Geophysical Journal International

SummaryOceanic transform faults (TFs) are fundamental elements of plate tectonics and have traditionally been viewed as conservative strike-slip boundaries. Seafloor observations and numerical modelling suggest the existence of extensional stress, however how it manifest at depth remains unknown. Moreover, slow-slipping TFs are often associated with thin crust and possible exposures of serpentinised peridotite near the seafloor. Here we apply full waveform inversion (FWI) to a 12-km offset seismic dataset across the Romanche TF, the largest TF on the Earth. We use source-receiver reciprocity and downward continuation to emulate a split-spread ocean bottom cable survey geometry from one-sided surface streamer data, bringing the refracted waves ahead of reflections while accounting for rough seafloor topography. We then perform travel time tomography followed by FWI to the downward-continued data to derive a high-resolution crustal model. The resolution is about 0.7 km horizontally and 0.4 km vertically, down to 3.5–4 km depth from the seafloor. Our results reveal low P-wave velocity in the upper 3 km, suggestive of basaltic origin, and no evidence for high velocities characteristic of serpentinised peridotite on the valley floor. Moreover, we image inward dipping normal faults extending to ∼4 km depth, forming a flower-like structure. Regional earthquake data reveal strike-slip mechanisms along the transform and normal-faulting near the RTI, with strike-slip hypocenters aligning with interpreted faults. These features suggest that the Romanche TF resembles a trans-tensional regime with a deep-rooted strike-slip fault in the middle, accommodating local strain deformation.

SummaryAdvances in satellite gravimetry technologies have enabled the integration of increasingly diverse mission datasets for high-resolution static gravity field modeling. However, during the construction of regularization matrices for stabilizing Spherical Harmonic Coefficients (SHCs), conventional regularization methods generally neglect significant correlations among SHCs, primarily due to heterogeneous noise characteristics of observations from different missions. To address this limitation, we propose a Full Signal Variance-Covariance (FSVC) regularization method by constructing a full regularization matrix based on a priori gravity anomaly signal amplitudes. Applying this method to combined normal equations integrating GOCE SGG, GRACE, and Swarm observations yield three solutions under different constraint strategies: a Kaula diagonal constrained solution (Tongji-GMMG2025S-KLA), a Diagonal Signal Variance-Covariance (DSVC) regularized solution (Tongji-GMMG2025S-DSVC) derived from the diagonal elements of the FSVC matrix, and the FSVC-regularized solution (Tongji-GMMG2025S-FSVC). Our analyses demonstrate that: Based on FSVC analysis, the proposed FSVC regularization method exhibits overall superior performance compared to the diagonal regularization approach, particularly when the prior model incorporates terrestrial gravity data. Even when using the Kaula-constraint solution as the prior model, quantitative evaluations in both spectral and spatial domains demonstrate that the FSVC-regularized solution still exhibits significantly improved performance relative to diagonal regularization schemes. In the degree range 151–300, the Tongji-GMMG2025S-FSVC model reduces cumulative geoid error degree variances by 9.28 per cent and 9.58 per cent compared to the Tongji-GMMG2025S-KLA and Tongji-GMMG2025S-DSVC solutions, respectively, indicating more effective suppression of medium- to high-degree noise. Spatial comparisons with the XGM2019 model further show reduced gravity anomaly discrepancies, with the FSVC solution achieving the lowest global standard deviation (4.94 mGal). Notably, this improvement is particularly evident in the Indonesia region, which is characterized by complex land-sea distributions. Independent validation using GNSS/Leveling data demonstrates that the FSVC-regularized solution overall higher accuracy than the diagonal-constrained solutions. In particular, the Tongji-GMMG2025S-FSVC model exhibits a distinct advantage, achieving noise reductions of 9.15 per cent and 8.53 per cent relative to the Tongji-GMMG2025S-KLA and Tongji-GMMG2025S-DSVC solutions in the Canadian region, respectively. In conclusion, the proposed FSVC regularization approach proves highly effective in suppressing high-degree noise and enhancing the accuracy of satellite-only static gravity field solutions. This improvement highlights the potential applicability of the proposed approach for future multi-satellite gravity mission integration.

