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.
Data‐driven two‐fault modelling of the Mw 6.0 2008 Wells, Nevada earthquake suggests a listric fault rupture