Geophysical Journal International

SummaryAn innovative method is presented for full 3D pressure and temperature dominated stress evaluations in the subsurface using tetrahedra-based analytical elements combined with the inflation point source solution. A mesh-free approach suitable to industry standard 3D flow simulation models (based on hexahedral cells) is obtained by representing the grid cells in tetrahedral elements, effectively preserving the 3D geometrical complexities of the reservoir. Contributions from neighboring grid cells are added via a Tartan grid representation of point sources that enables the spatial resolution to increase and the stress evaluations to be carried out in parallel, both of which significantly improve computational efficiency. The novel approach is demonstrated for synthetic low enthalpy geothermal models with clastic reservoir characteristics and varying degrees of geometrical and structural complexity. The method is shown able to accurately capture the effects of stress arching on complex faults causing reservoir throw and flow compartmentalization, and along the rim of the cold-water volume. Results of a synthetic geothermal development model of the heavily faulted Gullfaks field show the novel method to provide an accurate and computationally highly efficient approach for evaluating pressure and temperature dominated stress changes in structurally complex sedimentary reservoirs.

SummaryPore-water pressure and clay content have influence on porosity and bonding/cementing the grain boundaries, thus affecting elastic properties, strength, and other physical properties of rocks. Similar processes take place in cementitious materials, where in particular hydration plays crucial role. This process leads to changes in pore water composition, specific surface area (or alternatively Cation Exchange Capacity, CEC), and water content. Analytical expressions can be obtained between both the CEC and porosity and a hydration state variable. The hydration state variable can be in turn related to the hydration time. These phenomena can be assessed by geoelectrical methods, which have been used for a long time to observe the evolution of the textural properties and rheology of rocks, as well as of cementitious materials. However, such use has been so far rather qualitative. The aim of this study is to better describe the evolution of complex conductivity spectra (induced polarization) in relation to the hydration, using the recently developed dynamic Stern layer model to the hydration time through Powers model. Comparisons between the model predictions and literature data are used to test the suitability of the proposed solution. The model is verified using monitoring experiments of the complex conductivity spectra of several cements to study the evolution of both the in-phase and quadrature conductivity versus the hydration time. Although the current method is still semi-empirical, it makes it possible to analyze and understand the evolution of complex conductivity spectra of geomaterials and technogenic cementitious materials with a wide variety of geophysical applications in civil engineering, especially for dams and underground civil constructions.

SummaryWe have developed a physics-guided deep learning framework for geophysical inversion that incorporates Markov chain Monte Carlo (MCMC) sampling to assess the uncertainty associated with model parameters of interest. To enhance computational efficiency, a statistical sampling method is utilized to reduce the number of samples required while ensuring the training data remain both diverse and informative. As the inversion progresses iteratively, the training dataset is dynamically expanded using outputs from the stochastic sampler along with their corresponding forward responses. A supervised deep learning model is utilized, in which the Jensen-Shannon divergence is adopted as the loss function, and a Gaussian assumption is applied for analytical computation. We test the workflow on a seismic velocity model inversion, and successfully capture the geological features and velocity distributions, with results that closely match the reference model. Compared to the MCMC sampler applied to the whole data cube, the proposed workflow is more computationally efficient, as a small fraction of data is chosen using the active learning paradigm. This workflow is strongly generalizable and effective, making it suitable for a wide range of other inversion applications as well.

SummaryThe origin of seismic discontinuities in the Earth’s mid-mantle (∼700–1400 km) remains debated, with competing hypotheses attributing them to either partial melting due to water transport across the transition zone or compositional heterogeneities (subducted crust). Distinguishing between these scenarios has been hindered by the inability of standard imaging techniques to extract robustly the polarity of weak seismic reflections amidst noise and reverberations that contaminate mid-mantle reflections. Here, we introduce a novel signal processing framework that combines curvelet-based wavefield separation with extended multitaper deconvolution to resolve this polarity ambiguity. We validate this approach by applying it to a high-quality dataset of SS and PP precursors beneath the Central Pacific. This application yields the robust detection of a discontinuity at approximately 800 km depth, characterized by a sharp positive shear velocity contrast (δVS ≈ +4 − 5%) and a negligible density contrast. The observed positive polarity precludes partial melt or thermal plumes as primary causal mechanisms. Instead, the high-velocity, neutral-density signature is consistent with a layer of stagnant, subducted oceanic crust in thermal equilibration with the ambient mantle. These results demonstrate the efficacy of the deconvolution framework and provide direct seismic evidence for compositional stratification in the mid-mantle, supporting geodynamic models where viscosity increases facilitate the long-term preservation of recycled lithosphere.

