Geophysical Journal International

SummaryMost geologic materials exhibit a low-frequency dispersion of the complex conductivity that can be used to investigate their petrophysical characteristics. This frequency-dependent behavior is caused by polarization processes on the microscale, which can be described and investigated by suitable mechanistic models. However, most existing models only consider the polarization of single particles and can only be upscaled using simplistic models. In particular, the effect of particle-particle interaction on the microscale is often neglected. We present numerical models of systems of two particles and of clusters of particles to investigate the effect of interaction between adjacent particles on the modeled complex-conductivity spectra. Due to numerical reasons, most of the simulations are done in 2d. We observe that the peak caused by the polarization of the individual particles is increased or decreased, depending on the orientation of the particles relative to the external electric field. For small distances between the particles, we also observe a membrane-like polarization of the particle-particle interspace when the particles are not aligned with the electric field. For clusters of particles, the membrane-like behavior manifests itself by several minor peaks related to the different length scales of these interspaces. An additional polarization process is present for finite particle clusters, that is related to the polarization of the cluster as a whole, which becomes the main polarization process when the distance between the particles is decreased to zero, i. e., neighboring particles are in direct contact to each other. In that case, the length scale of the individual particles only acts as surface roughness. Our modeling results clearly show that we must expect particle-particle interaction to significantly increase the complexity of the low-frequency polarization response. Therefore, interaction effects need to be taken into account by considering the detailed microscale structure of the pore space for the interpretation of complex-conductivity spectra of geological materials.

SummaryGeophysical data observed in the Earth, planetary, and lunar sciences are spatially limited to sampling plans designed to span a feature of interest whose resolution and regularity in discretization are bound by site, observational, and instrumental constraints. Common wisdom tells us that the more observations we make, the less variable our estimates of statistical properties of measured physical quantities will be. However, given the limited spatial extent and possibly irregular sampling of any dataset, how confident can we be in our estimates of the parameters of statistical processes underlying such fields? We study how sampling geometry contributes to uncertainty in modeling spatial geophysical observations as sampled random fields characterized by stationary, isotropic, parametric covariance functions. We incorporate the signature of discrete spatial sampling patterns into an asymptotically unbiased spectral maximum-likelihood estimation method along with analytical uncertainty calculation. We illustrate the broad applicability of our modeling through synthetic and real data examples with sampling patterns that include irregularly bounded contiguous regions of interest, structured sweeps of instrumental measurements, and missing observations dispersed across the domain of a field, which spur behaviors in the estimator. We find through asymptotic studies that allocating samples following a growing-domain strategy rather than a densifying, infill scheme best reduces estimator bias and (co)variance, whether the field has been sampled regularly or not. As our modeling assumptions, too, shape how (well) an observed random field can be characterized, we study the effect of covariance parameters assumed a priori. We demonstrate the desirable behavior of the general Matérn class and show how to interrogate goodness-of-fit criteria to detect departures from the null hypothesis of Gaussianity, stationarity, and isotropy.

SummaryThe application of machine learning to planetary geophysics is frequently bottlenecked by a scarcity of labeled data and the complexity of non-stationary seismo-acoustic signals. While deep learning offers powerful feature extraction, it requires massive datasets that are rarely available for phenomena like Martian quakes or deep-ocean volcanism. Consequently, researchers often rely on unsupervised machine learning or hand-engineered features that are typically impoverished or hard to interpret. To resolve this, we introduce Spec2VEC, a framework that transforms parameter-dependent TFRs into a robust, interpretable, and high-dimensional vector space. Spec2VEC treats spectrograms not merely as energy distributions but as structured, textured images, and extracts physics-informed, dataset-agnostic features without requiring massive training loops. The framework systematically extracts a feature vector using two novel pathways: (1) a Textural path that utilizes Hilbert space-filling curves to map 2D spectral patterns into 1D sequences for advanced information-theoretic analysis (e.g., permutation entropy, fractal dimension); and (2) a Spatial path driven by PDFSI (Probabilistic Distribution Fitting for Signal Identification), a novel probabilistic pre-conditioning engine that quantifies spatial connectivity by dynamically separating coherent signal modes from stochastic noise. We validate this feature space on a comprehensive benchmark of complex synthetic wavefields and two real-world examples: an Exotic Seismic Events Catalog (ESEC) and a Distributed Acoustic Sensing (DAS) dataset. Spec2VEC provides objective features for information discrimination and enables highly accurate classification of the diverse geophysical signal classes present in the ESEC catalog. Furthermore, application on the high data volume DAS dataset confirms its computational efficiency and domain-agnostic capability to isolate anomalous seismic arrivals from background noise. Ultimately, Spec2VEC bridges the interpretability of classical signal processing with the discriminative power of modern representation learning, providing a reliable, interpretable foundation for automated discovery across the geosciences.

