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:
-
[1];
[2];
[3];
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA., Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, USA.
- Geophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
- Department of Computational and Applied Mathematics, Rice University, Houston, TX 77005, USA.
- Department of Geophysics, Stanford University, Stanford, CA 94305, USA.
- 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. https://doi.org/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. https://doi.org/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 = {Thu Mar 21 00:00:00 EDT 2019},
month = {Thu Mar 21 00:00:00 EDT 2019}
}
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https://doi.org/10.1126/science.aau0323
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