SummaryGeodetic observations, such as GNSS and InSAR, are increasingly used to investigate co-seismic surface deformation. Efficiently and simultaneously resolving fault geometry and slip distribution from surface displacements is essential for understanding earthquake processes, accurately estimating seismic magnitude and comprehensively assessing seismic hazard. Current mainstream approaches typically rely on Bayesian inference, such as Monte Carlo sampling. However, these methods typically suffer from long burn-in periods, low computational efficiency, strong sensitivity to initial parameter values and step sizes. Given these limitations, conventional approaches may yield suboptimal fits for the observations. To overcome these limitations, we propose and develop a novel Tree-structured Bayesian Optimization method (TBO), integrated with Helmert Variance Component Estimation (HVCE), to jointly determine fault geometry and slip distribution. To rigorously assess the feasibility and reliability of the proposed approach, we test it using both synthetic and real earthquake data. In the synthetic tests, we evaluate its robustness under a variety of conditions, including different fault types, varying types and densities of geodetic observations, diverse sub-fault sizes and asperities, and complex multi-fault scenarios. Four sets of synthetic experiments are designed, and the results conclusively demonstrate that the proposed method achieves stable and reliable performance in inverting fault geometry and slip distribution. Furthermore, comparative analysis with existing methodologies shows that our approach yields substantially improved computational efficiency, significantly reduced sensitivity to initial conditions, and smaller misfits to observations. Finally, we apply the method to the 2021 Mw 6.4 Yangbi earthquake in Yunnan, China. The retrieved fault geometry and slip distribution successfully explain the fault kinematics and the observed surface deformation field, thereby confirming the applicability and robustness of the method for real earthquake event analysis.

SummaryGravity inversion is an essential technique for recovering subsurface density variations. Constructing stratigraphic models from gravitational observations, however, remains challenging because gravity data provide limited vertical resolution and the inverse problem is strongly non-unique. To address these limitations, we develop a Bayesian geometry-based gravity inversion framework aimed specifically for stratigraphic reconstruction. Stratigraphic interfaces are parameterized using a Fourier series, which provides a compact set of variables, enables a controllable representation of layer geometry, and improves the vertical resolution of the recovered density model. Uncertainty in the inferred stratigraphy is quantified with Stein variational inference, yielding an ensemble approximation to the posterior distribution. The resulting posterior models reveal the confidence level and spatial variability of stratigraphic layers. The proposed method has been validated using two toy examples to illustrate the main concepts. Two synthetic lunar basin models, representing alternative interpretive hypotheses for upper-crustal layering, are further designed to evaluate algorithm performance. Finally, application to satellite gravity observations from the Orientale Basin demonstrates that the proposed framework can recover the basin’s large-scale tectonic structure.

SummaryThicknesses and bulk Vp/Vs ratios of crustal layers (or Poisson’s ratio) are fundamental parameters for understanding continental structure, composition, and tectonic evolution. Traditional receiver function (RF) methods analyze P-to-S converted phases to estimate these parameters but face significant challenges in regions with low-velocity sedimentary layers, where sediment-related multiples contaminate deeper crustal signals and bias parameter estimates. We present a sequential RF-AC H-κ phase-weighted stacking method that jointly analyzes RFs and coda autocorrelations (ACs) to simultaneously constrain sedimentary and underlying crystalline crustal properties. Integration of AC data provides independent constraints on P- and S-wave reflection times, improving estimation robustness. Synthetic tests demonstrate that our method effectively suppresses sediment multiple interference and yields more reliable layer thickness and Vp/Vs ratio estimates than conventional RF-only approaches. We apply this method to four broadband stations in contrasting tectonic settings: the extensional Bohai Bay Basin and stable Tarim Basin, China. Results reveal 2.0 – 10.0 km thick sedimentary layers with Vp/Vs ratios decreasing from ~ 2.90 to 2.00 with depth, consistent with progressive compaction. Crustal thickness estimates show significant tectonic variability: 28.0 – 32.0 km in the extensional Bohai Bay Basin versus ~ 40 km in the stable Tarim Basin. Crystalline crustal Vp/Vs ratios of 1.66 – 1.73 at three stations indicate predominantly felsic composition, while a higher ratio (~ 1.80) in southwestern Tarim suggests more mafic materials. These findings agree with independent geophysical observations and geological constraints, demonstrating that this method provides a robust framework for constraining layered continental crust with thick sedimentary basins.

