A Hybrid Deep Learning Model for Rapid Probabilistic Earthquake Source Parameter Estimation With Displacement Waveforms From a Flexible Set of Seismic or HR-GNSS Stations
Author:
Affiliation:
1. School of Geodesy and Geomatics, Wuhan University, Wuhan, China
2. College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/10006360/10329981.pdf?arnumber=10329981
Reference62 articles.
1. Advancement in source estimation techniques using broadband regional seismograms
2. A High-Resolution Seismic Catalog for the Initial 2019 Ridgecrest Earthquake Sequence: Foreshocks, Aftershocks, and Faulting Complexity
3. Determination of earthquake source parameters from waveform data for studies of global and regional seismicity
4. Spatio-temporal foreshock evolution of the 2019 M 6.4 and M 7.1 Ridgecrest, California earthquakes
5. Machine learning for data-driven discovery in solid Earth geoscience
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