Machine learning for data-driven discovery in solid Earth geoscience-Reference-Cited by-同舟云学术

Machine learning for data-driven discovery in solid Earth geoscience

Published:2019-03-22 Issue:6433 Volume:363 Page:
ISSN:0036-8075
Container-title:Science
language:en
Short-container-title:Science

Author:

Bergen Karianne J.¹²^ORCID,Johnson Paul A.³^ORCID,de Hoop Maarten V.⁴^ORCID,Beroza Gregory C.⁵

Affiliation:

1. Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA.

2. Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, USA.

3. Geophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

4. Department of Computational and Applied Mathematics, Rice University, Houston, TX 77005, USA.

5. Department of Geophysics, Stanford University, Stanford, CA 94305, USA.

Abstract

Automating geoscience analysis Solid Earth geoscience is a field that has very large set of observations, which are ideal for analysis with machine-learning methods. Bergen et al. review how these methods can be applied to solid Earth datasets. Adopting machine-learning techniques is important for extracting information and for understanding the increasing amount of complex data collected in the geosciences. Science , this issue p. eaau0323

Funder

National Science Foundation

U.S. Department of Energy

Simons Foundation

Los Alamos National Laboratory

Geo-Mathematical Imaging Group

Harvard Data Science Initiative

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference146 articles.