SummaryMachine learning offers new opportunities for geophysical inverse problems, yet conventional regularized inversions of potential field data remain limited by global smoothing constraints and low structural resolution. We propose a locally adaptive, data-driven framework that combines synthetic Earth model generation and ensemble learning for joint gravity and magnetic interpretation. Training models are generated using geologically informed Voronoi-based geometries and planar structures, and a random forest classifier is trained on local statistical features of gravity and magnetic anomalies. The method yields geologically consistent subsurface models that reproduce observed anomaly characteristics without explicit regularization or iterative inversion. Compared with nonlinear Bayesian and traditional regularized inversions applied to the same dataset, the approach provides a substantial reduction in computational cost while preserving key structural features. The performance of the method is inherently linked to how representative the training ensemble is with respect to the target structure, and the results should be interpreted within this context. This framework demonstrates a practical and efficient alternative for potential field inversion using machine learning.

SummaryThis paper describes the implementation of the direct solution method (DSM) using radial spectral elements for the calculation of synthetic seismograms in self-gravitating, spherically symmetric, non-rotating, anelastic, and transversely isotropic Earth models. In contrast to previous implementations of the DSM that used a potential formulation within fluid regions, we use a displacement formulation throughout. It is this feature that allows us to extend the DSM to account fully for self-gravitation along with arbitrary fluid stratification. Our code, DSpecM1D, is benchmarked against the normal mode summation code specnm as well as the direct radial integration code YSpec. Agreement between the codes is excellent for both elastic and anelastic models.

SummaryCarbonate dissolution represents a key mechanism for slab carbon release in oceanic subduction zones. However, the magnitude and controlling factors of carbonate dissolution remain unclear. Here, we develop a coupled thermo-petrological modeling method that integrates slab dehydration, carbonate mineral abundances and their solubilities into subduction-zone thermal models. Systematic model results establish a quantitative relationship between the dissolved CO2 outflux and the subduction-zone thermal parameter (here defined as φ = slab age × subduction velocity/100 in kilometers), which reveals a peak outflux at φ ≈ 13 km, corresponding to warm subduction zones. The dissolved CO2 outflux exhibits a sublinear increase at φ < 13 km and an exponential decline at higher φ. This indicates that warm subduction zones with moderate thermal parameters provide the favorable thermal conditions for carbonate dissolution. The style of aqueous fluid migration strongly influences both the pattern and magnitude of carbonate dissolution. In the pervasive-flow system, fluid infiltration substantially enhances the dissolved CO2 outflux, producing magnitudes approximately three times higher than those in the channelized-flow system. The specific model results for three representative subduction zones—hot Cascadia, warm Nicaragua, and cold Hokkaido—confirm that warm Nicaragua exhibits higher dissolved CO2 outflux, potentially explaining its high arc CO2 degassing outflux.

SummaryDistributed Acoustic Sensing (DAS) using metropolitan telecom fibre-optic cables provides an unprecedented opportunity for seismic monitoring in sedimentary basins, exemplified by Mexico City. In this study, we analyze 15 months of nearly continuous DAS measurements to identify previously undetectable details of wave propagation, thereby enabling the precise localization of local earthquakes. Using real seismic velocity models, we overcome the inaccuracies of traditional constant ${{V}_P}/{{V}_S}$ approaches, highlighting significant limitations of Wadati diagrams in sedimentary environments. Our results reveal clear hydro-seismic coupling, where intense early-season rainfall, coinciding with low aquifer levels, generates sufficient stress perturbations to trigger moderate-magnitude earthquakes (Mw ∼ 3.5). These main events subsequently induce slow slip along local faults and secondary seismicity on a perpendicular plane, driven primarily by stress imbalance rather than fluid involvement along faults. We further identify basin-converted and conical phases as dominant sources of ground shaking, underscoring the urgent need to integrate these secondary seismic phases into urban seismic hazard assessments and building codes. Our findings underscore the crucial role of continuous DAS measurements in comprehending urban seismic risk and managing aquifer resources, thereby establishing a robust monitoring framework with global applicability in sediment-filled megacities.

