Predicting Off‐Fault Deformation From Experimental Strike‐Slip Fault Images Using Convolutional Neural Networks

Abstract Crustal deformation occurs both as localized slip along faults and distributed deformation off of faults. While there are few robust estimates of off‐fault deformation in nature, scaled physical experiments simulating crustal strike‐slip faulting allow direct measurement of the ratio of fault slip to regional deformation, quantified as kinematic efficiency (KE). We offer an approach to predict KE using a 2D convolutional neural network (CNN) trained directly on fault maps produced by physical experiments. Experiments with different loading rates and basal boundary conditions generate the fault maps throughout the evolution of strike‐slip faults. Strain maps allow us to directly calculate KE and its uncertainty, utilized in the loss function and performance metric. The trained CNN achieves 91% custom accuracy in the KE prediction of an unseen data set. Although the CNN model is trained on scaled experiments, it can predict off‐fault deformation of crustal faults that matches available geologic estimates.