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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
PDF(3429 KB)
PDF(3429 KB)
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
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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4 353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 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.
现地预警 / XGBoost / SHAP / 机器学习 / 天然地震
onsite warning / XGBoost / SHAP / machine learning / earthquake
P315.3 / P315.7 / P315.9
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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（2018B02）；国家重点研发计划项目（2018YFC1504004）；黑龙江省自然科学基金优秀青年基金（YQ2020E005）；国家自然科学基金（U2039209）. 使用开源平台Anaconda、Python来设计模型. 日本防灾科学技术研究所（NIED）的K-NET为本研究提供数据支持. 中国地震局工程力学研究所强震动观测中心为本研究提供数据支持.
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