Graph-Partitioning Based Convolutional Neural Network for Earthquake Detection Using a Seismic Array

Keisuke Yano, Takahiro Shiina, Sumito Kurata, Aitaro Kato, Fumiyasu Komaki, Shin'ichi Sakai, Naoshi Hirata

研究成果: ジャーナルへの寄稿学術誌査読

19 被引用数 (Scopus)


We present a deep-learning approach for earthquake detection using waveforms from a seismic array consisting of multiple seismographs. Although automated, deep-learning earthquake detection techniques have recently been developed at the single-station level, they have potential difficulty in reducing false detections owing to the presence of local noise inherent to each station. Here, we propose a deep-learning-based approach to efficiently analyze the waveforms observed by a seismic array, whereby we employ convolutional neural networks in conjunction with graph partitioning to group the waveforms from seismic stations within the array. We then apply the proposed method to waveform data recorded by a dense, local seismic array in the regional seismograph network around the Tokyo metropolitan area, Japan. Our method detects more than 97% of the local seismicity catalog, with less than 4% false positive rate, based on an optimal threshold value of the output earthquake probability of 0.61. A comparison with conventional deep-learning-based detectors demonstrates that our method yields fewer false detections for a given true earthquake detection rate. Furthermore, the current method exhibits the robustness to poor-quality data and/or data that are missing at several stations within the array. Numerical experiments using subsampled data demonstrate that the present method has the potential to detect earthquakes even when half of the normally available seismic data are missing. We apply the proposed method to analyze 1-h-long continuous waveforms and identify new seismic events with extremely low signal-to-noise ratios that are not listed in existing catalogs. We also show the potential portability of the proposed method by applying it to seismic array data not used for the training.

ジャーナルJournal of Geophysical Research: Solid Earth
出版ステータス出版済み - 5月 2021

!!!All Science Journal Classification (ASJC) codes

  • 地球物理学
  • 地球化学および岩石学
  • 地球惑星科学(その他)
  • 宇宙惑星科学


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