气候变化领域集成服务门户(CCPortal): SCALODEEP: A Highly Generalized Deep Learning Framework for Real-Time Earthquake Detection

CCPortal

DOI	10.1029/2020JB021473
	SCALODEEP: A Highly Generalized Deep Learning Framework for Real-Time Earthquake Detection
	Saad O.M.; Huang G.; Chen Y.; Savvaidis A.; Fomel S.; Pham N.; Chen Y.
发表日期	2021
ISSN	21699313
卷号	126 期号:4
英文摘要	The detection of earthquake signals is a fundamental yet challenging task in observational seismology. A robust automatic earthquake detection algorithm is strongly demanded in view of the ever-growing global seismic dataset. Here, we develop an automatic earthquake detection framework based on a deep learning approach (SCALODEEP). It extracts high-order features embedded in three-component seismograms by encoding a time-frequency representation of the data (scalogram) into a deep network with skip connections. The SCALODEEP is trained and validated on an open-source dataset from North California, and then employed to seismicity detection in four areas, including Arkansas, Japan, Texas, and Egypt. Despite vastly varying characteristics of regional earthquakes (e.g., focal mechanism, duration, and noise level), SCALODEEP successfully detects seismic signals over a broad range of local magnitudes (as low as −1.3 (Formula presented.)) and outperforms conventional algorithms such as STA/LTA, FAST, and template matching. Compared to recently proposed deep learning based frameworks (e.g., CRED and Earthquake transformer), SCALODEEP achieves a superior generalization ability via a sophisticated network architecture. In summary, our study offers a promising new tool to improve existing earthquake detection systems and, as importantly, sheds light on designing an effective deep learning network for generalized earthquake detection. © 2021. American Geophysical Union. All Rights Reserved.
英文关键词	deep learning; earthquake; generalization; real-time
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/187206
作者单位	Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou, China; Seismology Department, National Research Institute of Astronomy and Geophysics (NRIAG), Helwan, Egypt; TexNet Research and the Center for Integrated Seismicity Research, Bureau of Economic Geology, University of Texas at Austin, Austin, TX, United States; Texas Consortium for Computational Seismology, Bureau of Economic Geology, University of Texas at Austin, Austin, TX, United States
推荐引用方式 GB/T 7714	Saad O.M.,Huang G.,Chen Y.,et al. SCALODEEP: A Highly Generalized Deep Learning Framework for Real-Time Earthquake Detection[J],2021,126(4).
APA	Saad O.M..,Huang G..,Chen Y..,Savvaidis A..,Fomel S..,...&Chen Y..(2021).SCALODEEP: A Highly Generalized Deep Learning Framework for Real-Time Earthquake Detection.Journal of Geophysical Research: Solid Earth,126(4).
MLA	Saad O.M.,et al."SCALODEEP: A Highly Generalized Deep Learning Framework for Real-Time Earthquake Detection".Journal of Geophysical Research: Solid Earth 126.4(2021).