基于支持向量机的现地地震预警地震动峰值预测

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (3) : 63-72.

论文

余聪1,2，宋晋东1,2，李山有1,2

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Prediction of peak ground motion for on-site earthquake early warning based on SVM

YU Cong1,2, SONG Jindong1,2, LI Shanyou1,2

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文章历史 +

摘要

以0.1-10Hz带通滤波后三分向矢量合成地震动峰值PGA与PGV为预测目标参数，利用日本K-net强震台网P波触发后3秒数据，基于人工智能中的经典机器学习方法-支持向量机，选取加速度幅值Pa、速度幅值Pv、位移幅值Pd、傅立叶谱幅值AMmax、速度平方积分IV2、破坏烈度DI、累积绝对速度CAV、阿里亚斯烈度Ia这8种特征参数构建地震动峰值预测模型。结果表明，对比常规的Pd预测模型，本文建立的支持向量机PGA与PGV预测模型，在测试数据集及随机选取2次震例数据集上计算得到的预测值与实测值更趋近1:1比例关系，且PGA与PGV的预测值误差不受震中距变化的影响，PGA与PGV预测时的低值高估与高值低估现象也得到了改善。本文构建的支持向量机预测模型可用于现地地震预警地震动峰值、即仪器地震烈度的预测。

Abstract

Here, taking ground motion peak values PGA and PGV synthesized by 3-D vectors after 0.1-10 Hz band-pass filtering as the prediction target parameters, using the data of 3 seconds after triggering P wave of Japan K-net strong earthquake network, based on the classic machine learning method in artificial intelligence-support vector machine (SVM), a ground motion peak value prediction model was constructed by selecting 8 feature parameters including acceleration amplitude Pa, velocity amplitude Pv, displacement amplitude Pd, Fourier spectrum amplitude AMmax, velocity square integral IV2, destruction intensity DI, cumulative absolute velocity CAV and Arias intensity Ia. Results showed that compared with the conventional Pd prediction model, using the constructed SVM prediction model for PGA and PGV, the predicted values calculated with testing data set and data sets of 2 earthquake cases selected randomly and the actual measured values are closer to a 1∶1 relationship, the errors for the predicted values of PGA and PGV are not affected by the change in epicenter distance; phenomena of under-estimation and over-estimation of PGA and PGV during prediction are improved; the constructed SVM prediction model can be used to predict ground motion peak values for in situ earthquake warning, i.e., to predict the instrument seismic intensity.

导出引用

余聪1,2，宋晋东1,2，李山有1,2. 基于支持向量机的现地地震预警地震动峰值预测[J]. 振动与冲击, 2021, 40(3): 63-72

YU Cong1,2, SONG Jindong1,2, LI Shanyou1,2. Prediction of peak ground motion for on-site earthquake early warning based on SVM[J]. Journal of Vibration and Shock, 2021, 40(3): 63-72

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