Title | LOC-FLOW: An End-to-End Machine Learning-Based High-Precision Earthquake Location Workflow |
Author | |
Corresponding Author | Zhang,Miao |
Publication Years | 2022-09-01
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DOI | |
Source Title | |
ISSN | 0895-0695
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EISSN | 1938-2057
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Volume | 93Issue:5Pages:2426-2438 |
Abstract | The ever-increasing networks and quantity of seismic data drive the need for seamless and automatic workflows for rapid and accurate earthquake detection and location. In recent years, machine learning (ML)-based pickers have achieved remarkable accuracy and efficiency with generalization, and thus can significantly improve the earthquake location accuracy of previously developed sequential location methods. However, the inconsistent input or output (I/O) formats between multiple packages often limit their cross application. To reduce format barriers, we incorporated a widely used ML phase picker—PhaseNet—with several popular earthquake location methods and developed a “hands-free” end-to-end ML-based location workflow (named LOC-FLOW), which can be applied directly to continuous waveforms and build high-precision earthquake catalogs at local and regional scales. The renovated open-source package assembles several sequential algorithms including seismic first-arrival picking (PhaseNet and STA/LTA), phase association (REAL), absolute location (VELEST and HYPOINVERSE), and double-difference relative location (hypoDD and GrowClust). We provided different location strategies and I/O interfaces for format conversion to form a seamless earthquake location workflow. Different algorithms can be flexibly selected and/or combined. As an example, we apply LOC-FLOW to the 28 September 2004 M 6.0 Parkfield, California, earthquake sequence. LOC-FLOW accomplished seismic phase picking, association, velocity model updating, station correction, absolute location, and double-difference relocation for 16-day continuous seismic data. We detected and located 3.7 times (i.e., 4357) as many as earthquakes with cross-correlation double-difference locations from the Northern California Earthquake Data Center. Our study demonstrates that LOC-FLOW is capable of building high-precision earthquake catalogs efficiently and seamlessly from continuous seismic data. |
URL | [Source Record] |
Indexed By | |
Language | English
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SUSTech Authorship | Others
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Funding Project | Natural Sciences and Engineering Research Council of Canada[RGPIN-2019-04297]
; Ocean Frontier Institute Seed Fund[ALLRP-559829-20]
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WOS Research Area | Geochemistry & Geophysics
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WOS Subject | Geochemistry & Geophysics
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WOS Accession No | WOS:001052385300004
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Publisher | |
ESI Research Field | GEOSCIENCES
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Data Source | Web of Science
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Citation statistics |
Cited Times [WOS]:25
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Document Type | Journal Article |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/406215 |
Department | Department of Earth and Space Sciences |
Affiliation | 1.Department of Earth and Environmental Sciences,Dalhousie University,Halifax,Canada 2.Institute of Geophysics,China Earthquake Administration,Beijing,China 3.Department of Earth and Space Sciences,Southern University of Science and Technology,Shenzhen,Guangdong,China 4.Seismological Laboratory,Division of Geological and Planetary Sciences,California Institute of Technology,Pasadena,United States |
Recommended Citation GB/T 7714 |
Zhang,Miao,Liu,Min,Feng,Tian,et al. LOC-FLOW: An End-to-End Machine Learning-Based High-Precision Earthquake Location Workflow[J]. SEISMOLOGICAL RESEARCH LETTERS,2022,93(5):2426-2438.
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APA |
Zhang,Miao,Liu,Min,Feng,Tian,Wang,Ruijia,&Zhu,Weiqiang.(2022).LOC-FLOW: An End-to-End Machine Learning-Based High-Precision Earthquake Location Workflow.SEISMOLOGICAL RESEARCH LETTERS,93(5),2426-2438.
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MLA |
Zhang,Miao,et al."LOC-FLOW: An End-to-End Machine Learning-Based High-Precision Earthquake Location Workflow".SEISMOLOGICAL RESEARCH LETTERS 93.5(2022):2426-2438.
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Files in This Item: | ||||||
File Name/Size | DocType | Version | Access | License | ||
Zhang2022srl.pdf(5344KB) | Restricted Access | -- |
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