TY - JOUR
T1 - Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?
AU - Li, Yunyue elita
AU - O’malley, Daniel
AU - Beroza, Greg
AU - Curtis, Andrew
AU - Johnson, Paul
N1 - Funding Information:
We are grateful to the editors and editorial staff of American Geophysics Union, who carried out a significant amount of editorial tasks. We also thank all the reviewers and authors who provided their best expertise and work to build this collection.
Publisher Copyright:
© 2023. American Geophysical Union. All Rights Reserved.
PY - 2023/1/5
Y1 - 2023/1/5
N2 - 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.
AB - 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.
U2 - 10.1029/2022JB026310
DO - 10.1029/2022JB026310
M3 - Article
SN - 2169-9313
VL - 128
JO - Journal of Geophysical Research: Solid Earth
JF - Journal of Geophysical Research: Solid Earth
IS - 1
M1 - e2022JB026310
ER -