Geophysical Journal International

SummaryNear-surface geological faults play a crucial role in subsurface systems because they influence fluid flow and transport and can cause geotechnical failure. Topography can sometimes indicate the presence of fault zones, particularly in active tectonic settings where geomorphic features such as triangular facets are preserved. However, particularly for older inactive faults, characteristic surface geomorphic signatures may have been completely removed by erosion, or the fault may be buried beneath younger sediments. Although direct observations from outcrops or borehole data can confirm the presence of a fault very reliably, such data are commonly too sparse and/or distant to precisely constrain fault location in the target area. In this context, near-surface geophysical methods, such as electrical resistivity tomography (ERT) and seismic refraction tomography (SRT), may be useful to locate faults. However, these geophysical data are typically inverted using smoothness-constrained approaches, which can obscure sharp structural features unless the latter are explicitly accounted for in the models. In this study, we present a workflow that enhances the identification of fault zones by jointly inverting collocated ERT and SRT data, considering a parameterization approach based on a layered subsurface model explicitly including a fault. Unlike conventional approaches, we run multiple inversions in which the starting models are initialized with randomly generated fault geometries, guiding the search toward different structural scenarios that yield comparable data misfits. In the absence of additional prior information, these solutions should be interpreted as equally plausible representations of the subsurface. The resulting ensemble of models provides a practical indication of where the structure is well constrained (i.e. features that appear consistently across solutions) and where ambiguity remains (i.e. features that vary among solutions). We showcase the applicability of our workflow with synthetic and field data, where collocated ERT and SRT measurements were collected across expected fault zones. Our results show that the joint inversion of ERT and SRT data consistently narrows the range of plausible fault locations, particularly in the shallow subsurface. Iterative incorporation of prior information (e.g., one vs. two boreholes) leads to significant improvements in model geometry and physical property estimates. However, consistent with the intrinsic ambiguity of geophysical inversion, the results remain non-unique; even models depicting opposite fault types (normal vs. reverse) may fit the data comparably well. This outcome highlights the strength of the workflow in identifying competing structural scenarios that are equally consistent with the observations. Thus, the resulting ensemble provides essential guidance for targeted ground-truthing and follow-up investigations aimed at resolving the remaining ambiguity.

SummaryTo address the challenge of assessing the reliability of three-dimensional (3D) magnetotelluric (MT) inversion results, we have developed a variational inference (VI) inversion framework (VI-MT) based on the Stein Variational Gradient Descent (SVGD) method. Through parallel particle optimization, we efficiently approximate the model posterior distribution, overcoming the computational limitation of traditional Markov Chain Monte Carlo (MCMC) methods. Synthetic tests show that the VI-MT inversion can effectively recover the synthetic model while reducing the tailing effect, and it can approximate the multimodal posterior distribution, providing quantitative uncertainty estimates. Furthermore, the VI-MT inversion is applied to the field MT data collected at the Weishan volcano in northeast China, with the posterior mean model consistent with the deterministic inversion model. A clear low resistivity body of ∼5 Ω·m with small uncertainties is imaged at depths of ∼2-6 km beneath the volcanic crater, suggesting the existence of a shallow magma chamber. Our study shows that the VI-MT inversion based on the SVGD method can efficiently solve the 3D MT Bayesian inversion, providing reliable model uncertainties.

