Deep outer-rise faults in the Southern Mariana Subduction Zone indicated by a machine-learning-based high-resolution earthquake catalog
Deep outer-rise faults in the Southern Mariana Subduction Zone indicated by a machine-learning-based high-resolution earthquake catalog
dc.contributor.author | Chen, Han | |
dc.contributor.author | Yang, Hongfeng | |
dc.contributor.author | Zhu, Gaohua | |
dc.contributor.author | Xu, Min | |
dc.contributor.author | Lin, Jian | |
dc.contributor.author | You, Qingyu | |
dc.date.accessioned | 2022-10-07T17:00:34Z | |
dc.date.available | 2022-12-06T07:27:14Z | |
dc.date.issued | 2022-06-06 | |
dc.description | Author Posting. © American Geophysical Union, 2022. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 49(12), (2022): e2022GL097779, https://doi.org/10.1029/2022GL097779. | en_US |
dc.description.abstract | Outer-rise faults are predominantly concentrated near ocean trenches due to subducted plate bending. These faults play crucial roles in the hydration of subducted plates and the consequent subducting processes. However, it has not yet been possible to develop high-resolution structures of outer-rise faults due to the lack of near-field observations. In this study we deployed an ocean bottom seismometer (OBS) network near the Challenger Deep in the Southernmost Mariana Trench, between December 2016 and June 2017, covering both the overriding and subducting plates. We applied a machine-learning phase detector (EQTransformer) to the OBS data and found more than 1,975 earthquakes. An identified outer-rise event cluster revealed an outer-rise fault penetrating to depths of 50 km, which was inferred as a normal fault based on the extensional depth from tomographic images in the region, shedding new lights on water input at the southmost Mariana subduction zone. | en_US |
dc.description.embargo | 2022-12-06 | en_US |
dc.description.sponsorship | This study is supported by National Natural Science Foundation of China (Nos. 91858207, 92158205, 41890813), Hong Kong Research Grant Council Grants (No. 14304820), Award from CORE (a joint research center for ocean research between QNLM and HKUST), Chinese Academy of Sciences (Nos. Y4SL021001, QYZDY-SSW-DQC005), and Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (No. GML2019ZD0205), Faculty of Science at CUHK. | en_US |
dc.identifier.citation | Chen, H., Yang, H., Zhu, G., Xu, M., Lin, J., & You, Q. (2022). Deep outer-rise faults in the Southern Mariana Subduction Zone indicated by a machine-learning-based high-resolution earthquake catalog. Geophysical Research Letters, 49 (12), e2022GL097779. | en_US |
dc.identifier.doi | 10.1029/2022GL097779 | |
dc.identifier.uri | https://hdl.handle.net/1912/29395 | |
dc.publisher | American Geophysical Union | en_US |
dc.relation.uri | https://doi.org/10.1029/2022GL097779 | |
dc.subject | Outer-rise fault | en_US |
dc.subject | Mariana Subduction Zone | en_US |
dc.subject | EQTransformer | en_US |
dc.subject | Ocean bottom seismometer | en_US |
dc.title | Deep outer-rise faults in the Southern Mariana Subduction Zone indicated by a machine-learning-based high-resolution earthquake catalog | en_US |
dc.type | Article | en_US |
dspace.entity.type | Publication | |
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relation.isAuthorOfPublication.latestForDiscovery | ecd5194f-09d4-4a51-9831-22eaff32ac7c |
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