Geophysical Journal International

SummaryThe time-variable gravity field obtained from the Gravity Recovery and Climate Experiment/Follow-On (GRACE/GRACE-FO) satellites has been successfully used to detect global water mass changes over the past two decades. However, the north-south striping noise in the GRACE spherical harmonic (SH) solution limits their effectiveness. Efforts to suppress this noise and achieve a higher signal-to-noise ratio (SNR) continue with various product releases, but there is still a great need for improvement. This study presents a new de-striping method called GBVMD, which employs a stepwise enhancing framework combining Gaussian filtering with bi-dimensional variational mode decomposition (BVMD). The methodological breakthrough comes from two innovations: First, it employs adaptive scale decomposition by dynamically adjusting the radius of the Gaussian filter in conjunction with BVMD reconstruction, effectively reducing noise across multiple scales. Second, it features a dual-decision optimization strategy that integrates SNR-driven mode reconstruction and iterative termination, thereby maximizing the SNR while adapting to the specific characteristics of the noise. In simulations, the GBVMD outperforms the five other filters in reducing noise and keeping signals, achieving an improvement in SNR by at least 19%, and reductions in root mean square error and mean absolute error by at least 14% and 11%, respectively. When applied to GRACE/GRACE-FO Level-2 SH solutions, GBVMD led to a higher SNR with an improvement of at least 12% compared to other filters. The GBVMD-filtered SH data showed strong consistency with three Level-3 Mascon solutions across 183 river basins. Comparable results were also found in polar regions, validated by altimetry data. Furthermore, we effectively corrected the leakage errors for two examples in the Caspian Sea and the Great Lakes, demonstrating the advantages of GBVMD-filtered SH over the Mascons for signal reanalysis. We recommend GBVMD for further applications, especially in specific regions such as ocean areas and other satellite missions requiring similar de-striping approaches.

SummaryDue to the extremely destructive characteristics of seismic hazards, and as one of the effective pre-seismic physical signals, abnormal changes in subsurface fluids can provide key precursor information for earthquake prediction. Furthermore, an efficient method for labelling anomalies in seismic monitoring data is urgently needed. Therefore, this paper analyses the change characteristics of subsurface fluid-water level data and proposes an Anomaly Detection Algorithm of Single Variable Time Series Data Based on Dynamic Parameter Tuning (ADSV-DPT) based on three important characteristics (jump, step and steep), which firstly determines the central tendency of the data by calculating the median of the water level data within the initial window, and then utilizes the Median Absolute Deviation (MAD) as a robust dispersion metric to reduce the impact of extreme values on the anomaly detection. The sliding window mechanism is employed to update the median and MAD step by step, thereby ensuring the efficiency and adaptability of the algorithm in processing time series data. Finally, the anomalies in the data are detected by setting dynamic thresholds. A comparison of the anomaly detection efficacy of the proposed ADSV-DPT algorithm with that of three alternative models (namely, K-Nearest Neighbor, KNN; Pruned Exact Linear Time, PELT; and One-Class Support Vector Machine, OC-SVM) was conducted. The experimental results demonstrate that the ADSV-DPT algorithm outperforms the other models in accurately identifying anomalous features. The average precision, recall, and F1-score of the ADSV-DPT algorithm all exceed 85%. The algorithm's capacity for adapting to variations in the data is noteworthy, as is its ability to accurately identify abnormal values that deviate from the established normal range.

SummaryThe quantitative interpretation of geological structures relies on multi-parameter models (MPMs) inversion. However, conventional full waveform inversion that matches simulated seismic data to observed seismic data cannot accurately obtain high-resolution MPMs because of the implicit inter-parameter coupling relations in the multi-parameter wave equation. Additionally, conventional supervised deep learning approaches that require a significant number of annotated labels cannot predict precise MPMs, as only a limited number of sophisticated synthetic MPMs are available as labels. To address this issue, we propose a self-supervised multi-parameter inversion (SS-MPI) to provide high-resolution MPMs from the prior first-arrival-based tomography and reflection-based migration image. SS-MPI creates representative MPMs from the prior information as pseudo-labels to pre-train the deep learning algorithm, which then predicts MPMs as feedback to update these training pseudo-labels iteratively. Synthetic examples of elastic and anisotropic models indicate that SS-MPI outperforms the conventional elastic full waveform inversion (EFWI) and delivers highly accurate and high-resolution MPMs.

