Geophysical Journal International

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.

SummaryDespite generally low seismicity typical of intraplate regions, magnitudes larger than 6 have occurred in the Amazon craton. The installation of permanent stations of the Brazilian Seismic Network in the Amazon around 2014, the Vale 5-station network in the Carajás mineral province in 2019, and a 30-station temporary deployment (October2021-March/2024) significantly improved earthquake detectability in the eastern part of the Amazon craton. A review of the seismotectonic characteristics of the oldest, Eastern part of the Amazon craton is presented here. Seismicity is not uniform, and areas of higher seismicity are identified, such as in the northern part of the Tapajós-Parima province and along the eastern border of the craton. No clear correlation with the main trends of faults was observed. Contrary to Central and Eastern Brazil, seismicity is not directly correlated with lithospheric thin spots in the Amazon Craton but tends to occur in the flanks of thick keels (“craton edge” effect). A possible influence of free-air gravity anomalies was noticed suggesting that flexural stresses contribute to control seismicity. Five new focal mechanisms are presented for the eastern edge of the Amazon craton, indicating a stress field with NW-SE compression and NE-SW extension. An updated map of the stress field for mid-plate South America shows that the maximum horizontal stresses vary from E-W in SE Brazil, NW-SE in central Brazil and SW-NE in the north. This pattern can potentially be explained by upper mantle flow, provided more detailed convection models are used.

SummaryElectrokinetic signals generated by coupled stress–fluid processes are increasingly recognized as indicators of fault-zone dynamics prior to earthquakes. However, their interpretation is often limited by the common reliance on point-source approximations, which neglect the inherently distributed nature of stress accumulation and fluid migration along fault planes. Here, we develop a quasi-static finite-fault electrokinetic framework in which coupled stress and fluid perturbations are represented as spatially distributed, time-evolving sources. The approach combines an extended Luco–Apsel–Chen generalized reflection and transmission method with a point-source superposition scheme, enabling efficient simulation of electrokinetic responses to area sources in layered porous media. Numerical results reveal that the horizontal components of geoelectric fields in the coupled stress–fluid system are highly sensitive to fluid-source geometry, whereas vertical components primarily reflect stress loading. Spatial variability in initiation time, arising from finite-rate fluid migration, further introduces waveform complexity, amplitude modulation, and multi-stage temporal evolution in surface signals. Notably, we find that the directional variations of the geoelectric field provide a robust diagnostic for distinguishing fluid-driven from stress-induced signals, with angular misalignments reaching up to 16.6°. These results establish a quantitative framework for interpreting near-fault electrokinetic signals and for guiding monitoring strategies aimed at constraining fault-zone fluid pathways and stress evolution.

SummarySix-component (6C) seismic observations offer a more comprehensive description of the wavefield than conventional three-component methods. However, current wavefield separation techniques are often constrained by their reliance on dense arrays. This study introduces a novel wavefield separation framework based on single-station 6C polarization analysis, which enables the simultaneous identification and improved separation of major seismic wave types: P-, SV-, Rayleigh, and transversely polarized horizontal waves (SH- and Love waves). Our proposed method models the observed wavefield as a weighted linear combination of theoretical wave models and optimizes the weighting coefficients via the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm, achieving reliable separation. A refined polarization-based filtering strategy incorporating likelihood estimation, degree of polarization, and energy distribution ratios enhances robustness under low signal-to-noise conditions. Furthermore, the inverse Short-Time Fourier Transform (iSTFT) is adopted to mitigate energy leakage issues associated with the inverse S-transform. Validation using synthetic and real teleseismic data suggests the method’s effectiveness and provides insight into its physical limitations. This study demonstrates a polarization-based framework for seismic phase identification and wavefield separation, which may support multi-phase joint inversion in selected seismic applications.

SummaryInjection-induced seismicity poses a major challenge to the safety of Enhanced Geothermal Systems (EGS). We customized a deep learning model to forecast seismicity rate under prescribed injection schedules. The model adopts a two-stage strategy where injection pressure is first forecasted as an intermediate variable and subsequently used to support seismicity rate forecasting. In this way, seismicity rates at both the field-scale Utah Frontier Observatory for Research in Geothermal Energy (Utah FORGE) and the mine-scale EGS Collab projects could be successfully forecasted. While the model without seismogenic index (Σ) could attain low forecast errors, incorporating Σ markedly improves its ability to capture the transient variability of seismicity rate. The forecasts at Utah FORGE and EGS Collab may highlight the importance of integrating key physical parameters calculated from raw observations into data-driven frameworks for forecasting injection-induced seismicity, and may demonstrate the potential of customized deep learning models for cross-stage forecasting in next-generation EGS.