SummaryCharacterizing electronically conductive minerals with spectral induced polarization (SIP) has been a longstanding goal of mineral exploration. A more comprehensive understanding of how mineral texture (e.g. disseminated grain versus veinlet forms) and mineral type influence SIP signals is required, as the polarization mechanisms at the electron conductor-fluid interface and their effect on SIP signatures remain incompletely understood. We performed laboratory measurements on synthetic copper (Cu) and graphite (Gr) samples prepared in disseminated grain and veinlet forms by mixing fixed concentrations of the electron conductor with a non-polarizable sand. Current density dependent SIP (complex impedance) responses were assessed by measuring at multiple current densities, and non-equilibrium was investigated by making measurements as a function of time. SIP responses that remained independent of current density were interpreted as evidence of non-Faradaic processes, with possible minor Faradaic contributions, being dominant. Conversely, current density dependent complex impedance responses were interpreted as an increased contribution from Faradaic polarization processes involving redox reactions at the electrolyte-electron conductor interface. Veinlet Cu samples exhibited strong current density dependent SIP responses and prolonged equilibration, in contrast to the negligible current density dependence of veinlet Gr and disseminated grain Cu and Gr samples. All samples that exhibited negligible current density dependence could be fit to a Pelton-Cole model, whereas samples exhibiting strong current density dependence could only be fit with a more flexible Debye decomposition model. Gr samples displayed a fluid-resistivity dependent peak in the phase response at a specific frequency consistent with mechanistic models, while Cu samples deviated from predicted behaviour. Measurements on rock cores were consistent with observations on synthetic samples, with the SIP response of a Cu bearing rock core exhibiting a clear dependence on current density, whereas the Gr bearing rock core showed negligible current dependence. The observed dependence of the phase and complex resistivity on current density, mineral type, and texture is consistent with differences in the relative importance of Faradaic and non-Faradaic polarization processes. These results suggest that distinguishing current-density-dependent Faradaic responses from current-independent non-Faradaic responses may improve the discrimination of mineralogical and textural characteristics using SIP measurements, although field-scale applications will be limited due to the lower current densities relative to those used in this laboratory study.

SummaryFull Waveform Inversion (FWI) leverages the amplitude and phase of full wavefield seismic data to obtain high-resolution subsurface structures by minimizing an objective function. However, FWI encounters severe cycle-skipping issues when the seismic data lack low-frequency components or the initial velocity model is significantly different from the true velocity model. Furthermore, an incorrect source wavelet can also directly affect the accuracy of inversion results. To overcome these challenges, we introduce the waveform-preserving source-independent wave-equation-based local-scale traveltime inversion (WPS-LTI) method. This approach aims to recover the low-wavenumber velocity structures as an initial velocity model, while reducing the influence of an incorrect source wavelet. In the WPS-LTI method, seismic data are processed via both convolution and deconvolution with reference traces, resulting in modified observed and synthetic seismic data that share the same seismic wavelet. This approach helps avoid waveform distortions typically introduced by conventional source-independent methods that rely solely on convolution. By preserving the waveform characteristics, we can use cross-correlation to compute more accurate local-scale traveltime differences, thus recovering accurate low-wavenumber velocity structures. Tests conducted on the Marmousi model and the Chevron 2014 blind test dataset demonstrate that the WPS-LTI method can significantly alleviate cycle-skipping issues and reduce the dependence on the source wavelet in FWI.

SummaryWith the shale gas exploration and development into deep strata (> 3500 m in depth), weak signal detection becomes more challenging and vital in surface-based microseismic monitoring. Most existing deep learning approaches are trained on synthetic or single-station data, which limits their performance in detecting weak events in field seismic recordings. Here, we propose a deep learning approach based on the computer vision object detection model YOLOv5 via transfer learning and train it on a dataset from field observation to better detect microseismic events in multi-channel seismic data. First, we convert continuous seismic data into waveform images in grayscale with a record length of 20 s, and manually annotate the microseismic events containing the first arrival signals using rectangular boxes. To train the model, the transfer learning strategy is used by using parameters of YOLOs in computer vision to initiate the model. After training and validation, we conducted comprehensive tests to examine the performance of the model. Compared with the single-station short-term average/long-term average (STA/LTA) detector, our method reduces false positives markedly, yet it also delivers higher recall rate than the available multi-station detection scheme. When applied to continuous dataset from a different area, the model achieved good performance with a precision of 0.9832 and a recall of 0.9702, indicating it learns representative features of microseismic signals. All these tests illustrate that the proposed method achieves high detection accuracy, low false positive rates, and robustness. Meanwhile, the method possesses the superiority for real-time monitoring and can be easily updated by adding a few new samples to the training dataset.