SummaryPermafrost degradation in the Arctic is both an indicator of, and contributor to warming global temperatures. In addition to the global impact of thawing permafrost, at the local scale permafrost degradation can result in infrastructure damage, ecosystem changes, chemical and microbial releases, landfill leaks, and river bank erosion. As such, accurate mapping and monitoring of the in-situ permafrost extent is crucial. Differentiation of frozen and thawed arctic soils is commonly achieved through electrical geophysical imaging methods; however, recently a hysteresis-like phenomenon was observed in the electrical conductivity of soils undergoing freezing followed by thawing. This phenomenon can result in over an order of magnitude difference in conductivity when measured on the same sample at precisely the same temperature. Here, we explore this effect in a clean sand with low surface conduction and a frost-susceptible clay-rich soil with relatively higher surface conduction. The unsaturated samples were prepared at a moisture content of 16.5 per cent by weight. Each soil was evaluated under four different NaCl pore-fluid conductivities 10 ppm, 100 ppm, 1000 ppm, 1000 ppm. We then developed a model to simulate the full hysteresis loop for the soil. In both cases the hysteresis effect was clearly observed in the real component of the conductivity. In the frequency dependent imaginary conductivity response only the frost-susceptible soil demonstrated consistent signs of hysteresis along with a soil-specific frequency response.

SummaryDownward continuation is a very interesting approach to enhance the information content of potential field data. However, the calculation of the downward continuation represents a fundamental challenge due to its inherent instability. In this study, a strategy to perform high-order vertical derivatives using the β-VDR method is introduced, called the generalized β-VDR method. Testing on a noisy synthetic model shows that the proposed strategy has the lowest noise compared to other methods. Based on stable vertical derivatives computed by using the generalized β-VDR method, a stable downward continuation method is also presented to enhance the information content of potential field data. The applicability of the generalized β-VDR downward continuation algorithm is demonstrated on both synthetic and real field gravity anomalies and compared to other downward continuation algorithms. In the case of synthetic examples, the proposed method provides sharper images and estimates more accurate amplitudes than other algorithms, even continuing the field to a level close to causative bodies. The real application shows that the proposed algorithm can give a meaningful result that agrees well with seismic data along a profile in the area.

SummaryThere are two fundamental probabilities in the seismic phase picking process – the probability of the existence of a seismic phase (detection probability) and the probability associated with the phase arrival time estimation (timing probability). The nearly ubiquitous approach in developing deep learning phase picking models is to use a kernel, such as a truncated Gaussian, to mask the labeled phase arrival time and train a segmentation model. Once a model is trained, the times of the peaks in the output are taken as phase arrival times (picks), and the height of the peaks are taken as “probability” of the picks. Here, we show that this “probability” represents neither the detection nor the timing probability because this approach forces the output to follow the shape of the kernel. We introduce an approach using two models to estimate these two distinct probabilities. We use a binary classifier with a calibrated confidence to address the detection probability and a multi-class classifier to obtain a probability mass function to address the timing probability. This new approach can make the deep learning-based phase picking process more interpretable and provide options to logically control seismic monitoring workflows.

SummaryThis study introduces a new method for calculating acoustic-gravity waves in a spherically layered atmosphere. The method introduces a model assumption and divides the atmosphere into finely stratified layers to solve the PDE with respect to the radial coordinate. The time-domain synthetic signal is obtained by summing over the orders of the associated Legendre functions and then applying the FFT. The method is applied to numerically simulate wave behaviour, including Earth curvature effects, and compares with the horizontally layered model (HLM). Results show that at near-field distances, our method aligns closely with HLM, but significant differences emerge in the far field, particularly beyond an epicentral distance of 50°, where Earth curvature becomes critical. Our method successfully simulates head waves of seismic phases, and Rayleigh waves, even for waves travelling multiple times around the Earth, which HLM cannot achieve. Simulations using a homogeneous Earth model reveal head wave characteristics consistent with previous studies, with the strongest energy observed in Rayleigh head waves. The application of the AK135 Earth model highlights the visibility of seismic phases through the Earth’s core. We validate our method by comparing synthetic records with actual data from the 1999 Chi-Chi earthquake. The synthetic records show good agreement with observed seismic signals and ionospheric perturbations in terms of arrival time and wave envelope. These results demonstrate the accuracy of our method in simulating acoustic-gravity waves at large epicentral distances.