SummaryReconstructing subsurface structures with high resolution is one of the main goals and potentials of full waveform inversion (FWI). However, FWI is a highly nonlinear and ill-posed problem. Conventional physics-based FWI methods, which rely on gradient-based optimization to minimize the difference between observed and synthetic data face cycle-skipping challenge. Although numerous deep-learning inversion approaches have shown promise, they typically focus on latent representations of time-domain seismic data. This often causes an unstable inversion process due to waveform mismatches. To overcome these limitations, we introduce FFT-InversionGAN, an unsupervised seismic inversion framework that integrates physics-based forward modeling with adversarial learning of the frequency-domain data based on Wasserstein generative adversarial network with gradient penalty (WGAN-GP). Fast Fourier transformer (FFT) is employed to transfer the time and phase information of time-domain seismic data into the spectrum and amplitude distributions to modify the feature space and sensitivity of the adversarial loss to different types of mismatches. By leveraging Wasserstein distance constraints, this method can naturally operate on the spectral distributions of seismic data. Compared with L2 norm, Wasserstein distance is far less sensitive to the linear variations in the phase spectrum. And our proposed method eliminates the need for network pre-training while improving stability and flexibility. FFT-InversionGAN demonstrates enhanced accuracy and resilience in numerical experiments on noise-free, noisy and missing low-frequency benchmarks. This was observed when applied to the Marmousi and overthrust models, where it consistently outperformed conventional FWI and FWIGAN. These findings highlight that FFT-InversionGAN has superior inversion effectiveness.

SummaryThe 2020 MS 5.0 Qiaojia earthquake occurred in a tectonically complex region near the Xiaojiang fault in southwestern China. We investigated the rupture process of this moderate-sized earthquake using a multi-empirical Green’s function (EGF) inversion method that integrated waveforms from multiple EGF events. Synthetic tests demonstrated that the multi-EGF inversion method recovered the input model more robustly than any individual EGF inversion. The resolved spatiotemporal rupture model of this earthquake indicated a compact rupture lasting approximately 2.9 s, dominated by a major asperity near the hypocenter and characterized by predominantly eastward rupture propagation. Bootstrap resampling analyses further confirmed the robustness of the resolved major coseismic slip distribution and the overall moment release pattern. We also observed a spatial complementarity between the coseismic slip and aftershock distributions, with most aftershocks clustering around the periphery of the major asperity. This study not only elucidates the source complexity of the 2020 MS 5.0 Qiaojia earthquake, but also validates the robustness and effectiveness of the multi-EGF inversion method in resolving the rupture processes of moderate-sized earthquakes. Our results provide new insights into the rupture kinematics of moderate-sized earthquakes and the heterogeneity of fault strength and stress within the Xiaojiang fault zone and its surrounding regions.

SummaryThe Wasatch Front Community Velocity Model (WFCVM) is the most complete and detailed Earth model for the Wasatch Front region in north-central Utah (USA). Until recently, it had not been well evaluated with strong ground motion observations due to a lack of local earthquakes with magnitude M5+. The 18 March 2020 Mw 5.7 Magna, Utah, earthquake generated excellent strong ground motion data at dozens of stations along the Wasatch Front, with peak ground accelerations up to 0.54 g. Here, we use the forward finite-difference code SW4 to simulate waveforms of the 2020 Magna mainshock in the WFCVM up to 3 Hz and compare its predictions to observations from 35 nearby stations at epicentral distances of 4–46 km. We use a finite fault source model with a semi-stochastic slip distribution and overlay stochastic velocity perturbations (S) and surface topography (T) on the WFCVM, which we refer to as the 3D+S+T model. Observed-predicted amplitude ratios and Goodness-of-Fit (GOF) scores for PGA, PGV, PGD, Arias intensity and duration, cumulative energy and duration are calculated. Our 3D+S+T model performed fairly, matching the general character of the observations with an average GOF score of 5.20 (out of a maximum of 10), slightly better than the unaltered WFCVM score (GOF=4.97). Stochastic velocity perturbations mostly affect peak ground motions at the closest sites (< 20 km), while surface topography improves durations for basin sites and generates more realistic signals at higher frequencies. Neither addition resolves underprediction of basin amplification in the eastern Salt Lake Basin and overprediction of ground motion at basin-edge sites, which likely reflect inaccurate representations of basin structure in the WFCVM. Based on these results, we recommend including stochastic velocity perturbations and topography in future simulations but conclude that updating deterministic models of basin structure will lead to the biggest improvement in forecasting ground motion for future large (M6.75+) earthquakes in the Wasatch Front region.