SummaryAccurate three-dimensional (3D) seismic velocity modeling through seismic travel-time tomography using both active- and passive-source data provides critical underpinning models for seismicity monitoring and hazard assessment. Because travel-time tomography is an inherently ill-posed inverse problem, uncertainty quantification (UQ) of the estimated models using Bayesian methods is also important for reliable downstream interpretations and analyses. However, Bayesian inference for 3D tomography based on conventional grid-based representations faces the “curse of dimensionality” and severe computational bottlenecks. Consequently, rigorous Bayesian UQ for margin-wide 3D travel-time tomography has remained largely unexplored, despite its importance for reliable interpretation of large-scale velocity models. In this study, we propose a meshless 3D Bayesian travel-time tomography method that combines physics-informed neural networks (PINNs) with a neural representation of the velocity structure, enabling tractable and data-efficient Bayesian inference through function-space Particle-based Variational Inference (fParVI). To efficiently integrate passive-source data into the Bayesian estimation of the velocity structure, we introduce an analytical marginalization strategy that treats uncertain parameters related to the origin time and location as nuisance parameters, with passive-source relocation carried out in post-processing. We validated the capability of our approach for 3D problems through synthetic experiments. These experiments demonstrate its robustness against biased prior source locations compared to standard methods that suffer from overfitting. Furthermore, we applied the method to a real-world dataset off the Kii Peninsula, Nankai Trough, using over 380,000 travel-time picks from marine active-source surveys and natural earthquakes. Our probabilistic 3D ensemble successfully resolves key geological features, such as the Kumano Pluton, and provides data-consistent uncertainty maps that offer new quantitative insights into the reliability of previously debated deep low-velocity zones. The posterior mean hypocenters shifted mainly in the vertical direction by 10-15 km, consistent with the deterministic relocation results of a previous study. Finally, the continuous neural representation drastically reduces storage requirements for the entire ensemble velocity model, highlighting the scalability and data efficiency of the proposed framework for Bayesian seismic travel-time tomography.

SummaryA rigid connection between the optical fiber and the rock allows to properly measure ‘rock strain’ with Distributed Acoustic Sensing (DAS). We demonstrate this by running four interrogator units (IU) on a testbed with single-fiber patch cables being cemented into a groove in the concrete floor of Black Forest Observatory (BFO). The recorded signals are compared with the recordings of a calibrated Invar wire strain meter array that has been continuously in operation for the last decades at a distance of about 350 m to the testbed. In this way we compare the ‘fiber strain’ against ‘rock strain’ at frequencies below 0.2 Hz. We investigate this on the background of a previous study with DAS cables unreeled on the floor and loaded down by sand and sandbags, where we found a significant loss of strain amplitude between rock and fiber. Waveform similarity for strong earthquake signals is high with typical values of the normalized correlation coefficient greater than 0.95. The strain amplitude measured by all four IUs is very consistent and by about 10 per cent larger than the strain measured by the strainmeters. We attribute this minor discrepancy to spatially varying strain-strain coupling caused by the local topography. At frequencies up to 14 Hz we make an intercomparison of IUs, showing no significant variation with frequency. The scatter of strain amplitude in between channels which are spatially near to each other in the same fiber route is about ±10 per cent in most cases. The variation of median values in between different IUs and earthquakes is less than 5 per cent. By subtracting the common mode noise, which is coherent along the fiber route, we lower the background signal level to an rms-amplitude of 100 pstrain at 0.1 Hz and 5 pstrain at 1 Hz in a bandwidth of 1/6 decade for the best cases. This allows the detection of the marine microseisms during times of moderate amplitude level.

