Machine learning and earthquake forecasting—next steps

A new generation of earthquake catalogs developed through supervised machine-learning illuminates earthquake activity with unprecedented detail. Application of unsupervised machine learning to analyze the more complete expression of seismicity in these catalogs may be the fastest route to improving...

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Bibliographic Details
Published in:	Nature communications Vol. 12; no. 1; p. 4761
Main Authors:	Beroza, Gregory C., Segou, Margarita, Mostafa Mousavi, S.
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 06-08-2021 Nature Publishing Group Nature Portfolio
Subjects:	704/2151/508 704/4111 Comment Earthquake prediction Earthquakes Forecasting Humanities and Social Sciences Learning algorithms Machine learning multidisciplinary Science Science (multidisciplinary) Seismic activity Seismicity
Online Access:	Get full text
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Summary:	A new generation of earthquake catalogs developed through supervised machine-learning illuminates earthquake activity with unprecedented detail. Application of unsupervised machine learning to analyze the more complete expression of seismicity in these catalogs may be the fastest route to improving earthquake forecasting.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 USDOE SC0020445
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-021-24952-6