SummarySpectral Induced Polarization (SIP) is a geophysical technique which measures the frequency dependent electrical properties of geologic materials which can, in turn, be linked to underlying petrophysical parameters. Four-electrode SIP measurements exhibit errors above 100 Hz related to parasitic capacitive coupling (PCC) inside of the instrumentation and to the impedance of the potential electrodes. These errors can easily mask the true sample response. Existing techniques to correct SIP data infected with these errors can be complex and prone to operational error. Here we present a simple procedure that utilizes joint two- and four-electrode measurements using the same sample holder to validate high frequency SIP data. We tested the practicality of this approach by performing a series of two electrode SIP measurements on a known NaCl solution using conventional coiled current electrodes composed of different metals. We compared this procedure with both theoretical values and against a four-electrode correction procedure (referred to as the Wang correction), which utilizes four impedance measurements to directly calculate high frequency phase errors in instruments with differential amplifiers. We found that two electrode measurements conducted with coiled Ag-AgCl electrodes performed well for resistive samples and for highly polarizable samples above 100 Hz, and for conductive samples above 1 kHz. The use of joint two- and four-electrode measurements on the same sample holder is simpler than existing correction techniques and presents a straightforward alternative to the validation of high-frequency four-electrode data.

SummaryMagnetization vector inversion is an effective method for analyzing magnetic anomaly data influenced by significant remanent magnetization. However, the multi-dimensional parameters of the magnetization vector increase both the non-uniqueness of the solutions and the computational burden. We propose a magnetization vector inversion method based on Gaussian radial basis function which the magnetization vector parameters are represented by the functional node parameters. By leveraging the inherent smoothness and local support characteristics of Gaussian radial basis function, the method suppresses spurious divergence in magnetization direction during the inversion process, thereby enhancing both the accuracy and computational efficiency of the inversion results. The proposed method is applied to interpret magnetic data in Xiangshan area for revealing the magnetization characteristics of magma-hydrothermal structures. The region of non-uniform magnetization vectors, which can be interpreted as lithological contacts and alteration fronts, may indicate multiple phases of magmatic intrusion. The distinct magnetization directions between shallow mineralized bodies and underlying magma conduits facilitates the identification of potential mineralized rocks and magma conduits that are undetectable by conventional magnetic intensity analysis. Drilling in the study area confirms the presence of Cu-Ni mineralization in the shallow mafic-ultramafic intrusions. Results demonstrate that the magnetization vector inversion could capture complex geological information, providing a promising tool for understanding volcanic and magmatic systems.

SummaryOn August 27th, 2024, at approximately 19:30 UTC, the Starlink-2382 satellite entered the Earth’s atmosphere following an uncontrolled re-entry manoeuvre over Central Europe. This event resulted in a relatively low-angle re-entry of the satellite into the atmosphere, which might have provided sufficient time to burn up the satellite before reaching the Earth’s surface. This study employs acoustic-seismic (A-S) data from 226 recording stations to analyse the trajectory of Starlink-2382’s re-entry, utilizing 3-D atmosphere models including wind data and acoustic ray tracing methods. To identify signals emitted by the falling satellite, we process A-S recordings of Austrian, French, German, Italian, Slovenian, and Swiss regional seismic networks. We compute the satellite trajectory with a novel ray-based direct-search optimization method and find an azimuth angle of 120.5°±0.4° from North and an initial elevation angle of 1.5° ±0.7°, together with an entry velocity of approximately 8.9 ±0.7 km s−1. Our findings indicate that this acoustic-seismic approach, including travel time effects due to wind, achieves a better fit to our large dataset compared to the trajectory solutions from optical methods in this specific context. Furthermore, we calculate an effective ablation coefficient of 0.11 ±0.02 s2 km−2 for the main satellite fragment. Within the limits of this estimate, this is consistent with a scenario in which the main fragment, with a mass of c. 100 kg could have experienced near-complete ablation during atmospheric descent.Finite-difference modelling illustrates the complex acoustic wavefield resulting from the satellite’s deceleration and shows the expected widening of the Mach Cone. This highlights the importance of accounting for trajectory curvature and time-varying Mach angles when modelling acoustic wave propagation from low-angle re-entering objects. For recording sites with both, acoustic (infrasound) and seismic sensors, the acoustic-to-seismic ground coupling coefficients are determined. These vary up to three orders of magnitude, from 4.31 $\times $ 10−10 m s−1 Pa−1 to 5.86 $\times $ 10−7 m s−1 Pa−1 across our station sites, which is primarily explained by differences in stiffness of surface rocks.