SummaryThe North Anatolian Fault (NAF) in the Sea of Marmara is characterized by varying mechanical behaviors and fluid manifestations along its path. In the present study, we investigate an earthquake sequence including a Mw 4.6 earthquake that occurred on November 27, 2013, in the partially creeping section of the NAF in the Sea of Marmara. Using onshore and offshore seismic data from October 2013 to August 2014, we document a sequence of highly-similar events prior to this earthquake, occurring within a zone where repeating earthquakes, likely indicative of creep, have been previously identified. This sequence coincides with the recently proposed existence of a several months-long slow-slip event (SSE) propagating westwards along the main fault trace at a rate of ∼0.5 km/day, based on the interpretation of onshore geodetic data and offshore surface sediment pore pressure data. The timings of the multiplet (i.e., the highly similar events), mainshock and decreases in pore pressure measured within the fault valley are compatible with the hypothesis that the SSE impacted first the multiplet-mainshock sequence and then the sub-seafloor hydrological network within the fault valley. Models of the SSE as a slipping patch propagating westward with a combination of right-lateral slip and tensile opening, show an increase in Coulomb stress of a few tenths of kPa at the position of the mainshock sequence. Despite limited calculated Coulomb stress transfer due to the SSE, our observations suggest that complex hydromechanical interactions exist between splay faults, such as those where the mainshock occurred, and the main NAF, and underline the interest of using multi-disciplinary data to study relationships between fluids and fault deformation.

SummaryWe investigate Reservoir-Triggered Seismicity (RTS) associated with the Castanhão Reservoir in northeastern Brazil, located within an intraplate region. Using continuous waveform data from a local seismic network (2009–2010), we construct a high-resolution earthquake catalogs of 227 events through template matching (PyMPA) and probabilistic location methods, with subsequent double-difference relocation improving spatial resolution for 103 events. The seismicity forms two clusters delineating a steeply dipping (∼80°) fault zone characterized as a reactivated strike-slip system with secondary transtensional/stepover structures within the Jaguaribe Shear Zone corridor, with moment magnitudes ranging from Mw 0.0 up to Mw 2.7. To assess triggering mechanisms, we model pore-pressure diffusion induced by reservoir impoundments. The estimated hydraulic diffusivity (∼0.080 m²/s) and spatiotemporal evolution of seismicity indicate a response governed by fluid pressure diffusion. Pore-pressure increases on the order of tens of kPa are sufficient to activate critically stressed fault segments, consistent with previous RTS observations. The seismic sequence exhibits swarm-like behavior, interpreted as progressive, fluid-driven rupture within a segmented fault network. We further analyze the temporal evolution of seismic strain using Seismic Strain Dynamics (SSD) and seismic regularity (dc) metrics, revealing patterns consistent with a multi-stage earthquake preparatory process. The largest event in the main cluster (Mw 2.1) occurs during a late-stage failure phase, following prolonged fault conditioning. Our results highlight the role of reactivated, critically stressed secondary strike-slip fault networks as hydraulically favorable conduits for pore-pressure diffusion and RTS. The study demonstrates that even moderate pore-pressure perturbations can induce delayed, swarm-type seismicity in intraplate settings, emphasizing the importance of integrating geological structure, hydromechanical processes, and seismic monitoring for improved hazard assessment near large reservoirs.