SummarySurface waves are sensitive to the shear wave velocity and low-velocity zone (LVZ). Here, we analyze the subsurface anomalies in the upper mantle beneath the Central Indian Ocean Basin (CIOB) utilising the ocean bottom seismometer (OBS) data. The Rayleigh wave dispersion curve analysis between earthquake clusters and OBS stations shows a period range between 12 and 300 s for the fundamental modes. A significant decrease in group velocity is observed at an intermediate period (60-180 s). The estimated depth of the lithospheric base is ∼81 km, ∼68 km, ∼67 km, and ∼82 km for P1, P2, P3, and P4 profiles respectively. A significant reduction in Vsv velocity is observed beneath the lithospheric base (i.e. ∼22-24 km thick Lithosphere-Asthenosphere boundary). Our results show an anomalous LVZ between ∼80 km and ∼170 km depth interval beneath the CIOB. A ∼18–20 per cent reduction in Vsv velocity within the LVZ suggests the presence of ∼1.9–2.0 per cent melt fraction in the shallow asthenosphere along P2 (∼3.7 km/s) and P3 (∼3.73 km/s) profiles. An excess temperature of ∼230°C is inferred across the P1-P4 profiles in the vicinity of LVZ beneath the CIOB. Henceforth, we propose a mechanism in which the presence of an unextracted melt fraction, in conjunction with the northward movement of the Indian plate and supplemented by plume-lithosphere interaction, can account for the formation and persistence of anomalous LVZ in the upper mantle. An additional ∼1 per cent melt within the LVZ along the P2 and P3 profiles, relative to the P1 and P4 profiles, favours the possibility of the west-to-east channelized asthenospheric flow beneath the CIOB region.

SummaryMagnetic inversion is a key tool for imaging subsurface geological structures, but conventional 3-D magnetic inversion in the spatial domain is often limited by the computational and memory cost of large dense kernel matrices. Existing transformed-domain approaches improve efficiency, yet pseudo-3D implementations still rely on layer-by-layer accumulation and repeated Fourier transforms. In this study, we develop a unified wavenumber-domain framework for the forward modelling and inversion of total-field magnetic anomalies and magnetic gradient-tensor data. For regularly discretized rectangular prisms beneath a planar observation surface, the wavenumber-domain Green operator is reformulated into a factorized representation consisting of two explicitly stored diagonal/block-diagonal spectral factors and one implicitly applied separable horizontal operator. This implementation avoids repeated vertical layer superposition and reduces the forward evaluation to a single FFT/IFFT pair together with structured spectral multiplications. The factorized forward operator is then embedded in a Tikhonov-regularized inversion and solved through a Sherman-Morrison-Woodbury (SMW) reduced system. The transformed-domain data term is defined as an unweighted complex-valued least-squares residual, and its relation to the spatial-domain least-squares formulation is stated under the corresponding padding and truncation assumptions. Synthetic examples show that the method reproduces conventional spatial-domain responses and recovers the principal features of prescribed magnetization models under 5% Gaussian noise. For a 200×200×100 model, the forward modeling and core inversion times are 0.172 s and 31.73 s, respectively, on a standard laptop. Application to field data is used as a practical feasibility test and shows a data-consistent recovered magnetization distribution, but it should not be regarded as an independent geological validation of the recovered model. The current implementation assumes a planar observation surface, a regular FFT-compatible grid, and a spatially uniform magnetization direction. It does not yet address strong remanence, spatially variable magnetization, irregular topography, irregular acquisition geometries, depth weighting, focusing stabilizers, or geological constraints. Under these assumptions, the proposed framework provides an efficient, memory-economical, and scalable alternative for large-scale magnetic anomaly interpretation.