SummaryDeep learning methods such as neural networks offer a rapid alternative to geophysical inversion and are increasingly applied to airborne electromagnetic (AEM) data for a range of earth science problems. Such networks rely on both the network architecture as well as suitable training datasets to produce realistic earth resistivity models. Here we develop a Convolutional Neural Network (CNN) to invert AEM data and pay particular attention to the characteristics of the training dataset. As well as covering a realistic range of subsurface property distributions, the training data must emulate the noise and data gap characteristics of the target survey data. We find neural networks trained on noise free data, while performing very well on validation data, perform poorly on real world data, often inducing geologically unrealistic artifacts in the resistivity model. Conversely, training data conditioned with noise and data gap characteristics appropriate for the survey of interest, fit the validation data less well, but produce fewer artifacts when applied to survey data. Choice of loss function metric and data normalisation method is important and may vary between surveys. Despite strong advances in neural network design, developing training data with broad enough characteristics to be generalised across many surveys and data acquisition platforms remains an important challenge.

SummaryAccurate separation of the Earth’s internal and external geomagnetic fields requires robust modeling, especially during the geomagnetic storms. Because the comprehensive CHAOS model is primarily designed to represent the Earth’s magnetic field under geomagnetically quiet periods, residuals of satellite observations relative to the CHAOS model during storms exhibit systematic dependencies on magnetic local time (MLT) and the disturbance levels, highlighting limitations in its representation of external field morphology. To address this limitation, we developed an empirical correction for the CHAOS model that specifically targets the magnetic signatures of the residual ring current at low and mid-latitudes. Using the high-quality scalar magnetic data from Swarm A/B and the Macau Science Satellite-1 (MSS-1) at nighttime hours, between November 2023 and September 2025, we modeled the systematic perturbations remaining after CHAOS background removal. Our separable parameterization explicitly resolves MLT structure while incorporating dependencies on geomagnetic disturbance levels. Event analyses demonstrate that our correction model can successfully capture the pronounced dawn-dusk MLT asymmetries of the external field during storms main and recovery phases. Statistically, the empirical model robustly captures residual ring current signals across all evaluated nighttime sectors, yielding the greatest improvements at dusk. Ultimately, the proposed correction effectively mitigates the activity-dependent biases in the CHAOS model’s external field, yielding a more reliable representation for the accurate interpretation of storm-time magnetic residuals.

SummaryGeophysical joint inversion offers a robust approach for integrating diverse geophysical datasets, leveraging their complementary information to mitigate the nonuniqueness inherent in individual data inversion. This approach thereby enhances model consistency and improves subsurface characterization. In this study, we investigate the effectiveness of three-dimensional (3D) joint inversion of gravity, magnetic and magnetotelluric (MT) data for deep geothermal exploration. We first implemented a reweighting optimization strategy for gravity and magnetic data to increase the vertical resolution of jointly inverted models of potential data. Subsequently, we integrated the optimized gravity and magnetic inversion scheme with MT data using a cross-gradient constraint for comprehensive joint inversion. Synthetic experiments validated the effectiveness of our developed reweighting optimization strategy and joint inversion scheme, demonstrating improved structural resolution and model reliability compared to separate inversions. Finally, we applied the methodology to the Yanggao geothermal field in northern Datong Basin, China, to assess its applicability for deep geothermal exploration. The joint inversion approach, offering an advantage over separate inversions, provides enhanced subsurface characterization by more accurately delineating hydrothermal pathways and potential geothermal reservoirs of the geothermal system within the investigated area.

