Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?. Issue 1 (18th January 2023)

• Record Type: Journal Article
• Authors: Li, Yunyue Elita; O'Malley, Daniel; Beroza, Greg; Curtis, Andrew; Johnson, Paul
• Is Part Of: Journal of geophysical research. Volume 128:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-18
• Subjects: Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1029/2022JB026310
• Languages: English
• ISSNs: 2169-9313

Abstract

Abstract: 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. Plain Language Summary: Machine learning has been the topic that attracts massive academic attention in Solid‐Earth Geosciences in the past decade. Applications of machine learning (ML), including the more conventional signal processing‐based methods and the trending deep neural network‐based methods, have dominated many scientific conversations. We introduce the special collection of ML applications in Solid‐Earth geosciences that range from earthquake signal processing, automatic image interpretation, to joint understanding of multiple geoscience datasets. With the extraordinary efforts in ML studies, we now have a better outline of the areas where ML has contributed most significantly through efficiency and automation, and where ML has the potential to revolutionize the workflow and advance the integrated scientific understanding of the Solid‐Earth processes. Key Points: Applications of machine learning (ML) in solid Earth geoscience have exploded in the past few years This special collection provides a snapshot of ML applications from data processing to inversion and interpretation Better integration of ML algorithms and scientific rigor is expected to further improve our understanding of the Earth