SummaryWe present a high-resolution three-dimensional P-wave attenuation tomography model of the northern Chilean subduction zone (21°–22°S), derived using the coda-normalization approach implemented in the MuRAT algorithm and a dense local earthquake dataset. This region represents an important segment of the South American margin, where the Nazca Plate subducts beneath the South American Plate, generating frequent intermediate-depth seismicity and sustained volcanic activity along the Western Cordillera. Understanding the distribution of attenuation and its relation to seismicity and fluid pathways is essential for constraining the physical state of the subduction system and its role in arc magmatism and crustal deformation. The inversion incorporates 147,639 high-quality waveforms from 42,460 local earthquakes recorded by 76 broadband stations between 2007 and 2021. The inversion was carried out using a three-dimensional velocity model with 10 km node spacing, and the resulting attenuation grid was parameterized at 14 × 25 km horizontally and 10 km vertically. The attenuation model reveals two main low-Q anomalies. The first extends along and immediately above the top of the subducting Nazca slab between 50 and 90 km depth, interpreted as the locus of fluid release from slab dehydration. The second low-Q zone ascends from the mantle wedge towards the lower crust beneath the volcanic arc, indicating fluid migration. These features coincide with high-Vp/Vs regions from velocity tomography models. Low-Q regions are generally found above seismicity concentrations in the downgoing Nazca slab, reaffirming the association of intraslab earthquakes with fluid release processes. Resolution tests confirm the robustness of the imaged structures. The obtained anomalies trace subduction-related fluids from their source in the downgoing slab through the mantle wedge towards the magmatic arc.

SummaryThe explicit inertial modes in spheres and oblate spheroids, owing to their clear and concise mathematical formulations, have been applied in many geophysical and astrophysical studies. In contrast, the implicit inertial modes are rarely used because of their mathematical complexity. Due to the presence of factorials and double factorials inherited from the associated Legendre polynomials, the computation of explicit inertial modes becomes intractable at high orders. Based on the implicit inertial modes, this research, for the first time, develops a new algorithm that enables fast computation of the inertial modes in spheres and spheroids of arbitrary eccentricity even at high orders. In addition, it offers an efficient approach to computing the geostrophic polynomials, which are a set of special inertial modes with zero frequency in spheres and spheroids. In this new algorithm the inertial modes and the half-frequencies are expressed as functions of the associated Legendre polynomials and their first derivatives with respect to the modified oblate spheroidal coordinates. Several numerical experiments demonstrate the efficiency of this new algorithm. It is also verified that both the non-penetrable boundary condition and the incompressible condition are satisfied by the numerical results produced by this algorithm.

SummaryTectonic gravity anomalies are commonly assumed as static, except during major geodynamic events like earthquakes or plate reorganizations. This study challenges such an assumption at the regional scale by examining the ongoing rifting in the Gulf of Aden. Using 3D finite element and gravitational modelling, it can be shown that horizontal motion between oceanic and continental crusts – characterized by a density contrast of 400 kg/m3 and a divergence rate of 1.25 cm/yr – generates a potentially measurable gravity rate of change, forming a dipolar pattern with peak amplitudes of ±40 nGal/yr. Numerical simulations were conducted to evaluate whether this signal could be actually measured by the forthcoming MAGIC satellite mission. To this aim, the time-variable gravity field derived from the 3D finite element was propagated into orbit simulations, considering only instrumental noise. A series of 1-year least squares solutions were computed from the simulated data in terms of spherical harmonics. Then gravity disturbance grids at 5 km height covering the Gulf of Aden were derived and the gravity rate was estimated at each point of the grid, considering different maximum harmonic degree. Results indicate that the noise level of the MAGIC instrumentation is low enough to make it sensible to this signal, despite spatial resolution limitations. The two opposing gravity stripes cannot be distinguished, but a central bump of gravity rate with an amplitude of about 6 nGal/yr can be well identified by considering a maximum harmonic degree of 70. Of course, the detectability of such a signal from MAGIC observations becomes unfeasible when considering the temporal aliasing induced by other geophysical phenomena involving stronger and faster mass transport. Nevertheless, these findings suggest that tectonic processes associated with rifting can induce measurable gravity variations (given the accuracy level of MAGIC instrumentation), even in the absence of episodic seismic activity, offering new prospects for satellite gravimetry in monitoring active plate boundaries.

