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地震 ›› 2023, Vol. 43 ›› Issue (4): 67-75.doi: 10.12196/j.issn.1000-3274.2023.04.005

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LSTM神经网络拾取地震P波到时

吴为治1, 汪小厉2, 何泽平1   

  1. 1.安徽省勘查技术院, 安徽 合肥 230041;
    2.安徽省地震局, 安徽 合肥 230031
  • 收稿日期:2023-02-09 修回日期:2023-06-09 出版日期:2023-10-31 发布日期:2023-12-29
  • 作者简介:吴为治(1989-), 男, 安徽无为人, 工程师, 主要从事人工智能应用研究。
  • 基金资助:
    安徽省深地钻探工程研究中心开放基金项目(2022AHSD06)

LSTM Neural Network for Automatic P Phase Picking

WU Wei-zhi1, WANG Xiao-li2, HE Ze-ping1   

  1. 1. Geological Exploration Technology Institute of Anhui Province, Hefei 230041, China;
    2. Anhui Earthquake Agency, Hefei 230031, China
  • Received:2023-02-09 Revised:2023-06-09 Online:2023-10-31 Published:2023-12-29

摘要: 震相到时拾取是地震学的一个基础问题, 本文针对该问题发展了地震P波到时自动拾取的LSTM神经网络方法。 该方法将到时问题转化成到时标签化后的概率问题, 建立了一个4层网络模型, 利用朝鲜核爆地震垂向数据进行了模型的训练, 成功对后续核爆地震P波到时做出准确的拾取, 且该方法对一定信噪比的数据仍具有一定适应性。 通过对添加不同程度噪声的随机截取的输入数据进行可靠性分析, 结果表明需要输入P波之后10 s以上的波形才能有稳定的拾取结果。 该方法作为一种人工智能的方法, 为波形到时自动拾取提供了新的方法。

关键词: LSTM神经网络, 震相到时拾取, 核爆地震

Abstract: Picking up the arrival time of seismic phases is one of the fundamental problems of seismology. This study introduces a method based on LSTM neural network for automatic P phase picking. We transformed the arrival time problem into the probability problem of time redefined labeling, and a 4-layer neural network was built and trained on the North Korean nuclear earthquake vertical waveforms. The P phase arrival time of subsequent events were picked up accurately and effectively. The method shows a certain adaptability with ambient noise. Random input samples test shows that the input waveform data should be better with longer than 10 s after the P phase to get stable results. As an artificial intelligence method, LSTM neural network provides a new solution for seismic phase picking-up.

Key words: LSTM neural network, P phase picking, Nuclear explosion earthquake

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