SummaryThe widespread, multi-year crustal deformation induced by megathrust earthquakes (Mw8+) is primarily controlled by the combined effects of continuous aseismic slip on the fault plane (afterslip) and viscoelastic relaxation driven by coseismic stress perturbations in the upper mantle. However, till today it remains a considerable challenge to separate these two mechanisms in geodetic observations. We derived the first 3-year GNSS observations following the 2021 Chignik Mw8.2 earthquake to investigate the mechanisms of postseismic deformation. We established a model capable of simultaneously simulating afterslip and viscoelastic relaxation, and constrained the upper mantle rheology beneath the Alaska Peninsula. The best-fit model effectively reproduces the GNSS observations and reveals a notable viscosity difference between the mantle wedge and the oceanic asthenosphere, with steady-state viscosities of $3 \times {{10}^{18}}$ Pa s and $4 \times {{10}^{19}}$ Pa s, respectively. The inferred mantle wedge viscosity beneath the Alaska Peninsula is lower than the values reported for south-central and southeastern Alaska, suggesting an eastward increase in viscosity along the subduction zone. Two main patches of afterslip are identified during the first 3 years. The patch of up-dip afterslip overlaps with the 1938 Chignik Mw8.3 earthquake rupture zone, and demonstrates a close spatial correlation with the slow slip event in 2018. The above new results enhance our insights into the spatial variability of regional rheology and slip behavior along the Alaska-Aleutian subduction zone.

SummaryNew Q (1/attenuation) models of the Central Alpine Fault provide unprecedented resolution to 20 km depth by incorporating new t* measurements from dense temporary seismograph deployments in the area. The models reveal significant heterogeneity in the crust, with the main Q features broadly similar along-strike the Alpine Fault but varying at length scales of 10-30 km. Accounting for heterogeneity is an important step towards understanding the seismic cycle of M7+ Alpine Fault earthquakes. Our models show the Alpine Fault as a southeast-dipping zone of very (<300) to moderately (600-900) low Q, contrasting sharply with high Q values (Qp>600, Qs>1000) within the Western Province bedrock and high Q values (Qp∼900, Qs∼1200) associated with uplifted Alpine schists to the east. The wealth of previous geologic and geophysical studies along this section of the Alpine Fault support a detailed interpretation of the observed Q values. We interpret the low Q values along the Alpine Fault as resulting from enhanced fracturing within the brittle crust with a proportion of these fractureslikely filled with fluids, which further enhance seismic attenuation through viscous dissipation. In the ductile crust (below ∼8 km depth), low Q values (<400) are likely predominantly caused by grain-size reduction from very high total shear strain and by small amounts of metamorphic fluids. Low Q values of 200-400 at 20-40 km depth downdip of the Alpine Fault and the generally low Q (<600) within the crustal root farther from the Alpine Fault, suggest increasing role of metamorphic fluids relative to that of grain-size reduction with depth and distance from the fault. The updated model also reveals a newly identified zone of low Q east of the Main Divide, approximately 40 km southeast of the Alpine Fault trace. This zone of low Q indicates significant strain accumulation on faults striking along the eastern flank of the Southern Alps, some of which have produced M6+ earthquakes in recent history. These faults represent a considerable seismic hazard for the South Island. The improved dataset and recent velocity models from temporary deployments also allow us to investigate the influence of the initial velocity model on the resulting t* measurements and Q models.

SummaryThe bulk component of the electrical conductivity of a porous material is related to the (connected) porosity and saturation by power-law functions defining the first and second Archie's laws. Recently, it was shown that for porous materials with fractal characteristics, the power-law exponent of Archie's law could be related to the fractal dimension of such materials. Similarly, the real and imaginary parts of the complex-valued surface conductivity are not just proportional to the specific surface area and saturation of the material but to power law functions of these properties defining two additional “interfacial” Archie's laws, which are called the third (saturated case) and fourth (unsaturated case) Archie's laws in this paper. These new laws have been poorly recognized and studied so far. A number of porous materials and especially clay-rich media are multiscale materials characterized by broad distributions of particle and pore sizes. We extend Archie's laws concept to describe the complex conductivity of such materials. We use both numerical simulations in fractal porous materials as well as published experimental datasets to propose a unified physical interpretation of the exponents entering the four Archie's type power-law relationships, which offer an updated complex conductivity model for natural porous media.

