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Deep learning for P-wave arrival picking in earthquake early warning

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Abstract

Fast and accurate P-wave arrival picking significantly affects the performance of earthquake early warning (EEW) systems. Automated P-wave picking algorithms used in EEW have encountered problems of falsely picking up noise, missing P-waves and inaccurate P-wave arrival estimation. To address these issues, an automatic algorithm based on the convolution neural network (DPick) was developed, and trained with a moderate number of data sets of 17,717 accelerograms. Compared to the widely used approach of the short-term average/long-term average of signal characteristic function (STA/LTA), DPick is 1.6 times less likely to detect noise as a P-wave, and 76 times less likely to miss P-waves. In terms of estimating P-wave arrival time, when the detection task is completed within 1 s, DPick’s detection occurrence is 7.4 times that of STA/LTA in the 0.05 s error band, and 1.6 times when the error band is 0.10 s. This verified that the proposed method has the potential for wide applications in EEW.

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Acknowledgment

This research has been supported by the National Natural Science Foundation of China (Grant Nos. 51968016 and 5197083806) and the Guangxi Innovation Driven Development Project (Science and Technology Major Project, Grant No. Guike AA18118008).

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Authors and Affiliations

  1. College of Architecture and Civil Engineering, Beijing University of Technology, Beijing, 100022, China

    Wang Yanwei & Li Xiaojun

  2. Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering, Guilin University of Technology, Guilin, 541004, China

    Wang Yanwei & Bao Enhe

  3. College of Architecture and Civil Engineering, Henan University, Kaifeng, 475004, China

    Wang Zifa

  4. China Earthquake Administration, Institute of Engineering Mechanics, Harbin, 150080, China

    Wang Zifa

  5. China Academy of Railway Sciences, China Railway Corporation, Beijing, 100081, China

    Shi Jianping

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  1. Wang Yanwei
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  2. Li Xiaojun
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  3. Wang Zifa
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  4. Shi Jianping
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  5. Bao Enhe
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Corresponding author

Correspondence to Wang Zifa.

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Supported by

National Natural Science Foundation of China under Grant Nos. 51968016 and 5197083806, and the Guangxi Innovation Driven Development Project (Science and Technology Major Project, Grant No. Guike AA18118008).

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Yanwei, W., Xiaojun, L., Zifa, W. et al. Deep learning for P-wave arrival picking in earthquake early warning. Earthq. Eng. Eng. Vib. 20, 391–402 (2021). https://doi.org/10.1007/s11803-021-2027-6

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  • DOI: https://doi.org/10.1007/s11803-021-2027-6

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