SummaryWe evaluated periodic pumping tests carried out in boreholes penetrating fractured gneiss in the research mine “Reiche Zeche”, Freiberg, Germany, employing a fractional diffusion equation, known as the generalized radial-flow (GRF) model. Water was injected into three borehole intervals, successively isolated by a double-packer probe. The three intervals correspond to distinct hydraulic conduits: (i) an induced axial fracture, (ii) a stimulated pre-existing radial fracture, and (iii) a fault zone intersecting the borehole at a near-normal angle. Pressure responses were monitored in sections of up to four monitoring boreholes. We evaluated the collected data by two spectral methods, injectivity analysis and interference analysis. The former exploits flow rate and pressure of the injection interval, whereas the latter relies on pressure records only, namely that of monitoring sections and of the injection intervals. In the GRF-framework, interference analysis yields a unique pair of flow dimension and hydraulic diffusivity, while the two observables of the spectral injectivity analysis, amplitude ratio and phase shift, only constrain admissible ranges of the three involved unknowns, flow dimension, hydraulic diffusivity, and conductivity. We addressed this under-determinateness by two combinations of spectral parameters from injectivity and interference observations. Even when accounting for the effects of finite length, storage capacity, skin, and non-parallelism of injection intervals and monitoring sections on the spectral parameters, it seems difficult to reconcile the bulk of our observations with the predictions of the GRF model. In cases, however, the gained fractal flow dimensions may bear geometrical significance, as for the likely anastomosing fault zone. The decrease in fractional flow regime with increasing mean interval pressure observed for two hydraulic paths between injection intervals and monitoring sections points towards a role of hydromechanical coupling in flow localization.

SummaryCertain geophysical inverse problems may be reduced to systems of sparse low-order polynomial equations. In simple systems of this type, the monomials are products of nodal values of a subsurface physical property, such as electrical conductivity, interior to a discretized modeling domain, whereas the coefficients are derived from the given boundary data. The latter are presumed to be over-specified by means of surface measurements made across a range of frequencies. Suppose an acceptable conductivity model is found, by any standard means, that fits the data. Then, provided other distinct, non-singular solutions exist, it is possible to find another one of them by charting a trajectory across model space using a path-tracking numerical method. A second path can then be tracked and, continuing in this manner, equivalent solutions to the inverse problem can be generated one by one. If the data are noisy, the equivalent models generate precisely the same numerical misfit. Regularization side constraints can be added which reduces the number of equivalent models and smooths the trajectories. This idea of tracking paths has been developed in the field of robotics where realizable configurations of kinematic mechanisms are sought; a similar approach is explored here, on small test systems, to explore its eventual applicability to practical geophysical inverse problems. In principle the method scales up to arbitrary size since the inverse of the forward matrix and the Jacobian can be expressed in polynomial form. The novel implications of a successful scaled-up implementation of this methodology is that multiple distinct inverse solutions can be obtained, each of which might entail a different geological interpretation. A surprising feature of the algorithm is that the forward problem becomes ill-posed while the inverse problem becomes well-posed.

SummaryFull waveform inversion (FWI) is among the most accurate seismic imaging techniques, yet its Bayesian formulation remains computationally prohibitive. The gold-standard method, Markov chain Monte Carlo (McMC), requires hundreds of thousands of expensive forward simulations. Variational inference (VI) approaches offer improved efficiency but require gradient computations and may converge to incorrect posteriors. We introduce a probabilistic surrogate-assisted framework that replaces most expensive forward evaluations during sampling with predictions from a misfit-guided surrogate based on Deep Learning (DL) models, instantiated here using a convolutional neural network (CNN). The framework supports three regimes: (1) an offline regime in which the surrogate remains fixed after training, (2) an adaptive regime in which the surrogate is refined during sampling using a self-supervised active learning mechanism, and (3) a two-stage regime in which the surrogate screens proposals prior to exact evaluation. The two-stage regime preserves the exact posterior by construction, while the adaptive regime is asymptotically exact in the infinite-sample limit, provided that the surrogate approximation error vanishes. Because the target subsurface features in our study are spatially localised, we adopt a rock-physics-guided, shape-based parameterisation that represents each target by a small number of control nodes. This results in a low-dimensional inverse problem with a dimensionality of order 10 in the examples considered here, at which a misfit-guided DL surrogate becomes tractable. We demonstrate our framework on synthetic models of increasing dimensionality, including a CO2 plume monitoring scenario. The adaptive regime achieves a 7.1 × wall-clock speedup and reduces GPU-hour cost by approximately 84% relative to McMC with the exact forward solver, while recovering posteriors in close agreement with the reference. Comparison with Stein variational gradient descent (SVGD) shows the surrogate-assisted McMC sampling is approximately an order of magnitude more efficient in the examples considered here. This framework applies to any surrogate paired with any forward solver and objective function producing a scalar misfit.

