Real-Time Discrimination Model for Local Earthquake Intensity Threshold Based on XGBoost

被引：0
作者：
Li S. [1 ,2 ]
Chen X. [1 ,2 ]
Lu J. [1 ,2 ]
Ma Q. [1 ,2 ]
Xie Z. [1 ,2 ]
Tao D. [1 ,2 ]
Li W. [1 ,2 ]
机构：
[1] Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin
[2] Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin
来源：
Diqiu Kexue - Zhongguo Dizhi Daxue Xuebao/Earth Science - Journal of China University of Geosciences | 2024年 / 49卷 / 02期
关键词：
earthquake; machine learning; onsite warning; SHAP; XGBoost;
D O I：
10.3799/dqkx.2023.159
中图分类号：
学科分类号：
摘要：
A key challenge in earthquake early warning (EEW) research is to predict whether the final intensity at a station during an earthquake will exceed 6 degrees using only a small amount of P‐wave information received by the station. In this paper, we propose a real‐time intensity threshold discrimination model based on Extreme Gradient Boosting Tree (XGBoost). The model uses five features calculated from the information within 3 seconds after receiving P‐waves as input features, and uses the threshold of whether the final instrumental seismic intensity at the station will exceed 6 degrees. A total of 4 353 acceleration records from 460 earthquakes recorded by the Japanese K‐NET seismic network from 1996 to 2022 were used to establish the XGBoost‐based real‐time intensity threshold discrimination model (XGBoost‐ITD). The results indicate that the model′s discrimination accuracy rate is 93% for low intensity and 88% for high intensity. Compared with the support vector machine classification method and the traditional method under the same dataset, the XGBoost method shows higher discrimination accuracy. © 2024 China University of Geosciences. All rights reserved.
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页码：379 / 390
页数：11
相关论文
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