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
Date
2022-06-06
Authors
Chen, Han
Yang, Hongfeng
Zhu, Gaohua
Xu, Min
Lin, Jian
You, Qingyu
Yang, Hongfeng
Zhu, Gaohua
Xu, Min
Lin, Jian
You, Qingyu
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Person
Person
Person
Person
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DOI
10.1029/2022GL097779
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Keywords
Outer-rise fault
Mariana Subduction Zone
EQTransformer
Ocean bottom seismometer
Mariana Subduction Zone
EQTransformer
Ocean bottom seismometer
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.
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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.
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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.