DOI码：

10.1785/0220230251

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：

Seismological Research Letters

刊物所在地：

美国

摘要：

Real-time classification of volcano seismicity could become a useful component in volcanic monitoring. Supervised learning provides a powerful means to achieve this but often requires a large amount of manually labeled data. Here, we build supervised learning models to discriminate volcano tectonic events (VTs), long-period events (LPs), and hybrid events in Kilauea by training with pseudolabels from unsupervised clustering. We test three different supervised models, and all of them achieve >93% accuracy. We apply the model ensemble to the six-day seismicity during the eruption in 2018 and

第一作者：

Xin Cui

合写作者：

Yanlan Hu, Shang Ma,李泽峰, Guoming Liu, Hui Huang

论文类型：

期刊论文

通讯作者：

李泽峰

论文编号：

29

学科门类：

理学

一级学科：

地球物理学

文献类型：

SCI

卷号：

95

期号：

3

页面范围：

1849-1857

ISSN号：

0895-0695

是否译文：

否

发表时间：

2024-01-02

发布期刊链接：

https://doi.org/10.1785/0220230251