Back to Search
Start Over
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.
- Source :
-
Geophysical Journal International . Jul2024, Vol. 238 Issue 1, p91-108. 18p. - Publication Year :
- 2024
-
Abstract
- 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]
Details
- Language :
- English
- ISSN :
- 0956540X
- Volume :
- 238
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Geophysical Journal International
- Publication Type :
- Academic Journal
- Accession number :
- 177745550
- Full Text :
- https://doi.org/10.1093/gji/ggae140