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Title: Enabling large-scale viscoelastic calculations via neural network acceleration

Journal Article · · Geophysical Research Letters
DOI:https://doi.org/10.1002/2017GL072716· OSTI ID:1465348
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Harvard Univ., Cambridge, MA (United States). Dept. of Earth and Planetary Sciences
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One of the most significant challenges involved in efforts to understand the effects of repeated earthquake cycle activity is the computational costs of large-scale viscoelastic earthquake cycle models. Computationally intensive viscoelastic codes must be evaluated at 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 years) 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 resolutions. We demonstrate that this machine learning approach accelerates viscoelastic calculations by more than 50,000%. Finally, 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.

Research Organization:
Krell Institute, Ames, IA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF); Southern California Earthquake Center; USGS
Grant/Contract Number:
FG02-97ER25308; EAR‐1033462; 6239; G12AC20038
OSTI ID:
1465348
Alternate ID(s):
OSTI ID: 1402388
Journal Information:
Geophysical Research Letters, Vol. 44, Issue 6; ISSN 0094-8276
Publisher:
American Geophysical UnionCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 26 works
Citation information provided by
Web of Science

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Machine Learning Approach to Characterize the Postseismic Deformation of the 2011 Tohoku‐Oki Earthquake Based on Recurrent Neural Network journal November 2019
A systematic review on deep learning architectures and applications journal March 2019
Machine learning for data-driven discovery in solid Earth geoscience journal March 2019
New Opportunities and Challenges of Geo-ICT Convergence Technology: GeoCPS and GeoAI journal August 2019

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