SummarySeismic reflections from mantle discontinuities provide critical constraints on the structure and dynamics of Earth’s interior, but their extraction remains challenging due to low signal-to-noise ratios (SNR), interference from other seismic phases and uneven spatial distributions. In this study, we propose an array-based extraction strategy that integrates data reconstruction with subsequent denoising for enhancing the extraction of weak mantle reflections. This proposed strategy is independently implemented using the Curvelet-, F–K, and Radon transforms, and the performance of the three implementations is systematically evaluated. Compared with the time-space domain, coherent signals and noise are more easily separated in the transformed domain. We apply these methods to synthetic waveforms generated using a modified ak135 Earth model and test their effectiveness in retrieving reflections from the mantle transition zone (SS/PP precursors) and the D″ discontinuity (ScS/PcP precursors), including cases with random noise and missing traces. All three methods effectively isolate weak mantle reflections, with the Curvelet transform demonstrates the highest robustness and SNR improvement, particularly under conditions of sparse or noisy data. Field applications to data sampling the Central Pacific and Central America further confirm the methods’ ability to recover weak mantle reflections and expand the distance range of usable data. These results demonstrate the potential of array-based extraction strategy to advance deep Earth seismic imaging.

SummaryThe Earth’s figure axis is the axis of maximum inertia for the deformed (oblate) Earth, as described by the degree-two, order-one geopotential coefficients C21 and S21. An extended mean-pole model is presented for evaluating solid-Earth and ocean-pole tides. 50-year Satellite Laser Ranging (SLR) data and 24-year GRACE/GRACE-FO data were analyzed to determine variations in Earth’s figure axis, as reflected in changes in the C21 and S21 coefficients. This study reveals that a significant atmosphere-ocean motion induced a variation in C21 that is captured by SLR data but does not appear in the GRACE solution. The current glacial isostatic adjustment (GIA) ICE-6G model requires improvement to account for the observed linear rates of C21 and S21. A significant 30-year and 60-year signal with an amplitude of ∼ 3×10−11 in the Earth’s figure axis is observed using SLR and a ∼10 mas in the PM (polar-motion), which could be predominantly driven by a 0.05-degree tilt of the inner-core figure axis relative to the figure axis of the entire core and is linked to partial electromagnetic core-mantle coupling.

SummaryContinuous, high-density strain and strain-rate distributed acoustic sensing (DAS) recordings are valuable for resolving the shallow Earth’s structure at a low cost, especially in environments that are otherwise difficult to access, such as continental shelves and near-coastal oceanic crust. In this study, we apply seismic ambient-noise methods to extract high-quality empirical Green’s functions (EGFs) from natural noise sources and model the velocity structure along a 30-km-long dark fiber-optic cable connecting the offshore CASTOR gas storage field in the Gulf of Valencia (Spain) with the associated land facility. We extract broadband EGFs containing a rich variety of seismic waves using wavelet phase cross-correlation and time-scale phase-weighted stacking methods. In the common-source EGF gathers, clean fundamental and first-overtone Scholte waves dominate the marine channel pairs, while the fundamental Rayleigh mode appears in the land channel pairs. In addition, weak wavefields reflected from the basin edge follow the main surface waves. We then construct a 2-D Vs model from local phase-velocity observations of the fundamental and first-overtone Scholte waves by solving pointwise depth inversions using Markov chain Monte Carlo methods. The model resolves the marine sedimentary basin from very shallow water-saturated sediments to depths exceeding 1 km, identifying the Amposta Central Fault and the basement bedrock west of this fault at roughly 1 km depth. These results help refine the offshore velocity model along the cable in a region where induced seismic activity has been observed, improving the accuracy of seismic monitoring and seismic hazard characterization.

SummaryThe conductive and capacitive properties of rocks are influenced by the type and concentration of the electrolyte present in the pore water. Sodium (Na⁺) and potassium (K⁺) are common pore water cations in saturated sedimentary rocks. Their distinct physicochemical properties are expected to produce different frequency-dependent electrical dispersion when adsorbed onto mineral surfaces. We tested this expectation by using spectral induced polarization (SIP), a method sensitive to interfacial processes. Complex conductivity spectra (10–2 to 105 Hz) were measured on two clayey, opal-A-rich diatomite samples, saturated with either NaCl or KCl solutions. One sample was tested over a stepwise increase in molar concentration (5.4–53 mM), while the other was tested over a stepwise increase in bulk water conductivity (0.050–0.48 S/m). At equivalent molar concentration, the in-phase conductivity of a sample was ~20 per cent higher when KCl saturated than when NaCl saturated, reflecting the greater molar conductivity of K⁺. At matched bulk water conductivity, which required a ~20 per cent higher NaCl molarity than KCl molarity, in-phase conductivity was ~10 per cent higher when NaCl saturated. In both tests, the quadrature conductivity and normalized chargeability followed a lower trend in the KCl-saturated state than in the NaCl-saturated state. This relatively low polarization for the K+ saturated state can be attributed to a weaker hydration and more compact adsorption of K⁺ within the inner layer of the electrical double layer. Additionally, time-lapse monitoring of complex conductivity spectra indicates that chemical equilibration via diffusion is achieved within 72 hours for both electrolyte types. This relatively rapid ionic diffusion is consistent with estimates based on the intrinsic formation factor and probably reflects the high porosity of the diatomite (~0.7). These findings establish that pore-water cation identity (Na⁺ vs. K⁺) is a primary control on SIP-derived polarization parameters, and cation identity must therefore be incorporated into petrophysical models to avoid biased estimates of surface area, permeability, and hydrogeochemical state.

