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- Title
Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 M<sub>W</sub> 6.9 Chihshang earthquake sequence.
- Authors
Sun, Wei-Fang; Pan, Sheng-Yan; Huang, Chun-Ming; Guan, Zhuo-Kang; Yen, I-Chin; Ho, Chun-Wei; Chi, Tsung-Chih; Ku, Chin-Shang; Huang, Bor-Shouh; Fu, Ching-Chou; Kuo-Chen, Hao
- Abstract
On 18 September 2022, the MW 6.9 Chihshang earthquake struck the south half of the Longitudinal Valley, Taiwan, and caused severe damage. A precise and rapid report for the distribution of aftershock sequence after a devastating earthquake provides key information for deciphering the seismogenic structure in the source region. The utilization of deep-learning methodologies for earthquake event detection offers a significant acceleration in data analysis. In this study, we use SeisBlue, a deep-learning platform/package, to extract the whole earthquake sequence from September to October 2022, including the MW 6.5 Guanshan foreshock, the MW 6.9 mainshock, over 14,000 aftershocks, and 866 foal mechanisms from two sets of broadband networks. After applying hypoDD for earthquakes, the distribution of aftershock sequence clearly depicts not only the Central Range Fault and the Longitudinal Valley Fault but also several local, shallow tectonic structures that have not been observed along the southern Longitudinal Valley.
- Subjects
TAIWAN; EARTHQUAKES; EARTHQUAKE aftershocks; DEEP learning; CATALOGS; CATALOGING; FOALS; DATA analysis
- Publication
Terrestrial, Atmospheric & Oceanic Sciences, 2024, Vol 35, Issue 1, p1
- ISSN
1017-0839
- Publication type
Article
- DOI
10.1007/s44195-024-00063-9