Bridging Supervised and Unsupervised Learning to Build Volcano Seismicity Classifiers at Kilauea Volcano, Hawaii

DOI number:10.1785/0220230251

Affiliation of Author(s):中国科学技术大学地球和空间科学学院

Journal:Seismological Research Letters

Place of Publication:美国

Abstract: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

First Author:Xin Cui

Co-author:Yanlan Hu, Shang Ma,Zefeng Li, Guoming Liu, Hui Huang

Indexed by:Journal paper

Correspondence Author:Zefeng Li

Document Code:29

Discipline:Natural Science

First-Level Discipline:Geophysics

Document Type:SCI

Volume:95

Issue:3

Page Number:1849-1857

ISSN No.:0895-0695

Translation or Not:no

Date of Publication:2024-01-02

Links to published journals:https://doi.org/10.1785/0220230251