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基于XGBoost的现地PGV预测模型
卢建旗, 王雨佳, 李山有, 谢志南, 马强, 陶冬旺
PDF(3140 KB)
PDF(3140 KB)
基于XGBoost的现地PGV预测模型
An XGBoost-Based Onsite PGV Prediction Model
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地震动峰值速度（Peak Ground Velocity， PGV）是常用于衡量地震动对建筑结构破坏潜力的参数之一，实时预测PGV大小是重大工程地震紧急处置中的关键技术.为进一步提升PGV预测准确性，提出一种基于极限梯度提升树（Extream Gradient Boosting， XGBoost）的现地PGV预测模型.该模型以台站观测到的P波前3 s的峰值加速度（P a）、峰值速度（P v）、峰值位移（P d）、累计绝对速度（CAV）及卓越周期（T pd）5种特征参数为输入，以该台站观测到的PGV为预测目标.选取日本K-NET台网记录的102次地震的6 918组加速度记录进行模型训练，89次地震的3 430组加速度记录测试模型的泛化能力.结果表明，相同数据集下，对比基于Pd的PGV预测模型和基于支持向量机的PGV预测模型，基于XGBoost的PGV预测模型的预测值与实测值更趋近1∶1比例关系，且预测误差标准差更小，预测残差均值更接近0，且在中国的实际地震震例上的运行结果良好.基于XGBoost的PGV预测模型可用于现地地震预警地震动峰值的预测.
Peak Ground Velocity (PGV) is one of the parameter commonly used to measure the damage potential of ground shaking to building structures, and real-time prediction of PGV is a key technology in emergency response to major engineering earthquakes. To further improve the accuracy of PGV prediction, in this paper it proposes an onsite PGV prediction model based on Extreme Gradient Boosting (XGBoost). The model takes five characteristic parameters, including peak acceleration (P a), peak velocity (P v), peak displacement (P d), cumulative absolute velocity (CAV), and predominant period (T pd) in the first 3 seconds of the P-wave observed at the station as inputs, and the PGV observed at the station as the prediction target. 6 918 sets of acceleration records from 102 earthquakes recorded by the K-NET station network in Japan were used for model training, and 3 430 sets of acceleration records from 89 earthquakes were used to test the generalization ability of the model. The results show that, within the same dataset, the PGV prediction model based on XGBoost has a predictive value that is closer to a 1:1 ratio with the actual measured values compared to the PGV prediction models based on P d and support vector machines. Additionally, the standard deviation of the prediction errors is smaller, the mean of the prediction residuals is closer to zero, and the model performs well on actual earthquake cases in China. The PGV prediction model based on XGBoost can be used for the prediction of peak ground motion in local earthquake early warning systems.
地震 / 预警 / 现地预警 / 极限梯度提升树 / 机器学习 / 地震动峰值速度 / 工程地质.
earthquakes / early warning / onsite warning / XGBoost / machine learning / peak ground velocity / engineering geology
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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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