Machine learning in seismology: Turning data into insights

TitleMachine learning in seismology: Turning data into insights
Publication TypeJournal Article
Year of Publication2019
AuthorsKong Q.K, Trugman D.T, Ross Z.E, Bianco M.J, Meade B.J, Gerstoft P
JournalSeismological Research Letters
Volume90
Pagination3-14
Date Published2019/01
Type of ArticleArticle
ISBN Number0895-0695
Accession NumberWOS:000460123500001
Keywordsalgorithm; array; discrimination; driven; earthquakes; Geochemistry & Geophysics; models; neural-network approach; picking; prediction; wave-form inversion
Abstract

This article provides an overview of current applications of machine learning (ML) in seismology. ML techniques are becoming increasingly widespread in seismology, with applications ranging from identifying unseen signals and patterns to extracting features that might improve our physical understanding. The survey of the applications in seismology presented here serves as a catalyst for further use of ML. Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning (EEW), ground-motion prediction, seismic tomography, and earthquake geodesy. We conclude by discussing the need for a hybrid approach combining data-driven ML with traditional physical modeling.

DOI10.1785/0220180259
Student Publication: 
No