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

摘要

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.

原文English
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
ISBN(列印)9781450364461
DOIs
出版狀態Published - 2018 七月 22
事件2018 Practice and Experience in Advanced Research Computing Conference: Seamless Creativity, PEARC 2018 - Pittsburgh, United States
持續時間: 2017 七月 222017 七月 26

出版系列

名字ACM International Conference Proceeding Series

Other

Other2018 Practice and Experience in Advanced Research Computing Conference: Seamless Creativity, PEARC 2018
國家United States
城市Pittsburgh
期間17-07-2217-07-26

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • 引用此

    Mu, D., Lee, E. J., Cicotti, P., Langer, F. J., Cui, Y., Qiu, J., Lee, H. J., & Morrin, C. (2018). Deep learning for seismic template recognition. 於 Practice and Experience in Advanced Research Computing 2018: Seamless Creativity, PEARC 2018 [a58] (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3219104.3219133