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Construction of Long-term Seismic Catalog with Deep Learning and Characterization of Preseismic Fault Behavior in the Ridgecrest-Coso Region (2008-2019)
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  • Yijian Zhou,
  • Abhijit Ghosh,
  • Lihua Fang,
  • Han Yue,
  • Shiyong Zhou
Yijian Zhou
Department of Earth and Planetary Sciences, University of California Riverside

Corresponding Author:yijian.zhou@email.ucr.edu

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Abhijit Ghosh
University of California, Riverside
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Lihua Fang
Institute of Geophysics, China Earthquake Administration
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Han Yue
Peking University
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Shiyong Zhou
Peking University
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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.
11 Mar 2023Submitted to ESS Open Archive
13 Mar 2023Published in ESS Open Archive