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Real-time determination of earthquake focal mechanism via deep learning

Author

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  • Wenhuan Kuang

    (Stanford University)

  • Congcong Yuan

    (Harvard University)

  • Jie Zhang

    (University of Science and Technology of China)

Abstract

An immediate report of the source focal mechanism with full automation after a destructive earthquake is crucial for timely characterizing the faulting geometry, evaluating the stress perturbation, and assessing the aftershock patterns. Advanced technologies such as Artificial Intelligence (AI) has been introduced to solve various problems in real-time seismology, but the real-time source focal mechanism is still a challenge. Here we propose a novel deep learning method namely Focal Mechanism Network (FMNet) to address this problem. The FMNet trained with 787,320 synthetic samples successfully estimates the focal mechanisms of four 2019 Ridgecrest earthquakes with magnitude larger than Mw 5.4. The network learns the global waveform characteristics from theoretical data, thereby allowing the extensive applications of the proposed method to regions of potential seismic hazards with or without historical earthquake data. After receiving data, the network takes less than two hundred milliseconds for predicting the source focal mechanism reliably on a single CPU.

Suggested Citation

  • Wenhuan Kuang & Congcong Yuan & Jie Zhang, 2021. "Real-time determination of earthquake focal mechanism via deep learning," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21670-x
    DOI: 10.1038/s41467-021-21670-x
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    Cited by:

    1. Weiqiang Zhu & Ettore Biondi & Jiaxuan Li & Jiuxun Yin & Zachary E. Ross & Zhongwen Zhan, 2023. "Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Jiaxuan Li & Weiqiang Zhu & Ettore Biondi & Zhongwen Zhan, 2023. "Earthquake focal mechanisms with distributed acoustic sensing," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

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