SummarySeismic source classification often relies on a single best-fitting moment tensor and deterministic rules on source-type diagrams. This study formulates classification directly in terms of the posterior distribution of the moment tensor on the fundamental lune. Moment tensors from waveform inversion are mapped into a uniform (v, w) source-type domain and used to build empirical source-type probability density functions p(v, w). We evaluate five hypotheses (±ISOCAL, DC, ±CLVD) plus a residual baseline, where ISOCAL denotes a calibrated isotropic kernel relative to the canonical ISO endmember. Classification probabilities P(Hi) are obtained by integrating p(v, w) against the hypothesis kernels; a residual probability Presidual is included as a sixth baseline (uniform) model that absorbs probability when the posterior is diffuse or poorly matched by the prototypes. The framework is applied to published regional moment tensor ensembles for 32 events, including 23 nuclear tests at the Nevada National Security Site and the DPRK Punggye-Ri test site, plus nearby earthquakes and collapses. Well-recorded underground nuclear tests yield tightly localized +ISOCAL probabilities with small Presidual, while events recorded by sparse, complex source-receiver paths show broader probability vectors and larger Presidual. Tectonic earthquakes map primarily to double-couple and CLVD hypotheses, while collapses map primarily to negative isotropic (–ISOCAL), with secondary CLVD in some events. The method is applicable wherever source-type posteriors are available, given a declared hypothesis family, and produces explicit hypothesis probabilities with uncertainty and a residual diagnostic that flags diffuse or weakly matched posteriors, and can feed directly into monitoring and decision pipelines.

SummaryThe Marine Self-Potential (SP) method has proven to be a useful geophysical tool for localizing seafloor massive sulfide (SMS) deposits. Conventional geophysical inversion approaches face significant limitations in mid-ocean ridge environments with complex topography, including high computational requirements due to high degrees of freedom in grid discretization and the complex computation of sensitivities. Meanwhile, data-driven neural networks, while powerful, often suffer from poor generalizability and interpretability in complex deep-sea environments, owing to their reliance on large-scale training datasets. In this study, we propose a simple-formulated intelligent SP inversion framework, called SP-PINN, based on the Physics-Informed Neural Network (PINN). By embedding the governing equation of SP as a physical constraint into the loss function, SP-PINN effectively couples the intrinsic correlation between SP signals and subseafloor causative sources. To address the characteristics of topographic relief in mid-ocean ridges and discrete distribution of SMS ore bodies, 2D and 3D synthetic case studies were constructed to verify the inversion performance of SP-PINN. Two SMS deposit bodies on the Southwest Indian Ridge (SWIR) were successfully imaged and delineated through the application of SP-PINN to the field data. Our results demonstrate that accurate imaging of SMS can be achieved by introducing physical constraint terms into the neural network, properly optimizing hyperparameters, and incorporating a small amount of field data. This study provides a straightforward and physically interpretable SP imaging method for the assessment of SMS under data-scarce conditions.

