Machine Learning in Geoscience: Riding a Wave of Progress

Journal Article · 03 May 2019 · Eos, Transactions American Geophysical Union (Online)

DOI: https://doi.org/10.1029/2019EO122671 · OSTI ID:1544700

Trugman, Daniel Taylor  ^[1]; Beroza, Gregory C. ^[2]; Johnson, Paul Allan  ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2. Stanford Univ., CA (United States)

The geosciences are data rich, with petabytes of readily and publicly available data. This availability, combined with the complexity of unsolved problems in the field, has motivated vigorous interest in the application of machine learning (ML) techniques. ML offers a new “lens” for viewing data and scientific hypotheses that differs from the perspective of traditional domain expertise. Initial uses of ML have tended to be limited in scope and isolated in application, but recent efforts to promote benchmark geoscientific data sets and competitions promise to propel broader, deeper, and increasingly coordinated and collaborative efforts.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1544700
• Report Number(s): LA-UR-19-22852; EOSTAJ
• Journal Information: Eos, Transactions American Geophysical Union (Online), Vol. 100; ISSN 2324-9250
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English