SummaryLocal seismic topographic amplification of rock slopes is a primary cause of earthquake-induced damage in mountainous engineering. To resolve the scattering of plane SH waves by individual rock slopes, this study introduces an analytical model derived from the Uniform Theory of Diffraction. Conventional wave function expansion methods (WFEM) often struggle with non-closed free boundary conditions across varying elevations and tend to suffer from series divergence under high-frequency incidence. This approach precisely partitions the slope into two semi-infinite ideal wedge domains sharing a common face, reconstructing the generalized geometric wave field through rigorous global ray tracing. Second-order coupled diffraction terms with a slope-diffraction correction are introduced to describe the two-way interaction between the slope crest and toe, while the UTD transition functions ensure continuity of the wavefield across shadow boundaries. Systematic numerical evaluations in both frequency and time domains reveal a frequency-selective interference mechanism with distinct spatial variability. Under horizontal grazing incidence, energy is strongly focused on the leeward face, while the back-slope forms a high-frequency seismic shadow zone due to intense destructive interference between direct and secondary diffracted waves. At certain oblique incidence angles, repeated scattering of broadband waves between the slope crest and toe leads to strong local resonance in the slope region. Findings indicate that the location of maximum dynamic response shifts sensitively with the incident wavefront inclination. The results show that the maximum amplification does not always occur at the slope crest, highlighting the importance of incidence angle in seismic microzonation and site planning for mountainous areas.

SummaryThe statistics of earthquake populations are governed by stress changes and event interactions at various scales which promote non-linear event cascades and seismicity clustering. Earthquake clustering in space and time is observed in both tectonic and volcanically dominated regions; however, notable differences exist for instance during eruptive sequences. Here, we characterize seismicity statistics in California and Hawaii and quantify differences in background rates and clustering. We compare three statistical approaches, i.e, i) Reasenberg declustering, ii) Gamma distribution fit, and iii) Nearest Neighbor Clustering which are applied to seismicity in Hawaii, southern California, and ETAS catalogs with known clustering characteristics. Tests with ETAS-catalogs suggest that background rate variations are more consistently resolvable using the Nearest Neighbor and Gamma distribution algorithm without requiring parameter adjustments to the specific study area. The Nearest Neighbor method tends to overestimate background rates, whereas the Gamma distribution algorithm shows systematic underestimation, suggesting that an ensemble model can improve the performance over any single method. Uncertainties in background rate estimates based on small magnitude events (e.g., M≥2.5) are high, in particular for the Reasenberg method, which overpredicts true rates by up to a factor of 4, and extrapolations to larger magnitudes need to be treated cautiously. Our analysis shows that the combination of the three approaches generates new insights into the processes that govern seismicity clustering. The analysis of southern California seismicity produces consistent results between the different methods, and the distribution of background seismicity rates is indistinguishable from Poissonian rates at magnitudes above 3.5. Conversely, the seismicity in Hawaii exhibits notable non-Poissonian background rate changes. Results from the Nearest Neighbor clustering analysis suggest notable differences between tectonic and volcano seismicity, including background rate changes and more pronounced spatiotemporal clustering around active volcanoes. Hawaiian seismicity is characterized by more localized spatial-temporal clustering, in particular during the 2018 eruption. This difference may be a result of magmatic, hydrothermal and eruptive processes, which make volcanic areas susceptible to stress perturbations and earthquake triggering at small scales.