SummaryShallow-water depth data serve as the essential foundation for marine spatial planning and coastal engineering projects. However, due to the attenuation of spectral signals, traditional retrieval of shallow-water seafloor topography struggles to maintain high accuracy in highly turbid waters and deeper areas. Furthermore, existing machine learning models incorporating spatial information are highly susceptible to spatial overfitting, which severely limits their generalization ability in complex waters. To address these issues, this study proposes a depth-stratified Extreme Gradient Boosting (XGBoost) approach combining Sentinel-2 remote sensing data and multi-source gravity data. This model innovatively incorporates gravity features, which are unaffected by water turbidity, effectively overcoming the signal attenuation limitations of spectral-only models in deep and turbid waters. In addition, the stratification strategy effectively mitigates spatial overfitting, which commonly affects machine learning models. Application results in the Pearl River Estuary, China, showed that compared to the unstratified spatially uncorrelated XGBoost global model, the root mean square error (RMSE) of the proposed model was significantly reduced by 1.362 m. Ablation experiments further confirmed that incorporating gravity features reduced the RMSE in deeper-water areas (depth > 30 m) by 1.033 m, and dropped the RMSE in highly turbid waters from 1.548 m to 0.955 m. Finally, validation in an adjacent area showed that the model maintained useful local spatial generalization under similar environmental conditions, with an RMSE of 0.591 m in the 30–35 m interval.

SummaryAccurate determination of rate-and-state friction parameters from experiments is crucial for physics-based modeling of the earthquake source. In this study, we develop a Bayesian inversion framework based on a spring-slider model of the experiment to infer rate-and-state frictional properties from stable velocity-step experiments of a fine-grained quartz gouge that is close to instability. The inversion framework infers frictional parameters with superior precision to conventional methods based on non-linear least squares. We identify that logarithmic sampling of the characteristic slip DRS is an essential step for accurate inversion. Testing the same gouge material under the same effective normal stress but different pore pressure additionally shows that the presence of pressurized pore fluids changes the inferred rate-and-state properties of the gouge material by increasing rate-and-state parameters a, b, and DRS. The Bayesian inversion framework provides a valuable tool for inferring accurately rate-and-state parameters of realistic gouge materials close to instability, with a small and quantifiable range of uncertainty in cases where the steady-state behavior quantified by a − b can be constrained independently. We also find that the steady-state friction properties evolve over the course of the experiment, adding additional uncertainty to the rate-and-state parameter estimates but not altering the conclusion about significant differences between the rate-and-state parameters in the presence of fluids.

SummaryEvaluating earthquake forecasts is a crucial step in understanding and improving the capabilities of forecasting models. The use of specific metrics to assess the consistency between forecasts and data on one particular aspect of the process is important to understand which aspects of seismicity a model is failing to describe, and, consequently, highlight where new versions of the model should improve. This can be done effectively only by metrics unaffected by inconsistencies in other aspects of the process. The Collaboratory for the Study of Earthquake Predictability (CSEP), which organises earthquake forecasting experiments around the globe, has developed different tests targeting different aspects of the process, such as the number N-test or the magnitude M-test, assessing (in)consistency between the observed and forecasted number and magnitude distributions of events, respectively. We find that the results of the recently proposed M-test for catalog-based forecasts (composed of a collection of synthetic catalogs from the model) depend on the N-test, i.e. the two tests do not isolate the desired aspects appropriately, rendering uninformative forecast results. Here, we address this problem using simulated data and provide a solution based on resampling of simulated catalogs. We implement this new M-test with resampling in the pyCSEP software toolkit, conduct two analyses using actual earthquake forecasts for Europe (1990-2015) and Switzerland (1933-1962, 1962-1992, 1993-2022) that provide inconsistent earthquake counts compared to observations, and analyse how the test results change using this proposed test. Lastly, we investigate alternative metrics, namely an unnormalised M-test, two Chi-square formulations, the Hellinger distance, the Brier score, and a novel Multinomial Log-Likelihood (MLL) score, and compare them based on the ability to find (in)consistency between data and forecast in various synthetic scenarios. We find that there are scenarios in which the alternative metrics outperform the resampled M-test. In particular, the MLL test outperforms the M-test in all the scenarios considered. We also study how the ability of finding inconsistency changes with the number of observations, and the cutoff magnitude, comparing one of the Chi-square metrics, the MLL, and the M-test against classical statistical methods such as the Kolmogorov-Smirnoff (KS) test, the Wilcoxon test, and the Anderson-Darling (AD) test. The MLL and the AD test are the ones providing the highest probability of finding inconsistencies for all combinations of number of observations and cutoff magnitude. This study shows how realistic synthetic examples can be used to compare the ability of different metrics in finding inconsistencies between data and forecasts, and shows that the MLL is the metric providing the best tradeoff between interpretability and probability of finding inconsistencies and, therefore, should be used.

