This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/bs.agph.2020.08.003. This is version 1 of this Preprint.
Machine learning and fault rupture: a review
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Authors
Christopher Ren,
Claudia Hulbert,
Paul A. Johnson,
Bertrand Rouet‐Leduc 
Abstract
Geophysics has historically been a data-driven field, however in recent years the exponential increase of available data has lead to increased adoption of machine learning techniques and algorithm for analysis, detection and forecasting applications to faulting. This work reviews recent advances in the application of machine learning in the study of fault rupture ranging from the laboratory to Solid Earth.
DOI
https://doi.org/10.31223/osf.io/xdmkq
Subjects
Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geophysics and Seismology, Mathematics, Physical Sciences and Mathematics, Theory and Algorithms
Keywords
machine learning, Seismology, Geophysics, geoscience, InSAR, Earthquake Detection, Fault Rupture
Dates
Published: 2020-07-01 22:22
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