Deep Outer-Rise Faults in the Southern Mariana Subduction Zone Indicated by a Machine-Learning-Based High-Resolution Earthquake Catalog

Chen，Han1; Yang，Hongfeng1,2; Zhu，Gaohua1,3,4; Xu，Min5,6; Lin，Jian5,7,8; You，Qingyu9

2022-06-28

10.1029/2022GL097779

GEOPHYSICAL RESEARCH LETTERS   Impact Factor & Quartile

0094-8276

1944-8007

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.

EQTransformer Mariana Subduction Zone ocean bottom seismometer outer-rise fault

SCI ; EI

English

NI Journal Papers

Others

National Natural Science Foundation of China[41890813];National Natural Science Foundation of China[91858207];National Natural Science Foundation of China[92158205];Chinese Academy of Sciences[QYZDY-SSW-DQC005];Chinese Academy of Sciences[Y4SL021001];

Geology

Geosciences, Multidisciplinary

WOS:000813617100001

AMER GEOPHYSICAL UNION

20222712305256

Machine learning ; Oceanography ; Plates (structural components) ; Seismographs ; Tomography

Structural Members and Shapes:408.2 ; Oceanography, General:471.1 ; Seismology:484 ; Earthquake Measurements and Analysis:484.1 ; Artificial Intelligence:723.4 ; Imaging Techniques:746 ; Special Purpose Instruments:943.3

GEOSCIENCES

2-s2.0-85133064942

Scopus

1.Earth System Science Programme,Faculty of Science,The Chinese University of Hong Kong,Hong Kong
2.Shenzhen Research Institute,The Chinese University of Hong Kong,Shenzhen,China
3.CAS Key Laboratory of Marine Geology and Environment,Center for Ocean Mega-Science,Institute of Oceanology,Chinese Academy of Sciences,Qingdao,China
4.Laboratory for Marine Geology,Qingdao National Laboratory for Marine Science and Technology,Qingdao,China
5.Key Laboratory of Marginal Sea Geology,Chinese Academy of Sciences,South China Sea Institute of Oceanology,Guangzhou,China
6.Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou),Guangzhou,China
7.Department of Ocean Science and Engineering,Southern University of Science and Technology,Shenzhen,China
8.Department of Geology and Geophysics,Woods Hole Oceanographic Institution,Falmouth,United States
9.Key Laboratory of Petroleum Resources Research,Institute of Geology and Geophysics,Chinese Academy of Sciences,Beijing,China

Chen，Han,Yang，Hongfeng,Zhu，Gaohua,et al. Deep Outer-Rise Faults in the Southern Mariana Subduction Zone Indicated by a Machine-Learning-Based High-Resolution Earthquake Catalog[J]. GEOPHYSICAL RESEARCH LETTERS,2022,49(12).

Chen，Han,Yang，Hongfeng,Zhu，Gaohua,Xu，Min,Lin，Jian,&You，Qingyu.(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).

Chen，Han,et al."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(2022).