This preprint has been submitted as an invited review for peer review to Advances in Geophysics, as yet this paper has not undergone peer-review. Chapter 1 Machine learning and fault rupture: a review Christopher X. Ren1∗, Claudia Hulbert2∗, Paul A. Johnson3, Bertrand Rouet-Leduc3∗∗ 1Intelligence and Space Research Division, Los Alamos National Laboratory, Los Alamos, NM, USA 2Laboratoire de Géologie, Département de Géosciences, École Normale Supérieure, Paris, France 3Geophysics Group, Los Alamos National Laboratory, Los Alamos, NM, USA ∗ These two authors contributed equally. ∗∗ Corresponding author email:
[email protected]. ABSTRACT Geophysics has historically been a data-driven field, however in recent years the ex- ponential increase of available data has led 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. KEYWORDS Machine Learning, Faulting, Earthquakes 1.1 INTRODUCTION The study of material failure and rupture in geophysics is an extremely broad field [1], involving observation and analysis of geophysical data from failure simulations at laboratory and field scale [2, 3, 4, 5, 6, 7, 8, 9], laboratory experiments [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26? ] and in the solid Earth [1, 27, 28, 29, 30, 31, 32, 33, 34, 35]. Despite the vast apparent scope of this field, one approach to distill its essence is based on the following question: given a seismic signal received by a sensor or a set of sensors, what information can be gleaned concerning the process generating the signal? Often these signals are noisy, numerous, and exist in high-dimensional spaces where it is non-trivial to extract meaningful information from them [36, 37].