SummarySeismic full waveform inversion (FWI) is a powerful technique that uses seismic waveform data to generate high resolution images of the Earth’s interior. However, significant uncertainty exists in all FWI solutions due to imperfect acquisition geometries, inherent noise in the data, nonlinearity of the forward problem, and the under-determined nature of real-world tomographic problems in which the target is heterogeneous over all length scales. Probabilistic Bayesian FWI addresses this non-uniqueness by estimating the entire family of possible model solutions and thus the solution uncertainty, described by the so-called posterior probability density function (pdf) over model parameter values. The posterior pdf can be estimated using nonlinear inversion methods to quantify full uncertainties, including those created by nonlinearity in the physics. Alternatively, by linearising (approximating) the physics relating parameters and observations around a chosen reference model solution, the posterior pdf is usually approximated by a compact distribution centred around the maximum a posteriori solution, typically a Gaussian pdf. This is referred to as the linearised method. In this work, we apply both nonlinear and linearised methods to 2D acoustic Bayesian FWI problems. We use one variational inference algorithm for the nonlinear case, in which a transformed Gaussian distribution is optimised to approximate the unknown, full posterior pdf, and a second, independent nonlinear variational algorithm – Stein variational gradient descent – for comparison. The results of both are then compared with those from a linearised, locally-Gaussian based method. The results show that while both the linearised and nonlinear methods recover the posterior mean models accurately, they exhibit different posterior uncertainty structures, especially around layer interfaces, due to the linearisation of wave physics. The differences become most obvious in partially constrained regions of the model, where posterior solutions are constrained jointly by data, prior information, and the nonlinearity of wave physics rather than being dominated by any single factor. We also demonstrate that linearised uncertainty estimates are significantly less accurate: they provide far less accurate fits to observed waveform data, and yield biased estimates of inferred or interpreted meta-properties such as volumes of geological bodies. This work therefore motivates the application of fully nonlinear inversion methods in Bayesian FWI if either accurate uncertainty estimates over parameters, or inferred or interpreted meta-properties are important.

SummaryReliable automatic phase picking is important for many seismic applications. With the development of machine learning approaches, many algorithms are proposed, evaluated and applied to different areas. Many of these algorithms are single station based, while recent proposed methods start to combine surrounding stations into consideration in the problem of phase picking. Among these algorithms, the Phase Neural Operator (PhaseNO) shows promising results on regional datasets comparing to existing algorithms. But there are many use cases for the local seismic networks in our community, therefore in this paper we evaluate the performance of PhaseNO on 4 different local datasets and compare the results to PhaseNet and EQTransformer. We used both individual phase picking metrics as well as association metrics to illustrate the performance of PhaseNO. By manually reviewing the newly detected events, we find the PhaseNO model outperforms the single station-based approaches in the local-scale use cases due to its consideration of coherent signals from multiple stations. We also explored PhaseNO’s behaviors when only using one station, as well as gradually increasing the number of stations in the seismic network to better understand its behavior. Overall, using the off-the-shelf machine learning based phase pickers, PhaseNO demonstrated its good performance on local-scale seismic networks.

SummaryEarthquake fault slip arises from nonlinear coupling among frictional evolution, elastic loading, and pore-pressure changes. When pore pressure evolves dynamically, the resulting hydro-mechanical rate-and-state models can be stiff and strongly coupled, making parameter inversion computationally demanding. Here we develop a physics-informed neural network (PINN) solver for a coupled spring–slider system that combines rate-and-state friction with pore-pressure/porosity evolution. The network approximates the time-dependent state variables and is trained by enforcing the governing differential equations together with initial conditions and, for inverse problems, observational constraints. To improve training stability, we employ adaptive inverse-residual weighting and a two-stage optimization schedule (Adam followed by L-BFGS). In forward simulations, PINN predictions closely match a Runge–Kutta reference solution across steady sliding and slow-slip transients, with normalized mean squared error below 0.08 and Pearson correlation coefficient above 0.975 for block velocity and frictional shear stress in the cases tested. In inverse experiments, the framework recovers the applied normal stress from noisy shear-stress observations; uncertainty increases with noise amplitude, but the ensemble mean remains stable, and at the highest noise level considered (q = 1) the inferred normal stress deviates by less than ~1% from the reference value. These results suggest that PINNs provide a differentiable alternative for forward modeling and parameter inversion in coupled hydro-mechanical rate-and-state fault models.

