基于XGBoost的现地地震烈度阈值实时判别模型

李山有; 陈欣; 卢建旗; 马强; 谢志南; 陶冬旺; 李伟

doi:10.3799/dqkx.2023.159

Volume 49 Issue 2

Feb. 2024

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Article Navigation > Earth Science > 2024 > 49(2): 379-390

Li Shanyou, Chen Xin, Lu Jianqi, Ma Qiang, Xie Zhinan, Tao Dongwang, Li Wei, 2024. Real-Time Discrimination Model for Local Earthquake Intensity Threshold Based on XGBoost. Earth Science, 49(2): 379-390. doi: 10.3799/dqkx.2023.159

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Li Shanyou, Chen Xin, Lu Jianqi, Ma Qiang, Xie Zhinan, Tao Dongwang, Li Wei, 2024. Real-Time Discrimination Model for Local Earthquake Intensity Threshold Based on XGBoost. Earth Science, 49(2): 379-390. doi: 10.3799/dqkx.2023.159

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

doi: 10.3799/dqkx.2023.159

Li Shanyou^{1, 2
,
,},
Chen Xin^{1, 2},
Lu Jianqi^{1, 2
,
,
,},
Ma Qiang^{1, 2},
Xie Zhinan^{1, 2},
Tao Dongwang^{1, 2},
Li Wei^{1, 2}

1.
Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
2.
Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China

Received Date: 2023-02-05
Publish Date: 2024-02-25

Abstract

Abstract

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.
- onsite warning,
- XGBoost,
- SHAP,
- machine learning,
- earthquake

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References(43)

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

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