SummaryFull waveform inversion (FWI) has significant potential to reconstruct the subsurface structure at high resolution, but it is a nonlinear and ill-posed problem. Traditional physics-based FWI methods that employ gradient-based optimization algorithms to minimize the discrepancy between observed and synthetic data face several challenges. The aforementioned methods are susceptible to becoming trapped in local minima as a result of cycle-skipping problems and require substantial computational resources, particularly with the increasing size and required resolution of seismic datasets. Nowadays many deep-learning methods are proposed to solve the above challenges. FWIGAN, a recently proposed unsupervised seismic velocity inversion method based on the Wasserstein generative adversarial network with gradient penalty (WGAN-GP), has demonstrated certain success in overcoming these challenges. However, FWIGAN enforces the Lipschitz constraint on the critic through a gradient penalty evaluated at dynamically sampled interpolation points between the observed and synthetic data. This strategy introduces additional computational and hyperparameter tuning costs. Motivated by these limitations, we introduce the spectral normalization into the FWIGAN framework, directly constraining the spectral norm of each network layer to control the Lipschitz behavior of the critic. It can enhance the stability of network training. The proposed method mitigates the dependence on having large labeled data and the pretraining of the network. The new scheme is applied to the Marmousi and Sigsbee models to verify its robust performance. The experimental results clearly indicate that our method outperforms common methods in terms of inversion accuracy.

SummaryPolar motion (PM) provides an independent, observation-based constraint on global mass redistribution, but interpretation of its long-term variability is primarily limited by uncertainty in the glacial isostatic adjustment (GIA) contribution, for which conventional model-based estimates exhibit substantial differences. Here we develop a refined approach that uses primarily GRACE and GRACE Follow-On (GRACE-FO) observations to estimate the secular PM associated with non-surface-loading processes, predominantly GIA. We restore the pole tide contribution removed during standard GRACE processing to reconstruct the total degree-2, order-1 spherical harmonic coefficients and the corresponding PM excitation, then isolate the non-surface-loading component as the difference between this excitation and the GRACE/GRACE-FO-derived excitation from contemporary surface loads and pole tides. This strategy avoids uncertainties associated with atmospheric winds, ocean currents, and core–mantle processes present in geodetic PM records. The resulting secular trends are 1.77 mas/yr in χ1 and − 2.49 mas/yr in χ2, differing markedly from commonly used GIA model predictions (e.g. 1.01 and − 4.67 mas/yr from Peltier et al., 2018). We validate our estimate by reconstructing the PM excitation for 1992–2016 using a comprehensive set of cryospheric, hydrologic, atmospheric, and oceanic contributions together with our inferred secular term; the reconstruction agrees closely with geodetic observations. Compared with the previous PM-based approach of Seo et al. (2021), our method yields a substantially smaller 95 per cent confidence region, providing an improved characterization of the long-term secular contribution to PM and a stronger foundation for using multi-decadal PM observations to diagnose twentieth-century ice mass and terrestrial water storage changes.

