Machine learning and earthquake forecasting-next steps

被引：77

作者：

Beroza, Gregory C. ^{[1
]}

Segou, Margarita ^{[2
]}

Mostafa Mousavi, S. ^{[1
]}

机构：

[1] Stanford Univ, Dept Geophys, Stanford, CA 94305 USA

[2] British Geol Survey, Lyell Ctr, Res Ave South, Edinburgh, Midlothian, Scotland

来源：

NATURE COMMUNICATIONS | 2021年 / 12卷 / 01期

关键词：

D O I：

10.1038/s41467-021-24952-6

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：3

共 16 条

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