气候变化领域集成服务门户(CCPortal): Graph-Partitioning Based Convolutional Neural Network for Earthquake Detection Using a Seismic Array

CCPortal

DOI	10.1029/2020JB020269
	Graph-Partitioning Based Convolutional Neural Network for Earthquake Detection Using a Seismic Array
	Yano K.; Shiina T.; Kurata S.; Kato A.; Komaki F.; Sakai S.; Hirata N.
发表日期	2021
ISSN	21699313
卷号	126 期号:5
英文摘要	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. © 2021. The Authors.
语种	英语
来源期刊	Journal of Geophysical Research: Solid Earth
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/187152
作者单位	The Institute of Statistical Mathematics, Tokyo, Japan; Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan; Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan; Earthquake Research Institute, The University of Tokyo, Tokyo, Japan; RIKEN Center for Brain Science, Saitama, Japan; Interfaculty Initiative in Information Studies, The University of Tokyo, Tokyo, Japan; National Research Institute for Earth Science and Disaster Resilience, Ibaraki, Japan
推荐引用方式 GB/T 7714	Yano K.,Shiina T.,Kurata S.,et al. Graph-Partitioning Based Convolutional Neural Network for Earthquake Detection Using a Seismic Array[J],2021,126(5).
APA	Yano K..,Shiina T..,Kurata S..,Kato A..,Komaki F..,...&Hirata N..(2021).Graph-Partitioning Based Convolutional Neural Network for Earthquake Detection Using a Seismic Array.Journal of Geophysical Research: Solid Earth,126(5).
MLA	Yano K.,et al."Graph-Partitioning Based Convolutional Neural Network for Earthquake Detection Using a Seismic Array".Journal of Geophysical Research: Solid Earth 126.5(2021).