Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequence
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 seismo...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-02-01
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| Series: | Terrestrial, Atmospheric and Oceanic Sciences |
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| Online Access: | https://doi.org/10.1007/s44195-024-00063-9 |
| _version_ | 1827327267740581888 |
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| author | Wei-Fang Sun Sheng-Yan Pan Chun-Ming Huang Zhuo-Kang Guan I-Chin Yen Chun-Wei Ho Tsung-Chih Chi Chin-Shang Ku Bor-Shouh Huang Ching-Chou Fu Hao Kuo-Chen |
| author_facet | Wei-Fang Sun Sheng-Yan Pan Chun-Ming Huang Zhuo-Kang Guan I-Chin Yen Chun-Wei Ho Tsung-Chih Chi Chin-Shang Ku Bor-Shouh Huang Ching-Chou Fu Hao Kuo-Chen |
| author_sort | Wei-Fang Sun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T14:57:43Z |
| format | Article |
| id | doaj.art-afd8351a16214813bdf7df90874546d7 |
| institution | Directory Open Access Journal |
| issn | 1017-0839 2311-7680 |
| language | English |
| last_indexed | 2024-03-07T14:57:43Z |
| publishDate | 2024-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Terrestrial, Atmospheric and Oceanic Sciences |
| spelling | doaj.art-afd8351a16214813bdf7df90874546d72024-03-05T19:20:38ZengSpringerTerrestrial, Atmospheric and Oceanic Sciences1017-08392311-76802024-02-0135111610.1007/s44195-024-00063-9Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequenceWei-Fang Sun0Sheng-Yan Pan1Chun-Ming Huang2Zhuo-Kang Guan3I-Chin Yen4Chun-Wei Ho5Tsung-Chih Chi6Chin-Shang Ku7Bor-Shouh Huang8Ching-Chou Fu9Hao Kuo-Chen10Department of Geosciences, National Taiwan UniversityDepartment of Geosciences, National Taiwan UniversityDepartment of Geosciences, National Taiwan UniversityDepartment of Geosciences, National Taiwan UniversityYIC Geological OfficeSeismological Center, Central Weather AdministrationInstitute of Earth Sciences, Academia SinicaInstitute of Earth Sciences, Academia SinicaInstitute of Earth Sciences, Academia SinicaInstitute of Earth Sciences, Academia SinicaDepartment of Geosciences, National Taiwan UniversityAbstract 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.https://doi.org/10.1007/s44195-024-00063-92022 MW 6.9 Chihshang earthquake sequenceSeisBlueAI earthquake catalogSeismogenic structureLongitudinal Valley |
| spellingShingle | Wei-Fang Sun Sheng-Yan Pan Chun-Ming Huang Zhuo-Kang Guan I-Chin Yen Chun-Wei Ho Tsung-Chih Chi Chin-Shang Ku Bor-Shouh Huang Ching-Chou Fu Hao Kuo-Chen Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequence Terrestrial, Atmospheric and Oceanic Sciences 2022 MW 6.9 Chihshang earthquake sequence SeisBlue AI earthquake catalog Seismogenic structure Longitudinal Valley |
| title | Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequence |
| title_full | Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequence |
| title_fullStr | Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequence |
| title_full_unstemmed | Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequence |
| title_short | Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequence |
| title_sort | deep learning based earthquake catalog reveals the seismogenic structures of the 2022 mw 6 9 chihshang earthquake sequence |
| topic | 2022 MW 6.9 Chihshang earthquake sequence SeisBlue AI earthquake catalog Seismogenic structure Longitudinal Valley |
| url | https://doi.org/10.1007/s44195-024-00063-9 |
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