SummaryWe estimated seismic moment tensors (MTs) for the 2017 magnitude M6 Hojedk, Central Iran, earthquake triplet and their aftershocks, employing 1D and 3D regional and global velocity models to evaluate source parameter stability and resolution fitting in-country waveform data. We used the Moment Tensor Uncertainty Quantification (MTUQ) software, which performs a grid search for MT estimation and uncertainty analysis. For the regional 3D velocity model, we used MEAD-M20, a full-waveform inversion model of the Middle East derived from fitting 15-s body waves with 30-s body and surface waves. We compared the regional 1D- and 3D-based results with an existing database of deviatoric MT solutions, and for both regional velocity models, we found good agreement. However, for periods T ≥ 25–30 s and events with moment magnitudes Mw≥ 4.5, the 3D regional synthetic seismograms outperformed the 1D regional model, reducing waveform misfits, time shifts, and non-double-couple contributions. We consider non-double-couple contributions spurious and their reduction an improvement, as previous studies of the sequence found predominantly shear faulting on reverse faults. Furthermore, uncertainty analysis shows that the moment tensor, non-double-couple component, magnitude, and depth are more tightly constrained for the 3D model. The current 3D model shows no clear improvements relative to the 1D model in terms of misfit, time-shifts, and non-double-couple contributions at short periods T ≈ 15-25 s relevant for modeling smaller events. Using the regional models results in lower misfits and tighter constraints on the MT solutions than with the global 1D PREM and the 3D S2.9EA models. Improved MT estimation and parameter resolution for moderate-to-large events using in-country data validate the recently developed 3D Middle East velocity model. Further model refinements are needed to model shorter-period data required to analyze and improve the resolution of smaller (M ≤ 4) seismic events. Such improvements are within reach by using available in-country data and well-constrained MT solutions from a regional moment tensor database.

SummaryMonitoring the activity of non-natural seismic events is crucial for constructing an accurate seismic catalog, overseeing the safety of industrial operations, and mitigating the potential threats to local residents. However, recent studies have shown that neural network models trained on local data sets may not generalize well to a different region. Here, we leverage the Siamese neural network (SNN) to enhance the generalization of neural network models in discriminating between blasts, collapses, and natural earthquakes under regional shifts. Two distinct data sets are analyzed. The model is trained on a data set from northeastern China and tested on an out-of-region data set from the Inner Mongolia Autonomous Region and Gansu Province. We evaluate the prediction performance of the SNN model against the Convolutional neural network (CNN) model using the K-fold cross-validation technique. Results show that both the CNN and the SNN models achieve highly comparable performance on the in-domain validation data set. However, when applied to the out-of-region test data set, the SNN model with target-region anchors can improve the predicted AUPRC values by 7% and 4% compared with that of the out-of-region CNN model and the CNN model with transfer learning using target-region anchors, respectively. Furthermore, Grad-CAM importance weight analysis shows that the SNN model mainly relies on early-arrival P- and S-wave trains. The study suggests that SNN model with target-region anchors can deliver better generalization and flexibility than the conventional CNN model under regional shifts, which is particularly valuable for regions lacking labeled data sets.

SummaryAfter the 2015 Ms 6.5 Pishan earthquake, three moderate-magnitude earthquakes, the Ms 5.4 Pishan earthquake on September 4, 2021, the Ms 5.4 Yecheng earthquake on September 5, 2021 and the Ms 5.4 Pishan earthquake on October 23, 2022, occurred in the seismically active western Kunlun Range foreland thrust system. The seismogenic structures responsible for the three most recent earthquakes and their relationships with the 2015 Ms 6.5 Pishan event are still not understood. Integrated analysis of relocation results of the three main events and their aftershock sequences, focal mechanism solutions, and regional geology has identified the seismogenic fault and structural geometry near the earthquake source. Our results recognize a gentlely S-dipping Kuoshi fault ramp, which is the frontal structure at the west part of the WKFTS and is responsible for the three most recent moderate-magnitude earthquakes. The 2015 Ms 6.5 Pishan earthquake and the recent moderate-magnitude events were all generated by the frontal fault ramp, indicating a deformation pattern characterized by simple outward thrusting. In the western Kunlun Range foreland, the most active deformation and topographic growth have migrated northward relative to those of the higher terrace folds as the rear ramp slip ceases. Our results provide new insights into deformation pattern and seismic hazard in the region.