SummaryImages of the Earth’s interior can provide us with insight into the underlying properties of the Earth, such as how seismic activity might emerge and the interplay between seismic and volcanic activity. Understanding these systems requires reliable high-resolution images to understand mechanisms and estimate physical quantities. However, reliable images are often difficult to obtain due to the non-linear nature of seismic wave propagation and the ill-posedness of the related inverse problem. Reconstructions rely on good initial estimates as well as hand-crafted priors, which can ultimately bias solutions. In our work, we present a 3D reconstruction of Kilauea’s magmatic system at a previously unattained resolution. Our eikonal tomography procedure improves upon prior imaging results of Kilauea through increased resolution and per-pixel uncertainties estimated through variational inference. In particular, solving eikonal imaging using variational inference with stochastic gradient descent enables stable inversion and uncertainty quantification in the absence of strong prior knowledge of the velocity structure. Our work makes two key contributions: developing a stochastic eikonal tomography scheme with uncertainty quantification and illuminating the structure and melt quantity of the magmatic system that underlies Kilauea.

SummaryThe global energy transition has created an urgent need for expanded critical mineral supply. Projected production from existing deposits and current discovery rates remains insufficient to meet this demand. More efficient exploration strategies are therefore required, particularly in optimizing costly and low-success-rate data acquisition campaigns. To address this challenge, we introduce the concept of sequential Efficacy of Information (sequential EOI), a decision-making metric that quantifies the uncertainty reduction of target variables under proposed exploration action sequences. Unlike Value of Information (VOI), sequential EOI operates in the domain of uncertainty reduction, removing the need for an economic model that is rarely available in early-stage exploration. We demonstrate the framework using synthetic 2D and more realistic 3D porphyry copper systems, evaluating sequential combinations of exploration plans including ambient noise tomography (ANT) surveys and borehole drilling campaigns. In both cases, sequential EOI identified exploration plans that maximized the uncertainty reduction to the target variables. These results demonstrate that sequential EOI offers a principled framework for multi-physics, multi-step, and uncertainty-driven plan optimization in mineral exploration, providing exploration teams with a practical and scalable decision-analytic tool for rational campaign design without requiring economic assumptions.

AbstractAccurate and rapid magnitude prediction is critical for earthquake early warning systems, directly affecting emergency response decisions and public safety. With global seismic monitoring networks expanding to over 15,000 stations and the emergence of crowdsourcing-based IoT device monitoring systems, daily seismic data has reached petabyte scales, posing enormous challenges for real-time processing under the typical 3-10 second warning window constraint. Existing deep learning methods predominantly adopt single-modal information processing strategies, focusing either solely on temporal features of time-domain waveforms or spectral information after frequency-domain transformation, failing to fully exploit the joint evolution patterns and complementary information of seismic signals in the time-frequency domain, thereby limiting prediction accuracy and generalization performance. This paper proposes MP-Net, an end-to-end deep learning framework based on multi-scale time-frequency fusion for local magnitude (ML) prediction. The method employs a dual-branch architecture that simultaneously processes raw three-component waveforms and spectrograms: the time-domain branch captures features from microscopic waveform details to macroscopic energy evolution through parallel multi-scale convolutions; the frequency-domain branch combines hierarchical 2D convolutional networks with adaptive spectral attention mechanisms to automatically identify magnitude-related frequency components while suppressing noise; a cross-attention based fusion module achieves deep integration of complementary information from both modalities. To preserve the absolute amplitude information physically consistent with the ML definition, logarithmic amplitude features are extracted prior to waveform normalization and provided as auxiliary inputs to the fusion layer. Comprehensive experiments on the large-scale STEAD dataset demonstrate substantial improvements over baseline models: mean absolute error decreased to 0.28, coefficient of determination R2 reached 0.872, with 82.5% of predictions achieving acceptable precision (error≤0.5). The proposed approach provides an efficient and accurate solution for real-time single-station magnitude prediction, applicable to earthquake early warning systems operating in both centralized and distributed computing environments.