SummeryThe seismic hazard due to higher magnitude Himalayan earthquakes largely depends on the geometry of the underthrusting Indian Plate beneath the Himalayas, i.e., the Main Himalayan Thrust (MHT). For an objective assessment of seismic hazard in the central Himalayan seismic gap, we determine the geometry of the Main Himalayan Thrust (MHT) along 4 ∼SW-NE oriented arc-normal seismic profiles covering the central Himalayan seismic gap. We use teleseismic P- wave coda autocorrelation on waveforms recorded at 117 broadband seismic stations spread along these profiles, with an interstation spacing of 3-5 km. The results show that along these seismic profiles, the MHT is mostly of flat-ramp-flat geometry. However, the mid-crustal ramp of the MHT shows variations in its location, dip angle, and width. We also observe variations in the MHT near the Main Frontal Thrust (MFT) and Main Boundary Thrust (MBT). The observed variations in the MHT geometry within the central Himalayan seismic gap thus suggest the possibility of along-strike segmentation of the Himalayan arc, and different seismic hazard scenarios may be present during any possible higher magnitude earthquake in the central Himalayan seismic gap.

SummaryRayleigh wave is widely used for characterizing shallow subsurface structures. The conventional Rayleigh-wave methods rely on the manual picking of dispersion curves, and the dispersion curves of multi-component data are usually merged manually. The manual processing of multi-component Rayleigh waves reduces the efficiency of the method, especially when the data size and the number of modes are large. To overcome these limitations, we develop an energy-based clustering method, namely the Energy-Density-Based Spatial Clustering of Applications with Noise (E-DBSCAN) algorithm. The E-DBSCAN algorithm extracts energy clusters and dispersion curves from a single dispersion image. It considers the dispersion-energy values of the surface wave and is able to pick the dispersion curve more reliably compared with the conventional DBSCAN algorithm. We propose a two-step clustering approach for the automatic picking of multi-mode dispersion curves from multi-component data: we first extract the energy clusters in the dispersion spectra of horizontal- and vertical-component data using E-DBSCAN, respectively, and combine them in the frequency-velocity domain. Then we extract multi-modal dispersion curves from the combined multi-component energy clusters with E-DBSCAN or DBSCAN. Numerical results show that our proposed method has fairly high accuracy and estimates more abundant multi-modal dispersion curves than the single-component method. Two field examples, including an active-source and an ambient-noise dataset, prove the validity of our method and the outperformance of multi-component results compared with the single-component results. Our proposed method has a relatively low dependence on parameter selection and is also applicable to multi-offset data, which is valuable for picking multi-modal dispersion curves.

SummaryRecently, there has been an increasing interest in employing rotational motion measurements for seismic source inversion, structural imaging and ambient noise analysis. We derive reciprocity and representation theorems for rotational motion. The representations express the rotational motion inside an inhomogeneous anisotropic earth in terms of translational and rotational motion at the surface. The theorems contribute to the theoretical basis for rotational seismology methodology, such as determining the moment tensor of earthquake sources.

SummaryDetermining earthquake focal mechanisms is essential for understanding fault geometry and the stress field in the Earth's crust. When focal mechanisms are estimated using P-wave first-motion polarities, accurate polarity determination is critical. In recent years, deep-learning-based polarity-determination models have been developed. However, the estimation of focal mechanisms using P-wave polarities is often not robust. When automating this process using deep learning models, it is crucial to identify and utilize only those polarity predictions that the model infers with high accuracy and low uncertainty. In this study, we propose a novel deep learning model, PoViT-UQ, that combines a Vision Transformer (ViT) with Monte Carlo Dropout (MCD) to estimate high-precision initial P-wave polarity classification and arrival time detection with uncertainty quantification. Using seismic waveform data sampled at 100 and 250 Hz, the model classifies polarities into three classes (Up, Down, and Noise) and simultaneously estimates P-wave arrival times. The results showed a classification accuracy exceeding 98% and a standard deviation of 0.027 s in the arrival time estimation using the 250 Hz model. By integrating MCD, we evaluate prediction uncertainty and apply an interquartile range threshold of ≤0.15 to improve the accuracy of focal mechanism estimates. Validation using aftershock data from the 2016 Central Tottori Earthquake confirmed that our approach contributes to efficient and high-precision focal mechanism estimates. Our model advances automated initial P-wave polarity determination and enables reliable data selection based on uncertainty quantification.

