Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019GL086189 |
Rapid Characterization of the July 2019 Ridgecrest, California, Earthquake Sequence From Raw Seismic Data Using Machine-Learning Phase Picker | |
Liu, Min1,2; Zhang, Miao2; Zhu, Weiqiang3; Ellsworth, William L.3; Li, Hongyi1 | |
2020-02-28 | |
发表期刊 | GEOPHYSICAL RESEARCH LETTERS |
ISSN | 0094-8276 |
EISSN | 1944-8007 |
出版年 | 2020 |
卷号 | 47期号:4 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; Canada; USA |
英文摘要 | The two principle earthquakes of the July 2019 Ridgecrest, California, earthquake sequence, M-W 6.4 and 7.1, and their immediate foreshocks and thousands of aftershocks present a challenging environment for rapid analysis and characterization of this sequence as it unfolded. In this study, we analyze the first 6 days of the sequence using continuous data from available seismic networks to detect and locate earthquakes associated with the earthquake sequence. We build a high-precision earthquake catalog using a deep-neural-network-based picker-PhaseNet and a sequential earthquake association and location workflow. Without prior information, we automatically detect and locate more than twice as many earthquakes as the routine catalog. Our high-precision earthquake catalog reveals detailed spatiotemporal evolution of the earthquake sequence and clearly defines multiple faults activated during the sequence. Our study demonstrates that it is possible to characterize earthquake sequences from raw seismic data using a well-trained machine-learning picker and our workflow. Plain Language Summary We build a high-precision earthquake catalog for the July 2019 Ridgecrest, California, earthquake sequence from 4 July 2019 to 9 July 2019 without prior information using machine-learning phase picks and a sequential earthquake association and location workflow. Our result is totally independent of the routine catalog and enables us to characterize the earthquake sequence starting from raw seismic data. Our high-precision earthquake catalog reveals detailed spatiotemporal evolution of the earthquake sequence and clearly defines multiple faults activated during the sequence. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000529120100078 |
WOS关键词 | REAL-TIME DETECTION ; HAYWARD FAULT ; LOCATION ; ALGORITHM ; FRACTURE ; PICKING ; STRESS |
WOS类目 | Geosciences, Multidisciplinary |
WOS研究方向 | Geology |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/279656 |
专题 | 气候变化 |
作者单位 | 1.China Univ Geosci Beijing, Sch Geophys & Informat Technol, Beijing, Peoples R China; 2.Dalhousie Univ, Dept Earth & Environm Sci, Halifax, NS, Canada; 3.Stanford Univ, Dept Geophys, Stanford, CA 94305 USA |
推荐引用方式 GB/T 7714 | Liu, Min,Zhang, Miao,Zhu, Weiqiang,et al. Rapid Characterization of the July 2019 Ridgecrest, California, Earthquake Sequence From Raw Seismic Data Using Machine-Learning Phase Picker[J]. GEOPHYSICAL RESEARCH LETTERS,2020,47(4). |
APA | Liu, Min,Zhang, Miao,Zhu, Weiqiang,Ellsworth, William L.,&Li, Hongyi.(2020).Rapid Characterization of the July 2019 Ridgecrest, California, Earthquake Sequence From Raw Seismic Data Using Machine-Learning Phase Picker.GEOPHYSICAL RESEARCH LETTERS,47(4). |
MLA | Liu, Min,et al."Rapid Characterization of the July 2019 Ridgecrest, California, Earthquake Sequence From Raw Seismic Data Using Machine-Learning Phase Picker".GEOPHYSICAL RESEARCH LETTERS 47.4(2020). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论