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

Yunyue elita Li, Daniel O’malley, Greg Beroza, Andrew Curtis, Paul Johnson

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

After 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.
Original languageEnglish
Article numbere2022JB026310
JournalJournal of Geophysical Research: Solid Earth
Volume128
Issue number1
DOIs
Publication statusPublished - 5 Jan 2023

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