Deep learning for seismic template recognition

Dawei Mu, En Jui Lee, Pietro Cicotti, Florin James Langer, Yifeng Cui, Junyi Qiu, Haemin Jenny Lee, Cody Morrin

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)


Detecting earthquakes is one of the most fundamental tasks in seismology. As continuous seismic recordings grow and become readily available, they offer opportunities for identifying weak seismic signals (e.g., microseismicity, nonvolcanic tremor). Mining this source of data recordings is now considered a substantial method to study various geophysical phenomena. Widely used methods, such as the template matching algorithm (TMA), use waveforms of confirmed seismic signal to slide through corresponding continuous recordings searching similarities. These methods present several weaknesses, including sensitivity to the choice of thresholds, signal-to-noise ratios (SNRs), and bias. In this work, we present a solution for seismic signal recognition using emerging deep learning techniques. We have designed a Convolutional Neural Network (CNN) that recognizes seismic signals using features extracted by multiple filter kernels. This software is designed for training with data from multiple stations and the SNRs of template waveforms. We demonstrate the accuracy of our approach using seismic waveform recordings from multiple stations during the 2016 ML 6.6 Meinong earthquake sequence in Taiwan.

主出版物標題Practice and Experience in Advanced Research Computing 2018
主出版物子標題Seamless Creativity, PEARC 2018
發行者Association for Computing Machinery
出版狀態Published - 2018 7月 22
事件2018 Practice and Experience in Advanced Research Computing Conference: Seamless Creativity, PEARC 2018 - Pittsburgh, United States
持續時間: 2017 7月 222017 7月 26


名字ACM International Conference Proceeding Series


Other2018 Practice and Experience in Advanced Research Computing Conference: Seamless Creativity, PEARC 2018
國家/地區United States

All Science Journal Classification (ASJC) codes

  • 軟體
  • 人機介面
  • 電腦視覺和模式識別
  • 電腦網路與通信


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