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Dr. Phoebe Robinson DeVries, a Postdoctoral Fellow at Harvard U., presents “Deep learning for aftershock location patterns and the earthquake cycle” at the MIT Earth Resources Laboratory.
“Over the past few years, deep learning has led to rapid advances in applied computer science, from machine vision to natural language processing. These methods are now accessible to scientists across all disciplines due to the availability of easy-to-use APIs and affordable GPU acceleration. We demonstrate two specific applications of deep learning within earthquake science. In the first, we train a deep neural network to learn computationally efficient representations of viscoelastic solutions, across large ranges of times, locations, and rheological structures. Once found, these efficient neural network representations may accelerate computationally intensive viscoelastic calculations by a factor of 500. In the second, we focus on aftershock location patterns and find that a fully connected neural network trained on 131,000+ mainshock-aftershock pairs can explain aftershock locations in an independent testing data set of 30,000+ mainshock aftershock pairs more accurately than static elastic Coulomb failure stress change. In contrast to the common assertion that deep learning produces “black box” results, the trained neural networks can provide some interesting physical insights.”