SummaryMapping the internal structure of sedimentary basins, including the depth to basement, is valuable for a variety of geological applications, particularly the identification of natural resources such as groundwater and minerals, or of the geological structures associated with them. The magnetotelluric (MT) method has proven to be a reliable technique for imaging complete sedimentary sequences, especially in areas where thick sedimentary cover makes imaging challenging. However, due to the high regularisation required by MT inversion procedures, it is limited in its ability to precisely locate geological interfaces, which is crucial for exploration. Building on previous work where we imaged the basement using interface probabilities derived from 1D probabilistic inversion of MT data, we present a new method which allows for the simultaneous classification of multiple interfaces across an entire survey, incorporating constraints on the spatial relationships between models. As a result, we obtain spatially consistent model ensembles and classified interfaces corresponding to transitions between layers of consistent electrical resistivity. This classification effectively reduces the size of the model ensemble, decreasing uncertainty in the estimated depth of the interfaces of interest. We apply this method to the Eucla sedimentary basin in Western Australia, analysing 550 MT sites distributed across 12 profiles. The ensemble clustering clearly identifies three interfaces which are consistent with the sedimentary succession expected from this basin. The results show great consistency across all the survey, providing valuable insights into the geological setting of the area. This research demonstrates the capability to reliably image the structure of a sedimentary basin using MT, within a workflow that integrates probabilistic inversion and model ensemble classification.

SummaryThe Tournemire Underground Research Laboratory (URL) operated by ASNR (Autorité de Sûreté Nucléaire et de Radioprotection) is a French test site specifically developed to study and comprehend the confinement properties of low-porosity claystone formations. More specifically, this site allows developing an improved knowledge of the transport properties of the Toarcian (Early Jurassic) shale formation. This claystone shares indeed similar characteristics to the Callovo-Oxfordian claystone formation encountered at the Meuse/Haute-Marne underground research laboratory operated by ANDRA (Agence Nationale pour la gestion des Déchets RAdioactifs) to study the feasibility of the long-tern storage of nuclear wastes. In the present study, 18 rock samples of the Toarcian formation are used to study (i) the induced polarization properties of this formation at full water saturation and (ii) the desiccation of this claystone by looking at the evolution of the complex conductivity spectra with the decrease of the water saturation in the frequency range 1 mHz-45 kHz. In this second set of experiments, the complex conductivity spectra are fitted with a double Cole Cole complex conductivity model. We then obtain the values of the first and second Archie’s exponents and estimates of both surface conductivity and normalized chargeability. We also test relationships between the surface conductivity, the normalized chargeability, the quadrature conductivity, and both the cation exchange capacity and water content of these clay-rocks. Furthermore, the laboratory data are favorably explained by the so-called Dynamic Stern Layer (DSL) model developed in the last decade. In order to test this new knowledge in field conditions, an in situ experiment is performed in a gallery of the Tournemire URL to test the ability of the method to image the damage zone around a 80-cm hole drilled normal to the gallery axis. For this purpose, a set of 5 wells equipped with a total of 240 non-polarizing electrodes is successfully used to get both electrical conductivity and normalized chargeability tomograms, which are in turn used to image the water content around the main hole. The results are analysed in terms of stress distribution including the effect of plasticity.

SummaryAcoustic wave propagation under pressure is inherently nonlinear and often exhibits hysteresis in elastic properties. Understanding this behaviour is important in rock physics and exploration seismology because pressure-dependent elastic responses contain information on evolving rock microstructure. We present a petrophysical model that describes the relationship between acoustic P- and S-wave velocities and effective rock pressure during both loading and unloading phases. Instead of prescribing a single microscopic mechanism, the model introduces an effective extensive state variable that represents the cumulative effect of pressure-induced microstructural evolution. Depending on the dominant physical process, this variable may be associated with compliant pore and microcrack volume, grain-contact evolution, or other pressure-sensitive structural descriptors. The model distinguishes between open and closed microstructural states and describes their stress-dependent evolution through different sensitivities during loading and unloading, thereby reproducing irreversible velocity-pressure behaviour within a unified framework. Model parameters are estimated from laboratory-measured acoustic velocities using linearized joint inversion. The inversion further enables the reconstruction of an acoustically weighted effective descriptor that characterises the pressure-dependent contribution of compliant microstructural evolution to the observed velocity response. In parallel, we apply the Preisach theory of hysteresis to pressure-dependent acoustic velocities. In this framework, compliant microstructural features are represented by effective hysteretic units (hysteron) characterized by distributed closing and opening pressures. The resulting Preisach model reproduces the measured hysteresis with high accuracy and enables a model-dependent reconstruction of the Preisach density function, which provides an effective, non-unique representation of hysteretic switching contributions across closing and opening pressures. By combining the petrophysical and Preisach approaches, we introduce two diagnostic quantities, the Ratio of Hysteron Jumps (RHJ) and the Cumulative Hysteron Jumps (CHJ), which quantify the relative and cumulative contributions of hysteretic elements throughout loading and unloading. While both approaches reproduce the observed velocity–pressure hysteresis, they provide complementary information. The petrophysical model describes the macroscopic evolution of elastic properties via an effective state variable and its acoustically weighted representation, whereas the Preisach model resolves the distribution and activation of hysteretic switching contributions in Preisach space via the reconstructed density function. The results demonstrate that acoustic hysteresis measurements contain not only information on effective elastic properties but also recoverable signatures of pressure-dependent microstructural evolution relevant to seismic interpretation.

