Geophysical Journal International

SummaryInstrument correction is a fundamental step in broadband seismology, yet it is commonly treated as a purely numerical operation stabilized by heuristic procedures. In practice, inverse filtering of instrumental responses is constrained by causality, stability, discretization, and uncertainty in instrument parameters, which jointly limit the recoverable frequency content and the physical interpretability of corrected ground motion. Here I present a unified framework for causal and uncertainty-aware instrument correction that explicitly formulates deconvolution as a constrained inverse problem in the digital domain. The proposed approach enforces causal realizability and bounded inverse behaviour while introducing regularization as a physically interpretable control of the bias–noise trade-off. Discretization effects arising from the mapping between continuous- and discrete-time responses are quantified and shown to induce systematic, frequency-dependent amplitude bias that interacts nonlinearly with regularization. I further extend the formulation to incorporate parametric uncertainty in the instrument response, propagating it through the inverse filter to derive confidence bounds on effective amplitude response and noise amplification. A set of diagnostic metrics is introduced to jointly characterize amplitude bias, noise amplification, effective bandwidth, and robustness under uncertainty. These diagnostics are combined into a data-driven decision framework that supports objective selection of the inverse filter and explicitly defines the frequency range over which instrument correction is reliable. Time-domain kernel diagnostics complement the frequency-domain analysis by ensuring causal behaviour and controlled temporal support. The framework is summarized in an end-to-end algorithmic workflow designed for reproducible application to broadband seismic data without reliance on ad hoc stabilization choices. By making physical constraints, trade-offs, and uncertainties explicit, the proposed methodology enhances the robustness and interpretability of instrument-corrected seismic waveforms and provides a principled foundation for downstream analyses in source studies, spectral characterization, and waveform-based investigations.

SummaryAirborne transient electromagnetic (ATEM) inversion relies on efficient forward modeling, yet conventional numerical methods struggle to balance computational efficiency and accuracy when handling undulating terrain and large survey datasets. We present a fast forward modeling approach using multi-scale dilated residual networks that takes two-dimensional conductivity profiles with embedded terrain information as image inputs and directly predicts electromagnetic response tensors across multiple flight altitudes. The network architecture employs progressively increasing dilation rates to capture multi-scale geological features while preserving spatial resolution. A dual-domain loss function combining log-normalized and linear domains balances the fitting weights across electromagnetic responses spanning seven orders of magnitude. We trained and tested the model on 100,000 synthetic samples of random terrain and geoelectric structures generated with the SimPEG three-dimensional finite volume method. The network achieves mean absolute percentage errors (MAPE) of 2.24%-3.57% across four terrain types (flat, slope, peak, and valley). Spline interpolation enables accurate prediction at arbitrary flight altitudes not included in training, with errors of 1.69%-3.53%. On a 10 km survey profile, the method achieves approximately 2000-fold speedup compared to SimPEG. This approach provides a practical forward modeling solution for rapid ATEM inversion over complex terrain, and the proposed multi-scale feature extraction strategy and dual-domain loss function design offer transferable insights for other geophysical machine learning applications.

SummaryTo address the challenge in seismic exploration where strong-energy ground roll severely interferes with effective signals and conventional suppression methods tend to damage these signals, this paper proposes a self-supervised Swin-Unet network method for ground roll suppression based on Fourier Positional Encoding and a bespoke masking strategy. This method operates without the need for clean label data. By employing a specially designed fan-shaped masking strategy, it disrupts the spatio-temporal coherence of the ground roll, thereby guiding the network to learn the intrinsic characteristics of the effective signals and reconstruct the data. The core innovation lies in the introduction of Fourier Positional Encoding, which overcomes the inherent low-frequency bias of the Transformer architecture. This significantly enhances the network’s capability to model and recover high-frequency effective signals. Experimental results on synthetic data and field 2D/3D seismic datasets demonstrate that the proposed method not only effectively suppresses strong ground roll but also surpasses the traditional f – k filtering method in terms of signal fidelity, particularly in preserving deep, weak reflections and high-frequency components. This showcases its robustness and potential for application in complex seismic data processing.

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.