| Autor: |
Cui, Wenfeng, Chen, Kejie, Wei, Guoguang, Lyu, Mingzhe, Zhu, Feng |
| Předmět: |
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| Zdroj: |
Geophysical Journal International; Jul2024, Vol. 238 Issue 1, p91-108, 18p |
| Abstrakt: |
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 w 6.4 to M w 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 w 7.4 Maduo earthquake and the 2023 M w 7.8 and M w 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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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