Hybrid Event Detection and Phase‐Picking Algorithm Using Convolutional and Recurrent Neural Networks

Research Article| April 10, 2019 Hybrid Event Detection and Phase‐Picking Algorithm Using Convolutional and Recurrent Neural Networks Yijian Zhou; Yijian Zhou aSchool of Earth and Space Sciences, Peking University, 5 Yiheyuan Road, Beijing 100871, China, yue.han@pku.edu.cn Search for other works by this author on: GSW Google Scholar Han Yue; Han Yue aSchool of Earth and Space Sciences, Peking University, 5 Yiheyuan Road, Beijing 100871, China, yue.han@pku.edu.cn Search for other works by this author on: GSW Google Scholar Qingkai Kong; Qingkai Kong bBerkeley Seismological Laboratory, University of California, Berkeley, 209 McCone Hall, Berkeley, California 94720 U.S.A. Search for other works by this author on: GSW Google Scholar Shiyong Zhou Shiyong Zhou aSchool of Earth and Space Sciences, Peking University, 5 Yiheyuan Road, Beijing 100871, China, yue.han@pku.edu.cn Search for other works by this author on: GSW Google Scholar Author and Article Information Yijian Zhou aSchool of Earth and Space Sciences, Peking University, 5 Yiheyuan Road, Beijing 100871, China, yue.han@pku.edu.cn Han Yue aSchool of Earth and Space Sciences, Peking University, 5 Yiheyuan Road, Beijing 100871, China, yue.han@pku.edu.cn Qingkai Kong bBerkeley Seismological Laboratory, University of California, Berkeley, 209 McCone Hall, Berkeley, California 94720 U.S.A. Shiyong Zhou aSchool of Earth and Space Sciences, Peking University, 5 Yiheyuan Road, Beijing 100871, China, yue.han@pku.edu.cn Publisher: Seismological Society of America First Online: 10 Apr 2019 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2019) 90 (3): 1079–1087. https://doi.org/10.1785/0220180319 Article history First Online: 10 Apr 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Yijian Zhou, Han Yue, Qingkai Kong, Shiyong Zhou; Hybrid Event Detection and Phase‐Picking Algorithm Using Convolutional and Recurrent Neural Networks. Seismological Research Letters 2019;; 90 (3): 1079–1087. doi: https://doi.org/10.1785/0220180319 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT We developed a hybrid algorithm using both convolutional and recurrent neural networks (CNNs and RNNs, respectively) to pick phases from archived continuous waveforms in two steps. First, an eight‐layer CNN is trained to detect earthquake events from 30‐second‐long three‐component seismograms. The event seismograms are then sent to a two‐layer bidirectional RNN to pick P‐ and S‐arrival times. The data for training and validation and testing of the networks are obtained from the continuous waveforms of 16 stations recording the aftershock sequence of the 2008 Wenchuan earthquake. The augmented training set has 135,966 P–S‐wave arrival‐time pairs. The CNN achieved 94% and 98% hit rate for event and noise segments in the test set, respectively. The RNN picking accuracies for P and S waves are −0.03±0.48 (mean error ± standard deviation) and 0.03±0.56 s⁠, respectively. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.