution of differential stress decreases from nearly that of confining pressure (0.88) to below half at the highest damage state tested. Similar decreases also occur in the contribution of differential stress to the remaining three wave polarizations and orientations. This shows that the degradation of physical properties brought about by microcracking and subsequent decrease in velocity overcomes any increase resulting from consolidation with increasing stress. The results provide a way to anticipate changes in elastic response and subsurface acoustic velocity brought about by increased damage and changing stress state through the use of a new empirical model. Additional methods to establish the distribution of microcracks and their orientations within a damaged material through differences in velocity from loading to unloading are presented which provide useful tools for non-destructively assessing damage state.

SummaryIncorporating anisotropy in seismic imaging is important to produce correct locations and amplitudes of subsurface reflectors. The pure quasi-P-wave equation has a good accuracy to describe wave propagation in the anisotropic media, but it requires complicated computation strategies. To mitigate this issue, we present a novel pure quasi-P-wave equation in the vertical transverse isotropic (VTI) media with a nonlinear scalar operator, which is determined by the anisotropic parameters and the phase-velocity vector. Inaccurately calculating the directions of wave propagation results in incorrect phase-velocity vector and accumulated simulation error. Here, we utilize the optical flow to accurately calculate the direction of wave propagation while maintaining computational efficiency. Then, we optimize the wavefield simulation workflow and accelerate the calculation of optical flow. Numerical experiments show that the proposed wavefield simulation method can accurately describe wave propagation in the VTI media with good computational efficiency. Finally, we apply the proposed method to reverse-time migration to correct the anisotropic effects in seismic imaging. Numerical tests for benchmark models and a land survey demonstrate the feasibility and adaptability of the proposed method.

AbstractGeophysicists using the spontaneous potential method measure differences in electrical potential without providing an active source of current. Most spontaneous potential surveys have been carried out on land or in marine environments. In the present paper, I evaluate the use of the spontaneous potential method in surface fresh water for small-scale environmental and engineering applications. In one survey reported here, the electrical potential between an electrode at the river edge and one suspended from a bridge was used to measure a high resolution profile across a river. In another, electrical potentials were measured between sets of electrodes mounted on a canoe. In both surveys, significant and consistent anomalies were detected particularly near bridge structures, and simple modeling in terms of point sources and line sources was undertaken to better characterize the causes of the anomalies. The possibility of an induction-induced voltage difference across the river caused by Earth’s magnetic field and flow in the river was also investigated. The absence of this potential is attributed to significant electrical conduction through the riverbed. The present work demonstrates the utility of spontaneous potential as a technique for detecting and characterizing anomalies of environmental and engineering interest in fresh water environments.

AbstractWe investigate the modeling of P-wave reflections on the mantle transition zone (MTZ) discontinuities (Pv410p* and Pv660p*) using ambient seismic noise generated by distant oceanic sources. Using ray theory and waveform simulations, we assess biases in arrival times and amplitude ratios when interpreting noise correlations as Green‘s functions. Our results show that source distribution and the b-caustic effect strongly influence signal recovery. Simulations based on realistic oceanic models (WAVEWATCH III) demonstrate that appropriate source conditions significantly reduce biases. This approach enables reliable imaging of the MTZ, particularly in regions like the greater Alpine area with favorable microseismic source distribution.

SummaryHigh-resolution detection of hidden geological faults is vital for city planning, earthquake disaster prevention, and large-scale engineering construction. This study deployed 229 short-period seismometers across a 10×30 km region within the Xianlin area of Nanjing. Of which, 199 formed a 2-D array, and 30 formed a linear array. Various methods were applied to detect hidden faults in the study area. Using ambient noise tomography, a three-dimensional (3D) S-wave velocity structure was obtained from the surface to a depth of 6.0 km, allowing the first locations of a hidden fault to be mapped via velocity anomalies. A linear array was subsequently deployed based on these early findings, and the horizontal-to-vertical spectral ratio (HVSR) method was applied to estimate bedrock depth and define shallow fault features in greater detail. Finally, a shallow seismic exploration was performed to verify the detection results of ambient noise tomography and HVSR analysis. The results indicate the presence of a hidden fault in the study area, which manifests as a distinctive area of alteration in the high- and low-velocity anomalies in the 3D S-wave velocity structure. Significant variation was identified in the sediment layer thickness in the shallow subsurface, as observed in the HVSR records. In addition, shallow seismic exploration defined important wave-group phase-axis discontinuities in areas with abrupt sedimentary thickness changes. Thus, the hidden fault identified in this study is a normal fault with a nearly north-dipping direction, dip angle of approximately 60°, and fault displacement of approximately 30 m. By linking these results with previous data, it is possible to suggest that such hidden faults are part of the Mufushan–Jiaoshan Fault. Future urban designs and buildings must thoroughly consider the seismic dangers in this region and apply suitable mitigation strategies.

