DOI码：

10.1038/s43247-023-01188-4

所属单位：

中国科学技术大学地球和空间科学学院

发表刊物：

Communications Earth & Environment

刊物所在地：

英国

摘要：

Earthquake monitoring is vital for understanding the physics of earthquakes and assessing seismic hazards. A standard monitoring workflow includes the interrelated and interdependent tasks of phase picking, association, and location. Although deep learning methods have been successfully applied to earthquake monitoring, they mostly address the tasks separately and ignore the geographic relationships among stations. Here, we propose a graph neural network that operates directly on multi-station seismic data and achieves simultaneous phase picking, association, and location. Particularly, the in

第一作者：

Xu Si

合写作者：

伍新明,李泽峰, Shenghou Wang, Jun Zhu

论文类型：

期刊论文

通讯作者：

伍新明,李泽峰

论文编号：

30

学科门类：

理学

一级学科：

地球物理学

文献类型：

SCI

卷号：

5

期号：

1

页面范围：

22

ISSN号：

2662-4435

是否译文：

否

发表时间：

2024-01-06

发布期刊链接：

https://doi.org/10.1038/s43247-023-01188-4