SummaryPrevious work has demonstrated a significant correlation between the pattern of sea level change computed from an altimeter-based inference of Greenland ice mass flux from 1993-2019 and sea surface height (SSH) observations adjacent to the island. However, a key question is unanswered in this detection; namely, what constraints on ice mass flux do the SSH observations provide? To address this issue, we perform a series of inversions of the available SSH data offshore Greenland. Our results indicate that such inversions are highly non-unique. However, we also demonstrate that robust inferences can be obtained by incorporating reasonable a-priori constraints, in our case limiting the ice model to a small set of discs associated with the major drainage basins of the ice sheet that are proximal to the SSH observations. Our inversions in this case yield estimates of average ice mass loss in the range 0.62-0.70 mm/yr in units of equivalent global mean sea level change over the period 1993-2019, when the observations are corrected for the signal of dynamic sea level change. This inference agrees with independent ice altimeter-based estimates of Greenland ice sheet mass flux rates, showing broadly consistent relative ice mass loss rates across southern Greenland basins. Our analysis is the first to directly invert SSH observations for ice mass changes and we conclude that the consideration of such data, particularly in combination with other data sets (e.g., GRACE gravity, ice altimeter measurements, GNSS observations) has the potential to improve constraints on ice sheet mass changes in a warming world.

SummaryAccurate traveltime computation is fundamental to high-accuracy 3-D seismic imaging and inversion. In anisotropic media, finite-difference schemes and conventional iterative fast sweeping methods (FSM) for the eikonal equation often suffer from numerical instability or convergence difficulties when the monotonicity of the slowness surface breaks down. Thus, we propose a traveltime computation method for 3-D tilted transversely isotropic (TTI) media that embeds a semi-analytical solver into the FSM framework. The proposed semi-analytical solver employs a lower triangular–diagonal–lower triangular transpose (LDLT) decomposition together with a resolvent cubic equation to robustly factorize the local quartic traveltime equation. Combined with a Newton-Raphson-based coefficient refinement strategy and a group-velocity-based causality check, the method directly and accurately identifies the physical root corresponding to the quasi-P (qP) wave. Numerical experiments show that the semi-analytical solver has better numerical stability than existing quartic solvers. For weakly anisotropic models, the proposed method achieves an accuracy comparable to that of Newton-based local solvers. Its main advantage lies in improved robustness in strongly anisotropic media or more complicated local quartic behavior, where admissible-root selection becomes more challenging.

SummaryThe collision between the European plate and the Adria microplate during the Cenozoic led to the formation and uplift of key mountain belts, including the Alps, Apennines, and Dinarides. This convergence also resulted in a highly complex assemblage of tectonic units, each characterized by distinct geological and geophysical properties within the accreted crustal domains. A comprehensive understanding of the geodynamic evolution of this region requires integrated imaging of both the crust and upper mantle. To achieve this goal, we apply Teleseismic Full Waveform Inversion (TFWI) to P-wave seismic data recorded by permanent European broadband stations, supplemented by the dense temporary deployments of the AlpArray initiative, SWATH-D, and CIFALPS-2 projects. Leveraging this unprecedented seismological coverage, our study aims to design a suitable TFWI workflow to develop a multiparameter model defined by P- and S-wave velocities and density of the Alpine orogen down to 500-km depth. The critical importance of high-quality data for ensuring the reliability of TFWI results first prompts us to develop a semi-automated workflow for data selection and quality control, from which we select 84 teleseismic events for inversion. The seismograms were filtered within the 5-to-25-s period band, and a 30-s time window from the first arrival was used for inversion. Other critical aspects are the assessment of the resolution power of TFWI provided by the field acquisition geometry, as well as potential sources of artefacts. We review the key theoretical factors controlling resolution and imaging artefacts, and further illustrate these issues with numerical experiments designed with the field acquisition geometry to provide the necessary guidelines for sound geological interpretation of the TFWI models. The reconstructed TFWI models effectively capture key crustal features, including low-velocity sedimentary basins, high-velocity anomalies like the Ivrea Body, deep mountain roots beneath the Alpine and Apennine chains, and the signature of the continental subduction. The TFWI models also reveal small-scale anomalies previously identified by local tomography studies. Then, we extend the analysis at upper-mantle depths by comparing the footprint of the subducting slabs in the P and S velocity TFWI models with previous ones obtained by surface wave tomography and teleseismic body-wave traveltime tomography. These comparative analyses highlight the incomparable power of TFWI to resolve multiparameter models of the Earth’s interior from the surface down to the upper mantle. From this first critical analysis of the TFWI results, a comprehensive geological survey of the reconstructed structures will be presented in a companion paper.