SummaryThe São Francisco Paleocontinental block (SFPB) and the Congo Paleocontinental block (CPB) composed, prior to the Atlantic opening in the Cretaceous, the São Francisco-Congo Paleocontinent (SFCP). The SFCP was one of the landmasses involved in the Neoproterozoic Brasiliano-Pan African Orogeny and the assembly of Western Gondwana. Both the SFPB and CPB underwent reworking during the amalgamation of Western Gondwana, forming the orogenic belts that surround the preserved portions of the SFPB and CPB that are known today as the São Francisco and Congo cratons. Although the crustal portions in the outermost areas of these paleocontinents are no longer visible in the surface due to this reworking, the limits of these paleocontinents remain relatively intact at subcrustal depths. The objectives of this study are to delimit the SFPB and to image the structures beneath the Araçuaí belt at mantle lithosphere depths. We present the results of our study which were obtained by the multiple-frequency seismic tomography method for the SFPB and adjacent structures between depths of 68 and 587 km. Data were obtained by processing broadband seismograms recorded between 11-August-2023 and 31-July-2024 for P and PKIKP phases. The processed database comprised 9 254 residuals, which were added to the database of a previous regional study resulting in 97 150 traveltime delays. Checkerboard resolution tests using patterns with horizontal dimensions of 390×390 km reveal very good recovery between depths of 68 and 226 km, although the sharp vertical transition between the checkerboard layers is not fully recovered between the depths of 316 and 384 km. The checkerboard pattern with horizontal dimensions of 312×312 km reveals that the best recovery is observed in the southern and eastern portions of the São Francisco craton (SFCr) and in the Borborema province (BP) between depths of 68 and 226 km. Our results show a high-velocity anomaly with roughly NE-SW trend that extends from the Paranapanema block (PaBl), through the entire extension of the SFCr up to the southern BP. This anomaly contains two major segmentations, one in the southern Brasilia belt, marking the division of the PaBl with the SFPB, and the other dividing the eastern and western arms of the SFPB with a roughly NW-SE trend, interpreted as the Paramirim aulacogen. We also observe a high-velocity anomaly beneath the Araçuaí belt (ArBe) that dips with a SE direction. This anomaly starts at the northern end of the Abre Campo suture at a depth of 136 km, deepens to the south, and is interpreted as the foundered cratonic lithosphere that originated from the delamination of the SFCr due to the passage of the Trindade plume. To reinforce our interpretation, we tested several synthetic models with geometries that were based on our results and previous studies in the area. The synthetic model which presents the most resemblance to the real data model contains a high-velocity anomaly beneath the Araçuaí belt between depths of 226 and 384 km. A strong low-velocity anomaly beneath the Ribeira Belt is interpreted as material rising from the Nazca Slab dehydration.

