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Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning

Author

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  • Weiqiang Zhu

    (California Institute of Technology
    University of California)

  • Ettore Biondi

    (California Institute of Technology)

  • Jiaxuan Li

    (California Institute of Technology)

  • Jiuxun Yin

    (California Institute of Technology)

  • Zachary E. Ross

    (California Institute of Technology)

  • Zhongwen Zhan

    (California Institute of Technology)

Abstract

Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. However, its distinct characteristics, such as unknown ground coupling and high noise level, pose challenges to signal processing. Existing machine learning models optimized for conventional seismic data struggle with DAS data due to its ultra-dense spatial sampling and limited manual labels. We introduce a semi-supervised learning approach to address the phase-picking task of DAS data. We use the pre-trained PhaseNet model to generate noisy labels of P/S arrivals in DAS data and apply the Gaussian mixture model phase association (GaMMA) method to refine these noisy labels and build training datasets. We develop PhaseNet-DAS, a deep learning model designed to process 2D spatio-temporal DAS data to achieve accurate phase picking and efficient earthquake detection. Our study demonstrates a method to develop deep learning models for DAS data, unlocking the potential of integrating DAS in enhancing earthquake monitoring.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43355-3
    DOI: 10.1038/s41467-023-43355-3
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    References listed on IDEAS

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