Long-term seismicity is an effective tool to infer fault properties at depth, but the catalog construction is challenging because of the large data volume. We propose a new deep learning-based workflow that follows a “Train-Detect-Pick” procedure, which solves the generalization problem in AI pickers. We apply the new workflow on the preseismic phase (2008-2019) of Ridgecrest-Coso region. Results show that the new workflow realizes efficient and stable detection, and well substitutes matched filter. Our new catalog helps characterize the preseismic fault behavior: (1) the Ridgecrest area has a distributed deformation, and the 2019-ruptured segment has a persistent asperity; (2) the central Garlock fault is unfavorable for rupture propagation, because of its discontinuous geometry and low coupling ratio; (3) the Coso geothermal field generates intense and shallow seismicity, which has a high b-value that does not correlate with seismicity rate and industrial production, thus suggest a low stress level.

Discrimination between tectonic earthquakes and quarry blasts is important for accurate earthquake cataloging and seismic hazard analysis. However, reliable classification is challenging with raw waveforms and no prior knowledge of source parameters. Here we apply deep learning to perform this task in southern California and eastern Kentucky, which differ significantly in available labelled data, class imbalance and waveform characteristics. Accordingly, we adopt different strategies for the two regions. First, we directly train a convolutional neural network (CNN) for southern California due to its data abundancy. To alleviate class imbalance, the blast data are augmented by randomly shifting waveform windows. The model for California yields an accuracy of 91.97% for single-station classification and 97.54% for network-averaged classification. Second, as eastern Kentucky has a much smaller data size, we fine-tune the pretrained California model to fit the Kentucky data. The fine-tuned model yields an accuracy of 97.35% for single-station classification and 99.46% for network-averaged classification. The fine-tuned model outperforms the model trained from scratch. Finally, we use occlusion test and gradient-weighted class activation mapping to illuminate which parts of waveforms are important for model prediction. Our results demonstrate that deep learning can achieve high accuracy in seismic event discrimination with raw waveforms and that transfer learning is effective and efficient to generalize deep learning models across different regions.

The 2021 Mw 6.1 Yangbi earthquake in southwest China is preceded by three major foreshocks: 05/18 Mw 4.3, 05/19 Mw 4.6, and 05/21 Mw 5.2. It provides a valuable chance to revisit two end-member models describing earthquake interaction: cascade-up and pre-slip model. We first determine the associated fault structure with relocated aftershocks and focal mechanisms obtained from multi-point-source inversion. We find that the mainshock and two smaller foreshocks occur on an unmapped near-vertical fault, and the largest foreshock occurs on a mapped stepover fault that dips to NE. Secondly, for each major foreshock, we estimate and delineate their rupture area based on aftershocks and spectral ratio analysis. Based on the rupture model, we finally calculate the evolution of Coulomb stress, with which to interpret the causality of each major event. Results show that the Yangbi sequence can be explained by the cascade triggering mechanism, while we also find evidence for aseismic slip that contributes to the triggering process: the first foreshock is preceded by a short-term localized cluster, and the aftershock zone of the second foreshock extents through time. The nucleation of mainshock is probably contributed by multiple major foreshocks through both seismic and aseismic processes. This detailed seismological characterization of Yangbi sequence lend supports for a deeper understanding on the foreshock mechanism: (1) the controlling mechanisms are not limited to cascade-up & pre-slip, multiple mechanisms can operate together; and (2) aseismic slip does not always provide more predictability on the mainshock.

The Xiaojiang Fault (XJF) Zone locates in the southeastern of Tibetan Plateau and defines the boundary between the South China and Sichuan-Yunnan blocks. Historical large earthquakes were hosted on the XJF, though its seismic hazard in the near future is under debate. In this study, we utilize portable broad-band seismic network to unravel the microseismic activities along XJF, and to further investigate the fault structures and their properties. Adopting PALM, a newly developed earthquake detection algorithm, we obtained ~13,000 relocated events. The micro-seismicity reveals widespread off-fault structures showing conjugate geometry, while the major faults present low seismicity. The fault branches conjugate to the main-fault present intensive microseismicity, which hosts repeating events and presents high b-value. Regional GPS stations reflect slips are mostly concentrated along the XJF, while the slip rate on off-fault branches correlates with seismic activities on these structures. Combining with other recent seismological and magnetotellurics evidences, we suggest a low strength on these off-fault structures, which may partially release tectonic stress loading and serve as a barrier for future big earthquakes. On the XJF, the microseismic events are clustered on the fault junctions with low b-value. A special set of clusters between 25°N to 25.5°N show an along-strike variation of depth from 10 to 25-km, imaging the boundary between creeping and locked fault portions. We revisit the seismic hazard problem of XJF, and conclude that XJF is at the late stage of inter-seismic period.

Intra-continental dip-slip earthquakes often occur in the orogen and rift zones with complex tectonics, providing rare opportunities to illuminate the deformation and evolution of continental structures. However, due to the sparse seismological and geodetic observations, such earthquakes are less studied. Here, we report the fault geometries of two dip-slip earthquakes recently occurred in Southern Tian Shan and the Mongolia-Baikal rift zone revealed by InSAR . The 2020 Mw6.0 Jiashi earthquake occurred in the Keping-tage fold-and-thrust belt in southwest Tian Shan. This region is seismically active, yet most well-recorded earthquakes occurred south of the mountain front. The lack of large earthquakes beneath the belt thus hinders our understanding of the orogenic process to the north. Combining InSAR measurements and relocated aftershocks, we found that a fault model involving a shallow thrust fault and two deeper faults can best reconcile the surface deformation and aftershock distribution. Stress analysis suggests that slips on the shallow fault reactivated the older basement structures at depth. Our results reflect the basement-involved shortening activated by a thin-skinned thrust faulting event with surface deformation, implying a southward orogenic process of the southwest Tian Shan. The 2021 Mw6.7 Lake Hövsgöl earthquake occurred in the Mongolia-Baikal rift zone (MBRZ), which is located in the northern tip of the northern Mongolia, and is bounded by the Tibet Plateau orogenic belt and the Siberian Platform. Using the Bayesian inversion method we derived a fault plane with two slip patches, one is mainly strike slip and the other is mainly normal slip component. The correlation between the observed and predicted displacements by the single fault model is 97.46%. Coulomb stress analysis shows that the 2021 event has a triggering effect on the western segment of the Tunka Fault to the north, where no large earthquakes have occurred since the 1905 M8+ earthquake, raising the potential for seismic risk in this region.

The Keping-tage fold-thrust belt in southwest Tian Shan is seismically active, yet most well-recorded earthquakes occurred south of the mountain front, hindering our understanding of the orogenic process to the north. The 2020 Mw6.0 Jiashi earthquake is an important event with surface deformation in the nappe structure well illuminated by InSAR. Here, we employ the surface deformation and relocated aftershocks to investigate the fault slip distribution associated to this event. Further added by an analysis of Coulomb stress changes, we derive a fault model involving slips on a shallow low-angle (~10º) north-dipping thrust fault as well as on a left-lateral tear fault and a high-angle south-dipping reverse fault in mid crust. Our results reflect the basement-involved shorterning activated by a thin-skinned thrust faulting event with the surface deformation implying the basin-ward orogenic process of the southwest Tian Shan.