SummaryNon-Newtonian pore-fluid behaviour is incorporated into a generalized Lagrangian–dissipation framework for wave propagation in fluid-saturated porous media. In contrast to approaches that rely on prescribed pore-scale flow patterns, pore geometries, or other microscopic structural assumptions, the present formulation introduces nonlinear rheological effects directly at the macroscopic constitutive level. The resulting model remains consistent with continuum mechanics and Biot-type poroelasticity while allowing non-Newtonian effects to enter three fluid-related dissipation mechanisms: Darcy-type fluid–solid friction, local-deformation (LD) relaxation, and bulk-viscous dissipation. Nonlinear dissipation potentials are constructed for these mechanisms and then reduced, through a first-harmonic approximation, to effective frequency-dependent coefficients for harmonic wave analysis. Numerical examples show that the non-Newtonian Darcy-friction and LD mechanisms primarily modify the characteristic frequencies of the corresponding relaxation processes, whereas the non-Newtonian bulk-viscosity mechanism affects both the frequency range and the strength of attenuation and dispersion, with a strong sensitivity to the assumed magnitude of the pore-fluid bulk viscosity. The framework also allows alternative nonlinear representations of Darcy-type fluid–solid friction to be compared within the same Lagrangian–dissipation structure; an alternative nonlinear dissipation operator produces wave-propagation predictions nearly indistinguishable from those obtained using the power-law Darcy-friction model, suggesting that different nonlinear constitutive assumptions may lead to similar effective attenuation structures at the macroscopic scale. Calibration against laboratory measurements on tight carbonate rocks shows that the observed attenuation and velocity-dispersion behaviour can be reproduced using physically interpretable non-Newtonian parameters. These results suggest that non-Newtonian pore-fluid rheology may contribute to frequency-dependent wave-propagation effects in fluid-saturated porous media and provide a flexible macroscopic framework for investigating fluid-related dissipation beyond conventional Newtonian poroelastic models.

SummaryGravity data is widely applied to determine Moho topography due to globally dense and homogeneous gravity observations. Compared with gravity data, gravity gradients can reveal more details of Moho structure, which are more competitive. However, gravity gradients must be transformed into gravity data for common Vening Meinesz-Moritz (VMM) methods, which causes loss of some high-frequency signals. Thus, an improved method by constructing direct function relation between Moho topography and gravity gradients is proposed in this paper. Subsequently, A case study in Tibetan Plateau is conducted using this improved method, and a new Moho topography with higher spatial resolution is determined. More refined tectonic implications can be also revealed from this new Moho topography: (1) the prevailing wavelengths of detected Moho folds are approximately 578 km in the east-west direction and approximately 515 km - 708 km in the north-south direction; (2) two possible lower crustal mass flow channels can be identified clearly according to the traces leaving on Moho topography in the southeastern Tibetan Plateau (3) clearer Moho subsidence, approximately 5 km, can be observed in the central Tarim Basin.

SummaryThe northeastern margin of the Tibetan Plateau exhibits pronounced spatial variations in present-day vertical crustal deformation, yet the mechanisms controlling these differences remain debated. In particular, contrasting uplift patterns are observed between the Liupanshan tectonic belt and the Maxianshan Fault, two key tectonic structures along the northeastern expansion front of the plateau. Here we develop two three-dimensional viscoelastic finite element models constrained by geological structures, seismic profiles, and GNSS observations to investigate the controls on vertical deformation. The modeling results show that when the mid–lower crustal viscosity is laterally uniform between the Longxi Basin and the Ordos Block, vertical deformation of ~2 mm/a is concentrated within a ~50 km-wide zone near the Liupanshan tectonic belt, consistent with leveling observations. This deformation pattern mainly reflects crustal shortening and thickening dominated by pure shear. In contrast, the observed ~4 mm/a uplift along the hanging wall of the Maxianshan Fault can only be reproduced when weak mid–lower crust and enhanced crustal flow are introduced beneath the Linxia Basin, indicating that northeastward mid–lower crustal flow is converted into vertical uplift along the fault. These results suggest that the Maxianshan Fault represents the northeastern termination of mid–lower crustal flow within the Qilian Block, whereas deformation in the Liupanshan tectonic belt is primarily accommodated by crustal shortening. Our findings highlight the fundamental role of lateral rheological heterogeneity in controlling vertical deformation and provide new insights into the mechanisms of present-day lateral growth of the northeastern Tibetan Plateau.