SummaryShipborne gravity anomaly data exhibit multi-vintage characteristics due to their extended temporal coverage. Currently, the measurement accuracy of gravimeters and the processing methods for shipborne gravity anomaly data have been significantly improved and refined. At this stage, the influence of temporal error on the processing of shipborne gravity anomaly data has become an issue that cannot be neglected. We propose a joint reprocessing method for multi-vintage shipborne gravity anomaly data considering temporal error effects. Firstly, the gross error of the shipborne gravity anomaly data is eliminated and filtered. When compensating for the survey line error, the time variable is added to the error equation in order to retain the temporal information in the observed value. The corrected shipborne gravity anomaly data by this method is closer to the real gravity field information. We applied this method to the real shipborne gravity anomaly data in the Philippine Sea. The results showed that the standard deviation of the discrepancy at the intersection points of the survey lines was reduced from the initial 13.46 mGal to 4.30 mGal. The shipborne gravity anomaly data processed after considering the temporal error effects conforms more closely to the actual gravity field information.

SummaryPassive surface wave method is increasingly being applied to urban subsurface exploration due to its non-invasiveness, low cost, and high efficiency. However, its imaging quality is often influenced by limited data acquisition time and the heterogeneous distribution of seismic ambient fields in complex urban environments. To extract coherent surface wave signals for seismic imaging in such challenging setting, we developed a multi-stage urban ambient noise deep clustering framework based on a convolutional autoencoder and deep embedded clustering algorithm. The initial clustering characterizes the distribution patterns of urban noise sources, which informs a secondary, finer clustering to select noise sources optimized for urban seismic imaging. Real-world experiment on the urban train noise field demonstrates our urban noise cluster framework effectively identifies and elucidates the temporal evolution patterns of moving train sources. Compared to traditional data selection methods, our approach yields superior dispersion measurements and significantly attenuates artifacts from the fundamental mode. Furthermore, by employing mode-specific clustering, we successfully capture the refined first overtone, enhancing the accuracy and depth resolution of seismic imaging. This study presents a new perspective to analyzing and selecting complex noise sources, significantly advancing seismic imaging and monitoring in alignment with emerging Artificial Intelligence trends.

SummaryBoth short-term coseismic off-fault damage and long-term fault growth during interseismic periods have been suggested to contribute to the formation and evolution of fault damage zones. Most previous numerical models focus on simulating either off-fault damage in a single earthquake or off-fault plasticity in seismic cycles ignoring changes of elastic moduli. Here we developed a new method to simulate the damage evolution of fault zones and dynamic earthquake cycles together in a 2D anti-plane model. We assume fault slip is governed by the laboratory-derived rate-and-state friction law while the constitutive response of adjacent off-fault material is controlled by a simplified version of the Lyakhovsky-Ben-Zion continuum brittle damage model. This study aims to present this newly developed modeling framework which opens a window to simulate the co-evolution of earthquakes and fault damage zones. We also demonstrate one example application of the modeling framework. The example simulation generates coseismic velocity drop as evidenced by seismological observations and a long-term shallow slip deficit. In addition, the coseismic slip near the surface is smaller due to off-fault inelastic deformation and results in a larger coseismic slip deficit. Here we refer to off-fault damage as both rigidity reduction and inelastic deformation of the off-fault medium. We find off-fault damage in our example simulation mainly occurs during earthquakes and concentrates at shallow depths as a flower structure, in which a distributed damage area surrounds a localized, highly damaged inner core. With the experimentally based logarithmic healing law, coseismic off-fault rigidity reduction cannot heal fully and permanently accumulates over multiple seismic cycles. The fault zone width and rigidity eventually saturate at long cumulative slip, reaching a mature state without further change.

SummaryGreen’s function expressions for seismic interferometry in acoustic and elastic media have been extensively studied and applied across a wide range of applications, including surface-wave tomography and generating virtual shot gathers. However, analogous expressions for coupled acoustic-elastic media systems remain absent, despite their importance for analysing cross-correlation wavefields from ocean-bottom nodal and seismometer recordings and other seismic problems in marine settings. To address this issue, we derive convolution- and correlation-type reciprocity relations for physically coupled acoustic-elastic media by combining Rayleigh’s and Rayleigh-Betti reciprocity theorems, incorporating the constitutive equations governing coupling at the acoustic-elastic interface, and applying time-reversal invariance principles for an arbitrary 3-D inhomogeneous, lossless medium. The derived relationships show that the acoustic and elastic Green’s functions between any two observation points in the medium can be expressed as integrals of cross-correlations of wavefield observations at those locations, generated by sources distributed over an arbitrarily shaped closed surface enclosing the two observation points. When the Earth’s free surface coincides with the enclosing surface, integral evaluation is required only over the remaining portion of the closed surface. If the sources are mutually uncorrelated ambient sources, the Green’s function representation simplifies to a direct cross-correlation of wavefield observations at the two points, generated by a specific ambient source distribution on the closed surface. However, in practical scenarios, the ideal source distribution necessary to retrieve Green’s functions is rarely realized, for example, due to non-uniform illumination. To address these challenges, we represent the ambient cross-correlations as self-consistent observations and introduce a cross-correlation modelling methodology that accounts for practical limitations in source distribution for coupled acoustic-elastic media scenarios. We illustrate the theory by modelling ambient cross-correlation wavefields for a deep-water scenario.

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.