SummaryIn marine seismic exploration, various types of seismic sources are employed to visualize geological structures beneath the seafloor, depending on survey objectives. Airgun sources, which generate large amounts of energy by releasing compressed air underwater, are typically used for imaging deep area; however, they have limited vertical resolution due to their low peak frequencies. In contrast, sparker sources generate wavelets with high peak frequencies using bubbles produced by discharging electrical energy to vaporize water, resulting in high vertical resolution. Sparker sources are useful for the detailed imaging of shallow strata but have a shallow penetration depth due to their low source energy. This paper proposes a method to integrate airgun and sparker data to broaden the frequency bandwidth and thus achieve more accurate geological interpretations. The study used small-scale airgun data and sparker data acquired in Yeongil Bay, Pohang, South Korea. A machine-learning-based shaping filter model was developed along with synthetic training data representing the airgun and sparker source wavelet characteristics, and the trained models were applied to regularize these source wavelets. Subsequently, time-variant spectral whitening (TVSW) and weighted integration were performed to yield the flattened broadband frequency spectrum. The integrated data have enhanced penetration depth and vertical resolution compared with the original single-source datasets, thus overcoming the interpretational limitations imposed by their limited frequency bandwidth and penetration depth and enhancing the reliability of associated geological interpretations.

SummarySite amplification in the Kumamoto area, Japan, is analyzed using 985 high-quality horizontal strong-motion records from 45 aftershocks (Mj = 2.7–4.9) recorded within 24 hours following the 2016 Kumamoto Mj 7.3 earthquake, as observed by 51 K-NET and KiK-net stations. For the generalized inversion technique (GIT), a reference station is required as a standard. In the GIT process, the number of events available for analysis is limited to those recorded by the reference station, and the stations whose site effects can be estimated are restricted to those that record common events with the reference station. To overcome the limitation of the GIT, the ‘transfer-station generalized inversion method (TSGI),’ a modified GIT, is introduced to increase the number of analyzed events and stations. The site responses obtained from GIT and TSGI for the same stations exhibit a high degree of consistency, thereby demonstrating the effectiveness of the TSGI. The discrepancies between the ${{Q}_S}$ estimates of GIT and TSGI can be attributed to the gradual expansion of the region represented by ${{Q}_S}$ as more events and stations are included in the inversion. However, the results of GIT and TSGI are relative to the reference station that may itself exhibit site effects. Thus, a reference-independent technique, i.e. genetic algorithm (GA), is also introduced to obtain the absolute site amplifications. The results show that at frequencies greater than about 1 Hz, the site response of the reference station is significantly lower than the theoretical amplification factor of 2, resulting in an overestimation of the site responses at other stations. When the results of GIT are corrected with the site response of the reference station obtained from GA, these two results agree very well for most of the stations. This indicates that the results of GIT are reliable if the reference station is an ideal surface rock station, and that the GA produces accurate absolute site amplification factors for the stations investigated in this study. In addition, we analyze the high-frequency attenuation characteristics of S-waves in the Kumamoto area, and establish $\kappa $ models for different site conditions and an empirical ${{\kappa }_0}$-${{V}_{S30}}$ relationship.

SummaryHydraulic fracturing in unconventional gas development has intensified concerns over induced seismicity, generating significant seismic hazards with potential risk implications for surrounding environments and communities. Accurate prediction and transparent interpretation of such hazards remain open challenges in seismology and engineering practice. This study addresses these challenges by developing a One-Step Prediction Random Forest framework to model the spatiotemporal relationships among hydraulic fracturing well deployment, geological factors, historical seismicity, and the likelihood of future seismic occurrences. A seismic energy labeling scheme based on one-step prediction enables the framework to estimate potential seismic energy release and identify dominant controlling factors through feature attribution. Building on these results, the conventional traffic-light risk management system is conceptually extended to a traffic-light hazard management scheme, which incorporates theoretical insights from risk analysis to improve interpretability and operational relevance. Predictive performance across low-, medium-, and high-hazard scenarios is assessed using confusion matrix analysis, while SHAP-based interpretability confirms that the framework preserves physical consistency by linking geological and operational drivers with seismic energy release. The findings advance methodological innovation in induced seismicity research by combining hazard prediction with a hazard-management framework inspired by risk theory, providing both theoretical insights and practical tools for shale gas development.

