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Title: Machine learning for data-driven discovery in solid Earth geoscience

Abstract

Understanding the behavior of Earth through the diverse fields of the solid Earth geosciences is an increasingly important task. It is made challenging by the complex, interacting, and multiscale processes needed to understand Earth’s behavior and by the inaccessibility of nearly all of Earth’s subsurface to direct observation. Substantial increases in data availability and in the increasingly realistic character of computer simulations hold promise for accelerating progress, but developing a deeper understanding based on these capabilities is itself challenging. Machine learning will play a key role in this effort. We review the state of the field and make recommendations for how progress might be broadened and accelerated.

Authors:
ORCiD logo; ORCiD logo; ORCiD logo;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1547596
Grant/Contract Number:  
KC030206
Resource Type:
Published Article
Journal Name:
Science
Additional Journal Information:
Journal Name: Science Journal Volume: 363 Journal Issue: 6433; Journal ID: ISSN 0036-8075
Publisher:
American Association for the Advancement of Science (AAAS)
Country of Publication:
United States
Language:
English

Citation Formats

Bergen, Karianne J., Johnson, Paul A., de Hoop, Maarten V., and Beroza, Gregory C. Machine learning for data-driven discovery in solid Earth geoscience. United States: N. p., 2019. Web. doi:10.1126/science.aau0323.
Bergen, Karianne J., Johnson, Paul A., de Hoop, Maarten V., & Beroza, Gregory C. Machine learning for data-driven discovery in solid Earth geoscience. United States. doi:10.1126/science.aau0323.
Bergen, Karianne J., Johnson, Paul A., de Hoop, Maarten V., and Beroza, Gregory C. Thu . "Machine learning for data-driven discovery in solid Earth geoscience". United States. doi:10.1126/science.aau0323.
@article{osti_1547596,
title = {Machine learning for data-driven discovery in solid Earth geoscience},
author = {Bergen, Karianne J. and Johnson, Paul A. and de Hoop, Maarten V. and Beroza, Gregory C.},
abstractNote = {Understanding the behavior of Earth through the diverse fields of the solid Earth geosciences is an increasingly important task. It is made challenging by the complex, interacting, and multiscale processes needed to understand Earth’s behavior and by the inaccessibility of nearly all of Earth’s subsurface to direct observation. Substantial increases in data availability and in the increasingly realistic character of computer simulations hold promise for accelerating progress, but developing a deeper understanding based on these capabilities is itself challenging. Machine learning will play a key role in this effort. We review the state of the field and make recommendations for how progress might be broadened and accelerated.},
doi = {10.1126/science.aau0323},
journal = {Science},
number = 6433,
volume = 363,
place = {United States},
year = {2019},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
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DOI: 10.1126/science.aau0323

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Cited by: 8 works
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