SummaryThe flows within Earth’s fluid outer core push and pull on the core-mantle boundary (CMB) through dynamic pressure variations, potentially leading to deformation of the CMB. It is therefore crucial to obtain a realistic estimate of the pressure associated with flows within the fluid core. In many studies, it is commonly assumed that the flow tangent to the CMB is in balance between Coriolis and pressure gradient forces, known as a tangentially geostrophic (TG) flow. A static pressure field is thereby associated kinematically to the flow field at the core’s surface. We run direct numeric simulations of the magnetohydrodynamic equations in the Boussinesq approximation that can solve for the pressure field and allow for a comparison between a fully dynamic solution and the TG pressure estimate. An excellent agreement between the two pressure fields is found for a steady image of the core surface dynamics. However, the performance of the TG pressure estimate is not without limitations. Although it effectively captures most of the temporal dynamics associated with the fluid flow, discrepancies arise, particularly near the equator and for rapid changes in flow dynamics.

AbstractSeismology has entered the petabyte era, driven by decades of continuous recordings of broadband networks, the increase in nodal seismic experiments, and the recent emergence of Distributed Acoustic Sensing (DAS). This review explains how cloud platforms, by providing object storage, elastic compute, and managed databases, enable researchers to “bring the code to the data,” thereby providing a scalable option to overcome traditional HPC solutions’ bandwidth and capacity limitations. After literature reviews of cloud concepts and their research applications in seismology, we illustrate the capacities of cloud-native workflows using two canonical end-to-end demonstrations: 1) ambient noise seismology that calculates cross-correlation functions at scale, and 2) earthquake detection and phase picking. Both workflows utilize Amazon Web Services, a commercial cloud platform for streaming I/O and provenance, demonstrating that cloud throughput can rival on-premises HPC at comparable costs, scanning 100 TBs to 1.3 PBs of seismic data in a few hours or days of processing. The review also discusses research and education initiatives, the reproducibility benefits of containers, and cost pitfalls (e.g., egress, I/O fees) of energy-intensive seismological research computing. While designing cloud pipelines remains non-trivial, partnerships with research software engineers enable converting domain code into scalable, automated, and environmentally conscious solutions for next-generation seismology. We also outline where cloud resources fall short of specialised HPC-most notably for tightly coupled petascale simulations and long-term, PB-scale archives-so that practitioners can make informed, cost-effective choices.

SummaryThe 1985 Mw 6.9 Wuqia earthquake, one of the strongest instrumentally recorded seismic events in the Pamir foreland thrust system, caused significant surface ruptures. The Pre-earthquake KH-9 and post-earthquake WorldView-3 and SPOT-6 satellite images are used to investigate the fault rupture and slip behavior of this earthquake. We revealed a more detailed ∼22 km long displacement belt beyond the previously documented ∼15 km rupture, using optical image correlation with sophisticated error post-processing. Several new fractures in western segment are identified which are confirmed in the displacement map. A comprehensive analysis of the strike change, near-surface dip and cross-fault offsets shows a ∼1.6 km dextral strike-slip tearing fault resulted from the heterogeneous strain release. Based on the empirical scaling relationship, a down-dip rupture width of 10.55 km is estimated using the observed rupture length and inverted slip. Combined with the previously published 3D fault geometry based on seismic imaging, we suggest that the 1985 Wuqia earthquake ruptured only the upper ramp. This study provides precise constraints on surface rupture characteristics, and new insights into the complex rupture pattern of a thrust-type earthquake within the tectonically active Pamir foreland region.

