Simultaneous magnitude and slip distribution characterization from high-rate GNSS using deep learning: case studies of the 2021 Mw 7.4 Maduo and 2023 Turkey doublet events

Cui，Wenfeng1; Chen，Kejie1,2; Wei，Guoguang1,3; Lyu，Mingzhe1,4; Zhu，Feng1

2024-07-01

10.1093/gji/ggae140

Geophysical Journal International   影响因子和分区

0956-540X

1365-246X

Rapid and accurate characterization of earthquake sources is crucial for mitigating seismic hazards. In this study, based on 18 000 scenario ruptures ranging from M 6.4 to M 8.3 and corresponding synthetic high-rate Global Navigation Satellite System (HR-GNSS) waveforms, we developed a multibranch neural network framework, the continental large earthquake agile response (CLEAR), to simultaneously determine the magnitude and slip distributions. We apply CLEAR to recent large strike-slip events, including the 2021 M 7.4 Maduo earthquake and the 2023 M 7.8 and M 7.6 Turkey doublet. The model generally estimates the magnitudes successfully at 32 s with errors of less than 0.15, and predicts the slip distributions acceptably at 64 s, requiring only approximately 30 ms on a single CPU (Central Processing Unit). With optimal azimuthal coverage of stations, the system is relatively robust to the number of stations and the time length of the received data.

Deeping Learning HR-GNSS Large Earthquake

SCI ; EI

英语

第一 ; 通讯

20242016098694

Deep learning ; Earthquakes ; Global positioning system

Ergonomics and Human Factors Engineering:461.4 ; Seismology:484

GEOSCIENCES

2-s2.0-85192939922

Scopus

1.Department of Earth and Space Sciences,Southern University of Science and Technology,Shenzhen,518055,China
2.Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology,Southern University of Science and Technology,Shenzhen,518055,China
3.Institute of Geophysics,Department of Earth Sciences,Eth Zürich,Zürich,8092,Switzerland
4.Asian School of the Environment,Nanyang Technological University,Singapore,639798,Singapore

Cui，Wenfeng,Chen，Kejie,Wei，Guoguang,et al. Simultaneous magnitude and slip distribution characterization from high-rate GNSS using deep learning: case studies of the 2021 Mw 7.4 Maduo and 2023 Turkey doublet events[J]. Geophysical Journal International,2024,238(1):91-108.

Cui，Wenfeng,Chen，Kejie,Wei，Guoguang,Lyu，Mingzhe,&Zhu，Feng.(2024).Simultaneous magnitude and slip distribution characterization from high-rate GNSS using deep learning: case studies of the 2021 Mw 7.4 Maduo and 2023 Turkey doublet events.Geophysical Journal International,238(1),91-108.

Cui，Wenfeng,et al."Simultaneous magnitude and slip distribution characterization from high-rate GNSS using deep learning: case studies of the 2021 Mw 7.4 Maduo and 2023 Turkey doublet events".Geophysical Journal International 238.1(2024):91-108.