SummaryIndustrial activities in active foreland fold-and-thrust belts complicate conventional seismic hazard assessment, yet how tectonic deformation and industrial perturbations affect fault stability remains poorly understood. The Hutubi underground gas storage facility, situated within the Hutubi anticline at the deformation front of northern Tian Shan, has experienced a marked increase in seismicity since the start of gas injection and extraction. Here, we integrate seismic-reflection interpretation, growth strata analysis, geomorphic surface survey and optically stimulated luminescence dating, and area-depth-strain analysis to reconstruct the structure and growth history of the Hutubi anticline and quantify its late Quaternary shortening rate. The Hutubi anticline is a detachment fold growing above an 8–9 km‐deep detachment. The shortening and uplift rates of anticline have undergone a marked acceleration, rising from long-term averages of ∼0.06 and ∼0.03 mm/yr initiated in the Pliocene to ∼1.1 and ∼1.3 mm/yr since ∼0.4 Ma, respectively. This acceleration, consistent with other foreland structures, reflects ongoing basinward propagation of the Tian Shan deformation front in the late Quaternary. Within this tectonic setting, we further modeled the Coulomb stress evolution on secondary faults within the anticline driven by tectonic deformation. Our results indicate that tectonic deformation drives secondary faults to the seismic triggering threshold over a decade, and governs where ruptures nucleate and how they propagate, consistent with relocated earthquake observations in previous studies. Given sustained late Quaternary activity and pronounced injection-extraction perturbations, the potential for moderate-to-strong earthquakes (∼Mw5.3–7.0) on these faults cannot be ignored in the future. Thus, in active tectonic regions, seismic hazard assessments must incorporate deformation rates to accurately evaluate tectonic deformation and industrial perturbation effects on fault stability.

SummaryWhile well-log data provide direct measurements of subsurface properties essential for building lithofacies models, their sparse distribution and limited availability in exploration areas introduce significant uncertainty. This study presents an approach that integrates seismic data, rock physics, machine learning, and geostatistics to construct a regional-scale three-dimensional (3D) lithofacies model in the Jeju Basin, located offshore the southern Korean Peninsula. The proposed workflow comprises three integrated steps: performing Simultaneous Pre-Stack inversion on processed seismic data to derive rock properties, training neural networks for lithofacies prediction, and expanding the two-dimensional (2D) predictions into a 3D lithofacies model using Sequential Indicator Simulation. The prediction networks reproduced the interpretation-derived lithofacies labels with reasonable accuracy for the primary sedimentary classes, as demonstrated by quantitative evaluation metrics. The resulting 3D lithofacies model captured key geological features indicated by 2D seismic data, including faults and sedimentary structures, exhibiting improved spatial continuity and detail compared to conventional well-log-only geostatistical simulation. Comparison with a borehole outside the training volume demonstrated reasonable agreement between the predicted and well-log-derived lithofacies. This observation preliminarily implies the workflow’s applicability to field data beyond the 3D seismic coverage, though this assessment is based on a single well location. This integrated approach offers a cost-effective solution for lithofacies characterisation in frontier exploration sites where regional 3D seismic surveys are economically unfeasible and well control is limited.

SummarySeismic inversion plays a critical role in oil and gas exploration and CO2 storage monitoring by providing accurate subsurface structural imaging and quantitative characterization of rock properties, which offer valuable insights into reservoir characteristics, fluid flow, and storage integrity. However, traditional model-driven approaches are computationally expensive and heavily reliant on accurate initial models, which limits their applicability in complex geological settings. Furthermore, the need for manual hyperparameter tuning and expert intervention impedes automation and reduces their suitability for time-sensitive tasks such as real-time monitoring. Recent advancements in deep learning have demonstrated significant potential for seismic inversion by enabling neural networks to capture complex, nonlinear relationships between seismic data and subsurface properties. However, current data-driven methods still face major challenges, including limited generalization across diverse geological settings, poor interpretability due to the black-box nature of neural networks, and inadequate control over resolution and inversion fidelity. To address these limitations, we propose an implicit seismic inversion (ISI) approach that models subsurface physical parameters as a continuous function, implicitly represented by a deep neural network. Specifically, this study focuses on both post-stack impedance inversion, which recovers acoustic impedance as the key physical property, and pre-stack AVO inversion, which simultaneously inverts for P-wave velocity, S-wave velocity, and density. In this way, ISI leverages the learning dynamics of neural network optimization to reduce dependence on prior information, while enhancing interpretability and generalization ability through physics-informed constraints. Moreover, the continuous nature of the representation can significantly reduce the computational burden without compromising inversion accuracy. Experimental results on both synthetic and field datasets demonstrate that the proposed ISI method surpasses existing approaches in terms of accuracy, convergence, and independence from prior information. These advantages position ISI as a promising framework for scalable, automated, and real-time seismic inversion in next-generation geophysical workflows.