SummaryThe complexity of earthquake rupture dynamics and the diversity of observed seismic behaviors are fundamentally governed by the frictional properties of faults and their response to tectonic stress. Grounded in the rate-and-state constitutive law derived from laboratory experiments on rock friction at slow slip velocities, we employ a fully dynamic model to investigate how frictional conditions give rise to a diverse range of rupture modes and influence their propagation dynamics. Under uniform background stress and nucleation conditions, the rupture type, whether supershear, sub‑Rayleigh, self‑arresting or slow self‑arresting rupture (SSAR), is governed by the relative contributions of the direct effect and the evolution effect, expressed as $R = 1 - \frac{a}{b}$, together with the normalized characteristic slip distance D. Their respective regimes are summarized in a phase diagram. We demonstrate that the friction parameters R and D significantly influence the rupture process, with R primarily enhancing stress release and slip during rupture, while D predominantly controls the rupture speed. For varying values of R, there exists an optimal intermediate D that maximizes rupture velocity. Furthermore, simulations suggest that when frictional parameters approach the boundaries between different rupture types regimes, the earthquake may not be confined to a single mode. Instead, a single rupture event can exhibit complex, continuous, yet rapid transitions between distinct types under a single triggering without interruption. These transitions can occur smoothly among various rupture types, including transitions from SSAR to sub-Rayleigh rupture and subsequently to supershear rupture. This study indicates the key role of frictional properties in governing rupture dynamics, offering new perspectives on the inherent complexity of earthquake processes.

SummaryThe Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission continues the legacy of satellite gravimetry in monitoring Earth’s mass redistribution. Equipped with dual-frequency Global Positioning System (GPS) receivers and a K-Band Ranging (KBR) system, it enables precise orbit determination and high-resolution gravity field recovery. While integer ambiguity resolution (IAR) has proven effective for GPS-based orbit determination, its impact on time-variable gravity field recovery remains unclear. Here we develop a dynamic framework that jointly estimates GRACE-FO satellite orbits and monthly gravity fields by integrating GPS and KBR observations, in which single-differenced integer ambiguities are fixed and constrained into the normal equations as pseudo-observations with micrometer-level constraint precision. Using GRACE-FO onboard data from July to December 2019, we compare ambiguity-fixed and ambiguity-float solutions in terms of post-fit residuals, orbit accuracy, and gravity field quality. IAR improves three-dimensional orbit precision to ∼1.2 cm RMS, with along- and cross-track components enhanced by up to 52 per cent and 71 per cent, respectively. Satellite Laser Ranging validation confirms ∼1.2 cm agreement. Gravity field solutions from float ambiguities agree closely with official Science Data System (SDS) RL06.1 models up to degree 96, whereas IAR-based solutions maintain consistency only to about degree 40 and exhibit irregular oscillations beyond this range, particularly near orbital resonance conditions around order 45. At higher degrees, these oscillations are accompanied by intensified north–south striping in equivalent water height maps. Covariance diagnostics reveal increased off-diagonal correlations between spherical harmonic coefficients under IAR, indicating weakened spectral orthogonality and potential leakage of high-degree noise. These results indicate that ambiguity-fixed gravity solutions do not consistently outperform float-based solutions beyond spherical harmonic degree 40 in the near-polar orbiting GRACE-FO constellation.

SummaryMicroseismic source location is essential for seismic monitoring and subsurface resource exploitation. Both traveltime inversion and waveform stacking methods suffer from limited accuracy when processing low signal-to-noise ratio (SNR) data under complex velocity models. Existing deep learning approaches mainly employ purely data-driven strategies without physical constraints, exhibiting limited capability to suppress large and unexpected location errors. We propose a physics-constrained deep learning method for microseismic source location that integrates the physical principles of cross-correlation stacking (CCS) imaging into network training. The method incorporates a joint loss function combining source imaging quality loss and traveltime consistency loss, with a Pareto dynamic weighting strategy to balance different loss components. Synthetic experiments on the Marmousi velocity model demonstrate that the joint-constrained method reduces the mean absolute error (MAE) from 34.09 m to 27.91 m compared to the purely data-driven approach. The maximum error decreases from 280.18 m to 130.38 m, a 53.5% reduction, demonstrating effective suppression of large location errors. The trained network achieves single-event imaging prediction in 0.04 s, providing a 75-fold speedup over the 3 s required by conventional CCS. The proposed method shows great potential in near-real-time microseismic monitoring with dense arrays.