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DOI10.1126/science.aau0323
Machine learning for data-driven discovery in solid Earth geoscience
Bergen K.J.; Johnson P.A.; De Hoop M.V.; Beroza G.C.
发表日期2019
ISSN0036-8075
卷号363期号:6433
英文摘要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. 2017 © The Authors.
英文关键词computer simulation; data assimilation; machine learning; solid Earth; computer simulation; machine learning; review
语种英语
来源期刊Science
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/243027
作者单位Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, United States; Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, United States; Geophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, United States; Department of Computational and Applied Mathematics, Rice University, Houston, TX 77005, United States; Department of Geophysics, Stanford University, Stanford, CA 94305, United States
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Bergen K.J.,Johnson P.A.,De Hoop M.V.,et al. Machine learning for data-driven discovery in solid Earth geoscience[J],2019,363(6433).
APA Bergen K.J.,Johnson P.A.,De Hoop M.V.,&Beroza G.C..(2019).Machine learning for data-driven discovery in solid Earth geoscience.Science,363(6433).
MLA Bergen K.J.,et al."Machine learning for data-driven discovery in solid Earth geoscience".Science 363.6433(2019).
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