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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Detalles Bibliográficos
Autores principales: Beroza, Gregory C., Segou, Margarita, Mostafa Mousavi, S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Comment
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8346575/
https://www.ncbi.nlm.nih.gov/pubmed/34362887
http://dx.doi.org/10.1038/s41467-021-24952-6
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author Beroza, Gregory C.
Segou, Margarita
Mostafa Mousavi, S.
author_facet Beroza, Gregory C.
Segou, Margarita
Mostafa Mousavi, S.
author_sort Beroza, Gregory C.
collection PubMed
description 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.
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spelling pubmed-83465752021-08-20 Machine learning and earthquake forecasting—next steps Beroza, Gregory C. Segou, Margarita Mostafa Mousavi, S. Nat Commun Comment 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. Nature Publishing Group UK 2021-08-06 /pmc/articles/PMC8346575/ /pubmed/34362887 http://dx.doi.org/10.1038/s41467-021-24952-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Comment
Beroza, Gregory C.
Segou, Margarita
Mostafa Mousavi, S.
Machine learning and earthquake forecasting—next steps
title Machine learning and earthquake forecasting—next steps
title_full Machine learning and earthquake forecasting—next steps
title_fullStr Machine learning and earthquake forecasting—next steps
title_full_unstemmed Machine learning and earthquake forecasting—next steps
title_short Machine learning and earthquake forecasting—next steps
title_sort machine learning and earthquake forecasting—next steps
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8346575/
https://www.ncbi.nlm.nih.gov/pubmed/34362887
http://dx.doi.org/10.1038/s41467-021-24952-6
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