Enabling large-scale viscoelastic calculations via neural network acceleration

TL;DR

This paper introduces a neural network-based method that accelerates large-scale viscoelastic earthquake cycle simulations by over 50,000%, enabling more comprehensive and detailed modeling of complex fault systems over extended periods.

Contribution

The study presents a novel neural network approach that significantly speeds up viscoelastic calculations, allowing for large-scale, high-resolution earthquake cycle modeling across diverse parameters.

Findings

Neural network accelerates viscoelastic calculations by over 50,000%.

Enables modeling of complex faults over thousands of earthquake cycles.

Supports wider ranges of model parameters with high spatial and temporal resolution.

Abstract

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.