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Predicting fault slip via transfer learning

Authors :
Kun Wang
Christopher W. Johnson
Kane C. Bennett
Paul A. Johnson
Source :
Nature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
Publication Year :
2021
Publisher :
Nature Portfolio, 2021.

Abstract

By teaching machine learning models with earthquake fault numerical simulations laboratory fault slip is predictable. Training the model further with a snippet of laboratory data improves predictions suggesting an approach to probing faults in Earth.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
12
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.b60a515c71564fb28dfe6f185c137e67
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-021-27553-5