  Simulating dynamic rupture propagation is challenging due to the
uncertainties involved in the underlying physics of fault slip, stress
conditions, and frictional properties of the fault. A trial and error approach
is often used to determine the unknown parameters describing rupture, but
running many simulations usually requires human review to determine how to
adjust parameter values and is thus not very efficient. To reduce the
computational cost and improve our ability to determine reasonable stress and
friction parameters, we take advantage of the machine learning approach. We
develop two models for earthquake rupture propagation using the artificial
neural network (ANN) and the random forest (RF) algorithms to predict if a
rupture can break a geometric heterogeneity on a fault. We train the models
using a database of 1600 dynamic rupture simulations computed numerically.
Fault geometry, stress conditions, and friction parameters vary in each
simulation. We cross-validate and test the predictive power of the models using
an additional 400 simulated ruptures, respectively. Both RF and ANN models
predict rupture propagation with more than 81% accuracy, and model parameters
can be used to infer the underlying factors most important for rupture
propagation. Both of the models are computationally efficient such that the 400
testings require a fraction of a second, leading to potential applications of
dynamic rupture that have previously not been possible due to the computational
demands of physics-based rupture simulations.