SummaryWe present a new, regionally adjusted local magnitude (ML) model for Switzerland and surrounding regions. The model is derived based on Wood-Anderson displacement amplitudes (AWA) calculated from 150,000 high-quality waveforms from 15,000 earthquakes between 2000 and 2025, recorded by more than 700 seismic instruments. This dataset is substantially richer than those used in previous ML studies in Switzerland, with a large number of near-source recordings and data from low-magnitude events, which were notably sparse in earlier works. AWA attenuation over hypocentral distance is parametrised through linear and logarithmic distance terms along with hinge distance points, which allow proper modelling of the attenuation characteristics at long distances and changes in attenuation associated with post-critical reflected phases. Regional differences in attenuation between the Alpine region in southern Switzerland and the northern Foreland are smoothly modelled through a ray-path-specific regional adjustment parameter, allowing the model coefficients and the hinge distances to vary spatially. The coefficients of the parametric attenuation curves are estimated using mixed-effects regressions, and the model is anchored to yield a magnitude 3 for an AWA of 10 mm measured at a hypocentral distance of 17 km. The station terms are calculated with respect to Swiss reference rock conditions. The new ML model reduces uncertainty by 33 per cent compared to the current ML scale used by the Swiss Seismological Service and does not exhibit any residual trends with respect to hypocentral distance, earthquake depth, local site conditions, or event magnitude. Empirical radiation pattern corrections are derived, further reducing the uncertainty by 8 per cent for strike-slip events. Alternative models, based on non-parametric and cell-based 2D approaches, are derived independently to validate the parametrisation of the parametric model. The new model – MLS26 – yields lower magnitudes for smaller events (with catalogue magnitudes lower than about 2.5) and for events located in the northern Foreland, whereas the magnitudes of the larger Alpine events remain similar. The reduced magnitudes of smaller events decrease the b-value of the input earthquake catalogue from 1.00 to 0.93, corresponding to a reduction of about 7 per cent. MLS26 scales one-to-one with moment magnitude (MW) for MLS26 > 4, while for smaller events, it scales with the logarithm of the seismic moment.

SummaryThis study aims to understand the recurrent seismicity that occurs in a limited volume along a major fault in the French western Alps, the Vuache Fault, which crosses the geological Jura in a flat-and-ramp zone. In 1996, an M5.3 earthquake occurred near Annecy (France), located at a depth of approximately 2 km. In this article, we analyze the seismicity that has occurred since then and calculate the seismic velocity variations at local permanent seismic stations. The magnitude and focal mechanism of the 1996 M5.3 earthquake indicate that the process was tectonic in origin. However, the duration of the Omori decay of its aftershocks, their migration, the variations in spring flow, the existence of repeated swarms, the seasonal variations in seismic velocity and their relationship to rainfall and seismicity show that the continuation of this seismicity is linked to the pressurization of fluids in a deep, fractured aquifer connected to the surface. This aquifer appears to be limited to the sedimentary formations. Earthquakes, especially during the M5.3 aftershock migration, reach the depth of the Triassic gypsum. The aftershock sequence occurred in two different phases: an initial phase lasting around ten days, during which the earthquakes did not show any migration but rather a random spatial distribution, and a second phase showing a clear migration, suggesting a fluid diffusion process. During this last phase, the hydraulic diffusivity of the aquifer was calculated and estimated at around 2 m²/s. The same order of magnitude was obtained by using the correlation between seismic velocity variations and rainfall. This is a high value, close to those found during man-made fluid injections. This aquifer forms a confined, pressurized reservoir between two thrusts in the regional flat-and-ramp structure. The scenario described in this article could be found, at various scales, in flat-and-ramp regions where tectonic stresses are sufficient to generate seismicity.