Machine learning and fault rupture: a review

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Authors

Christopher Ren , Claudia Hulbert, Paul 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: 2020-07-02 02:22

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License

CC BY Attribution 4.0 International

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