Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

Author:

Li Yunyue Elita1ORCID,O’Malley Daniel2ORCID,Beroza Greg3ORCID,Curtis Andrew4ORCID,Johnson Paul5ORCID

Affiliation:

1. Sustainability Geophysics Project Department of Earth, Atmospheric, and Planetary Sciences Purdue University West Lafayette IN USA

2. Computational Earth Science Group Los Alamos National Laboratory Los Alamos NM USA

3. Stanford Doerr School of Sustainability Stanford University Stanford CA USA

4. Institute of Earth and Planetary Sciences The University of Edinburgh Edinburgh UK

5. EES Division Los Alamos National Laboratory Los Alamos NM USA

Abstract

AbstractAfter decades of low but continuing activity, applications of machine learning (ML) in solid Earth geoscience have exploded in popularity. This special collection provides a snapshot of those applications, which range from data processing to inversion and interpretation, for which ML appears particularly well suited. Inevitably, there are variations in the degree to which these methods have been developed. We hope that the progress seen in some areas will inspire efforts in others. Challenges remain, including the formidable task of how geoscience can keep pace with developments in ML while ensuring the scientific rigor that our field depends on, but with improvements in sensor technology and accelerating rates of data accumulation, the methods of ML seem poised to play an important role for the foreseeable future.

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Geochemistry and Petrology,Geophysics

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