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ISSN  2096-3955

CN  10-1502/P

Citation: Zhou, P. C., Ellsworth, W. L., Yang, H. F., Tan, Y. J., Beroza, G. C., Sheng, M. H., and Chu, R. S. (2021). Machine-learning-facilitated earthquake and anthropogenic source detections near the Weiyuan Shale Gas Blocks, Sichuan, China. Earth Planet. Phys., 5(6), 501–519.

2021, 5(6): 501-519. doi: 10.26464/epp2021053


Machine-learning-facilitated earthquake and anthropogenic source detections near the Weiyuan Shale Gas Blocks, Sichuan, China


Key Laboratory of Ocean and Marginal Sea Geology, South China Sea Institute of Oceanology, Innovation Academy of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, Guangzhou 510301, China


Earth System Science Programme, Faculty of Science, The Chinese University of Hong Kong, Sha Tin, Hong Kong 999077, China


Department of Geophysics, Stanford University, Stanford, CA 94305, USA


State Key Laboratory of Geodesy and Earth’s Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China

Corresponding author: HongFeng Yang,

Received Date: 2020-07-25
Web Publishing Date: 2021-11-15

Seismic hazard assessment and risk mitigation depend critically on rapid analysis and characterization of earthquake sequences. Increasing seismicity in shale gas blocks of the Sichuan Basin, China, has presented a serious challenge to monitoring and managing the seismicity itself. In this study, to detect events we apply a machine-learning-based phase picker (PhaseNet) to continuous seismic data collected between November 2015 and November 2016 from a temporary network covering the Weiyuan Shale Gas Blocks (SGB). Both P- and S-phases are picked and associated for location. We refine the velocity model by using detected explosions and earthquakes and then relocate the detected events using our new velocity model. Our detections and absolute relocations provide the basis for building a high-precision earthquake catalog. Our primary catalog contains about 60 times as many earthquakes as those in the catalog of the Chinese Earthquake Network Center (CENC), which used only the sparsely distributed permanent stations. We also measure the local magnitude and achieve magnitude completeness of ML0. We relocate clusters of events, showing sequential migration patterns overlapping with horizontal well branches around several well pads in the Wei202 and Wei204 blocks. Our results demonstrate the applicability of a machine-learning phase picker to a dense seismic network. The algorithms can facilitate rapid characterization of earthquake sequences.

Key words: induced seismicity, machine learning, Weiyuan Shale Gas Block

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Machine-learning-facilitated earthquake and anthropogenic source detections near the Weiyuan Shale Gas Blocks, Sichuan, China

PengCheng Zhou, William L. Ellsworth, HongFeng Yang, Yen Joe Tan, Gregory C. Beroza, MinHan Sheng, RiSheng Chu