SummaryUnderstanding the geophysical drivers of seasonal geocenter motion (GCM) variations remains challenging due to the complexity of Earth system interactions, limited data on individual mass redistribution components, and model uncertainties. This study presents a comprehensive investigation of seasonal GCM signals from April 2002 to January 2024 using the Fingerprint Approach (FPA), which enables direct quantification of contributions from distinct Earth system components. Additionally, Multichannel Singular Spectrum Analysis (MSSA) is applied to quantify the influence of terrestrial water storage (TWS), atmosphere (ATM), and ocean (OCN) variability on seasonal GCM fluctuations. Correlation and lag analyses are employed to explore their temporal relationships and underlying geophysical linkages. The results reveal that TWS, ATM, and OCN jointly explain 97.9 per cent, 98.1 per cent, and 90.8 per cent of the seasonal variance in the X, Y, and Z components of GCM, respectively. TWS exerts as the dominant contributor in the Y (66.4 per cent) and Z (67.9 per cent) components, while ATM and OCN each contribute less than 49 per cent in all components. Further analysis indicates that ATM, OCN, and TWS exhibit varying lag relationships with GCM in the X and Z components, while TWS demonstrates a notably stronger correlation with GCM in the Y component. Importantly, an approximately 120-day periodic signal identified in GCM is, for the first time, linked to global precipitation variability, providing a novel geophysical interpretation. These findings enhance our understanding of climate-driven geophysical mass redistribution and offer new insights into the processes governing seasonal GCM variations.

SummaryCrashing ocean waves, or surf, have previously been identified as persistent generators of coherent infrasound signals from 0.5 to 20 Hz. Here, we demonstrate that infrasonic and seismic (seismo-acoustic) signals from surf are composed of repetitive transient events which can be detected and characterized using template matching. Using data collected from a series of field experiments designed to study seismo-acoustic surf signals in Santa Barbara, California, we show that source regions of these events can be constrained primarily to just offshore of a local coastal headland using a reverse-time-migration implementation on a small spatial scale (<5 km2). Our data include one continuously running infrasound sensor (September 2022–July 2023) to examine temporal signal evolution, complemented by several short-duration campaigns involving various infrasound arrays, co-located seismometers, and video recordings. Throughout varied oceanographic and atmospheric conditions, we detect up to tens of thousands of independent surf repeaters per day over the course of a year. The amplitudes of detected infrasound signals are correlated with offshore significant wave height and local wind speed. We identify coincident arrivals of seismic and infrasound signals with similar spectral characteristics, suggesting a linked source mechanism locally producing both the seismic and acoustic transient signals. Source regions estimated from array- and network-based methods correspond to the surf zone as seen in video footage, and the directions of selected transient signals align with the location of a rocky reef shelf nearshore. This work showcases the ability to extract near-real-time information about the coastal sea state from seismic and acoustic signal features.

SummaryCurrent geodetic velocities show that over half (up to 10 mm/yr) of Arabia-Eurasia shortening in the west is accommodated within a relatively narrow zone across the Kura basin of Azerbaijan, in which the most prominent active structure is the Kura fold-and-thrust belt, bordering the southern margin of the Greater Caucasus. The GNSS velocities furthermore suggest equivalent amounts of north-south right-lateral shear across the eastern Kura basin along the West Caspian fault zone that is accommodating relative motion between the Kura basin and the South Caspian basin. Although destructive historical earthquakes are known to have occurred, their spread is restricted geographically and their moment release accounts for only half of the accumulated deformation. These observations can be explained by incompleteness of the historical record, that the faults fail in rare larger earthquakes, or that they slip aseismically. To distinguish between these hypotheses we produce an InSAR velocity field using Sentinel-1 SAR data to image active tectonic deformation within the Kura basin of Azerbaijan. Tectonic signals are superimposed on those relating to non-tectonic processes, including widespread mud volcano inflation that highlights the important role of fluid flow within the basin sediments. We show aseismic creep occurs on two parallel faults of the West Caspian fault zone, and infer this also on the Kura Fold and Thrust Belt from sharp gradients in velocity indicating active fold growth. Recent paleoseismic studies of the faults imaged here indicate discrete slip events, and we speculate that the creep may be episodic, perhaps triggered by deeper earthquake events or by periods of enhanced fluid mobilisation. Together, the right-lateral and left-lateral faults appear to accommodate a large-scale expulsion of the Absheron region towards the South Caspian basin, perhaps driven by gravitational potential energy contrasts.