基于XGBoost的现地地震烈度阈值实时判别模型  

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

作　　者：李山有[1,2] 陈欣 卢建旗 马强[1,2] 谢志南 陶冬旺[1,2] 李伟 Li Shanyou;Chen Xin;Lu Jianqi;Ma Qiang;Xie Zhinan;Tao Dongwang;Li Wei(Key Laboratory of Earthquake Engineering and Engineering Vibration,Institute of Engineering Mechanics,China Earthquake Administration,Harbin 150080,China;Key Laboratory of Earthquake Disaster Mitigation,Ministry of Emergency Management,Harbin 150080,China)

机构地区：[1]中国地震局工程力学研究所地震工程与工程振动重点实验室,黑龙江哈尔滨150080 [2]地震灾害防治应急管理部重点实验室,黑龙江哈尔滨150080

出　　处：《地球科学》2024年第2期379-390,共12页Earth Science

基　　金：中国地震局工程力学研究所基本科研业务费专项资助项目(No.2018B02);国家重点研发计划项目(No.2018YFC1504004);黑龙江省自然科学基金优秀青年基金(No.YQ2020E005);国家自然科学基金(No.U2039209)。

摘　　要：如何在地震中利用台站接收到的少量P波信息预测该台站处的最终烈度是否会超越6度是地震预警研究中亟待解决的关键问题.提出了一种基于极限梯度提升树(XGBoost)的现地烈度阈值实时判别模型,该模型以由台站接收到P波后3秒内的信息计算的5种特征作为输入参数,以该台站处的最终仪器地震烈度是否会超越6度作为阈值.选取1996—2022年日本K-NET台网记录的460次地震的4353条加速度记录建立了基于P波前3秒信息的烈度阈值实时判别模型(XGBoost-ITD).结果表明,该模型对低烈度的判别准确率为93%,对高烈度的判别准确率为88%.在相同数据集条件下,相较于支持向量机分类方法及传统方法,XGBoost方法对现地烈度阈值判别具有更高的精度.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 4353 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.

关 键 词：现地预警 XGBoost SHAP 机器学习 天然地震 

分 类 号：P315.3[天文地球—地震学] P315.7[天文地球—固体地球物理学] P315.9[天文地球—地球物理学]