SummaryAmbient noise based monitoring of subsurface velocity changes is possible without the explicit retrieval of Green’s functions by correlation. Velocity variations can directly be observed from the fluctuations in the spectrograms of ambient noise time series or their cross-spectra. This approach is more resource efficient than the conventional Green’s function based monitoring and ideally suited for edge processing and the analysis of large volumes of data for example from fibre-optic records. The spectral fluctuations result from wave propagation in the heterogeneous subsurface and interference between different scattering paths. The fluctuations are directly related to the occurrence of coda waves in the Green’s function. In contrast to coda wave interferometry in the time domain, monitoring the evolution of spectral fluctuations allows for measurements of velocity changes that are unbiased by changes in the source spectrum. Recognizing that the imprint of the subsurface heterogeneity is directly encoded in the spectral fluctuations opens a new perspective on the investigation of subsurface scattering and attenuation.

SummaryWe develop a discrete event modeling framework that captures the progression of geophysical systems toward catastrophic failure through sequences of distinct damage events. By representing geophysical system evolution as a succession of temporally accelerating and amplitude-varying events, the framework reveals how finite-time singularities, both logarithmic and power law types, naturally emerge from the interplay between shrinking interevent intervals and growing event magnitudes. This event-based perspective, which can be viewed as a discrete representation of progressive damage accumulation in heterogeneous geomaterials, provides an intuitive physical understanding of rupture processes, highlighting how precursory signals such as accelerating strain rate, event frequency, and energy release can be traced back to simple underlying mechanisms. A mean-field damage-based formulation further links the observed power law exponents to the evolving stiffness of the geophysical system under constant or time-varying stress. Incorporating stochastic fluctuations, the model captures the inherent randomness of natural systems leading to the emergence of stochastic finite-time singular behavior. Together, these results establish a simple yet powerful framework for interpreting the dynamics of catastrophic events, providing a common event-based perspective on observations from landslides, glacier breakoffs, volcanic eruptions, and other related processes, and strengthening the physical foundations of early warning and hazard forecasting.

SummaryThe Accurately Controlled Routinely Operated Signal System (ACROSS) is an artificial seismic source that generates highly repeatable and stable seismic waves for high-resolution temporal monitoring. While direct P- and S-waves from ACROSS have been widely used to monitor earthquakes, volcanic activity, and environmental changes, the scattered waves that arrive later, known as coda waves, have received limited attention. In this study, we used coda waves generated by ACROSS to monitor subsurface seismic velocity changes (dv/v) over 10 months. The ACROSS signals were recorded by a seismic array of 14 seismometers deployed approximately 3 km from the source in Morimachi, central Japan. By deconvolving the records with a known source function, we obtained transfer functions corresponding to band-limited Green’s functions. Coda wave interferometry applied to the coda portions of these transfer functions detected temporal variations in dv/v, exhibiting both seasonal long-term variations and rainfall-induced short-term variations. Comparative analyses using direct P- and S-wave travel-time changes from the same ACROSS data and ambient-noise interferometry show that, under the present observational conditions, these conventional methods primarily detect long-term variations; however, they do not resolve the rapid, transient short-term variations. In contrast, the stable ACROSS source combined with the broader sampling of coda waves provides superior sensitivity to environmental changes across both timescales over kilometer-scale distances, highlighting its effectiveness for high-temporal-resolution monitoring. To investigate the mechanisms responsible for the observed dual-timescale variations, we used a poroelastic model with precipitation input and considered thermoelastic effects and groundwater-induced changes as possible contributors to the long-term variations.