Geophysical Journal International

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.