  One of the most significant challenges involved in efforts to understand the
effects of repeated earthquake cycle activity are the computational costs of
large-scale viscoelastic earthquake cycle models. Computationally intensive
viscoelastic codes must be evaluated thousands of times and locations, and as a
result, studies tend to adopt a few fixed rheological structures and model
geometries, and examine the predicted time-dependent deformation over short
(<10 yr) time periods at a given depth after a large earthquake. Training a
deep neural network to learn a computationally efficient representation of
viscoelastic solutions, at any time, location, and for a large range of
rheological structures, allows these calculations to be done quickly and
reliably, with high spatial and temporal resolution. We demonstrate that this
machine learning approach accelerates viscoelastic calculations by more than
50,000%. This magnitude of acceleration will enable the modeling of
geometrically complex faults over thousands of earthquake cycles across wider
ranges of model parameters and at larger spatial and temporal scales than have
been previously possible.