SummaryThe 23 April 2025 Mw 6.2 Marmara earthquake near İstanbul in northwestern Türkiye provided a rare opportunity to evaluate the capability of distributed acoustic sensing (DAS) for near-fault seismology in a densely populated and tectonically active region. The DAS monitoring complements the borehole-based Geophysical Observatory at the North Anatolian Fault (GONAF) expanding near-fault observatory towards high-resolution monitoring of deformation along the Main Marmara Fault (MMF) in the vicinity of Istanbul. In this experiment, an existing telecommunication dark fiber within the national optical network was repurposed as a long-offset submarine seismic array without any dedicated underwater installation. This fiber-optic cable, which extends underwater along the southern coast of Istanbul and follows the northern boundary of the Princes Islands, successfully recorded the Mw 6.2 Marmara earthquake. In this study, we analyzed the resulting dataset, which provides an aperture of approximately 34 km with 8 m channel spacing and a 250 Hz sampling rate, and includes sections extending both parallel and perpendicular to the fault. The DAS strain rate recordings of the Mw 6.2 mainshock exhibit clear P- and S- wave arrivals across thousands of channels with high signal coherence. After amplitude and phase calibration, the recordings were converted into equivalent particle velocity using a gauge length based transfer function and compared with nearby GONAF borehole seismic waveform data. Spectral analyses show that the DAS array preserves seismic energy up to ~25 Hz, with spectral shape and amplitude distributions consistent with those of the borehole recordings. Curvelet, f–k, and time domain slowness based conversion methods were employed for the transformation process. The submarine DAS array interrogated by an OptoDAS system enables high resolution spatial sampling of strain derived from the recorded strain-rate data. Systematic variations in apparent velocity (Vp ≈ 2.38 km/s, Vs ≈ 2.30 km/s) and amplitude decay are observed, which could possibly represent shallow sediment heterogeneity along the fiber. Short-wavelength fluctuations in strain are present, which may reflect local structural complexity as well as cable-related factors such as orientation and burial conditions. Overall, these results demonstrate that existing dark-fiber infrastructure can be used as long-aperture seismic arrays for capturing local seismic wavefields providing high-density measurements useful for monitoring near-fault strain variations. This study shows the long-offset DAS recording of a moderate size earthquake in Türkiye and one of the few submarine DAS observations worldwide. It demonstrates that dense strain measurements derived from fiber-optic infrastructure can complement the monitoring of offshore active fault segments, offering unprecedented spatial resolution for monitoring dynamic strain, and fault zone deformation in regions of high seismic hazard such as the Sea of Marmara.

SummaryThe Fuyang Depression, located in the Southern North China Basin, exhibits promising gas generation potential and favorable hydrocarbon accumulation conditions. The hydrocarbon resource survey in this region has primarily involved active-source seismic exploration and drilling operations. Dense-array ambient noise imaging has proven to be an efficient and low-cost detection technique, providing background information on the reservoir accumulation conditions in sedimentary basins. This study utilizes continuous ambient noise records from two linear dense arrays in the Fuyang depression to build S-wave velocity structures and radial anisotropy models down to 3 km beneath the arrays. A modified cross-correlation beamforming method is applied to sub-arrays of the linear array to extract dispersion curves of the fundamental mode and first overtone Rayleigh waves, as well as fundamental mode Love waves, from the ambient noise. The phase-velocity cross sections of different surface wave modes beneath two linear arrays are thereby obtained directly without tomographic inversion. Depth inversion is then performed to derive SV- and SH-wave velocity structures and radial anisotropy for the two linear arrays. The sediment thickness of Quaternary and Neogene (Q+N) sedimentary sequences are delineated by the iso-velocity of S-wave at 1.3 km/s, calibrated with the borehole data. The widespread negative radial anisotropy layers are observed at the shallow subsurface, which are interpreted as the water-saturated open fractures. The S-wave velocity and radial anisotropy models provide valuable constraints for both seismic hazard assessment and hydrocarbon exploration within sedimentary basins.

