Probing Seismogenic Faults with Machine Learning
Author:
Affiliation:
1. Los Alamos National Laboratory,Geophysics Group,Los Alamos,New Mexico,USA
Funder
U.S. Department of Energy
Office of Science
Basic Energy Sciences
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9897158/9897159/09897239.pdf?arnumber=9897239
Reference40 articles.
1. Acoustic emission and microslip precursors to stick-slip failure in sheared granular material
2. Continuous chatter of the Cascadia subduction zone revealed by machine learning
3. LABORATORY-DERIVED FRICTION LAWS AND THEIR APPLICATION TO SEISMIC FAULTING
4. Estimating Fault Friction From Seismic Signals in the Laboratory
5. Earthquake Catalog‐Based Machine Learning Identification of Laboratory Fault States and the Effects of Magnitude of Completeness
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