SummarySolving wave equations using machine learning methods, such as physics-informed neural networks (PINNs) and neural operator approaches, has become an active area of research in the computational seismology community. However, a significant challenge associated with these methods is the degradation of long-term prediction accuracy, which arises from the inherently nonlinear dynamics of wavefields governed by partial differential equations (PDEs). Koopman theory provides a promising framework by enabling the transformation of nonlinear dynamical systems into linear ones, thus allowing linear analysis tools to be applied to complex, nonlinear problems. In this study, we introduce a data-driven operator learning method, the Koopman Neural Operator (KNO), for solving the two-dimensional acoustic wave equation in the time domain. Within the KNO framework, the time-domain wavefield is treated as the state variables, while the velocity model acts as the control variables. These form a nonlinear dynamical system, which is mapped into a linear latent space using a convolutional encoder. The Koopman operator is then approximated by parameterizing the integral kernel in the wavenumber domain, facilitating linear time evolution of the encoded variables. A convolutional decoder subsequently transforms the evolved latent variables back into the original wavefield domain to obtain the predicted time-domain wavefields. To evaluate the performance of KNO, we first conducted numerical experiments on the three most complex datasets from the OpenFWI benchmark and compared the results with those of the current state-of-the-art Fourier Neural Operator (FNO). The results demonstrate that KNO outperforms FNO in terms of prediction accuracy, computational efficiency, memory consumption, and convergence speed. Additionally, KNO exhibits notably strong stability in seismic wavefield extrapolation on the Marmousi model. Finally, we comprehensively evaluate the parallel scalability of the proposed KNO model, and compared KNO with the finite difference method (FDM) (based on both CPU and GPU) in terms of computational speed. Collectively, these experiments indicate that KNO provides a promising new approach for long-term and relatively high-precision wavefield extrapolation in seismic modeling.

SummarySpectral Induced Polarization (SIP) has gained recognition as an advanced geophysical method for monitoring soil water content. SIP’s ability to simultaneously assess soil texture and water content makes it particularly valuable for studying soil dynamics under varying environmental conditions. However, its application in complex field environments has been hindered by issues such as capacitive and inductive coupling, which affect the quality of measurements. In this study, we combined water monitoring in soil and plant (field and lab SIP measurements, sap flow and soil moisture monitoring) to characterize soil heterogeneity and evaluate vine water availability in a Médoc vineyard during the summer drought of 2023. Different SIP field acquisition strategies relying on multiwire cables, fully coaxial cables array or hybrid coaxial/multiwire were tested. The acquisition setup was shown to strongly affect data quality depending on soil moisture conditions. Lab and field SIP measurements confirmed a strong correlation between the quadrature conductivity at 0.25 Hz and soil volumetric water content (VWC) as well as a linear relationship between phase shift at 0.25 Hz and VWC. The real and imaginary parts of the conductivity was used to infer VWC dynamics based on empirical petrophysical relationships established in situ. A mechanistic model based on the Dynamic Stern Layer model was also applied to high-quality SIP data for the same purpose. We found that imaginary conductivity was much less sensitive to soil water conductivity than real conductivity. Thus, in vineyard soils subject to soil amendments and resulting variations in soil water salinity, we hypothesized that SIP monitoring provide more reliable estimates of changes in soil moisture content than standard electrical resistivity tomography. We showed that SIP monitoring effectively captured soil drying dynamics down to a depth of 1 m during the growing season. The SIP method combined with soil moisture probes could thus provide simultaneous access to both soil moisture dynamics and the spatial distribution of soil texture, opening up new perspectives for mapping soil moisture dynamics in the field, even in case of potentially large soil water salinity fluctuations. In our case, SIP indicated a decrease in soil water storage from 150 to 50 mmH20 during the summer drought of 2023. By combining SIP and vine sapflow monitoring, Vine water availability, defined as total transpirable soil water could also be estimated at 98±8 mm H20 for the vines equipped with sapflow sensor, which is of great interest for culture water management. Finally, the distinct responses of the real and imaginary conductivity components underscore the value of SIP for soil moisture assessment in viticultural environments subject to variable salinity inputs. This work is the first to attempt a quantitative estimation of soil water storage in commercial vineyards using SIP methods. It extends previous applications limited to other agricultural settings and broaden the applicability of mechanistic models (Dynamic Stern Layer model) for predicting volumetric water content based on multi-frequency complex conductivity measurements under field conditions.