基于遗传编程技术的强地震动参数预测方法

Research on the Prediction Method of Strong Ground Motion Parameters Based on Genetic Programming Techniques

基于NGA数据库,利用遗传编程技术,给出一套地震动峰值加速度(PGA)、峰值速度(PGV)和峰值位移(PGD)的预测方程。通过建立地震动参数与震级、断层距、断层机制以及30 m内的场地剪切波速度等关键地震学参数的关联性,在给出显式预测公式的基础上,进行信度检验和模型比对。结果表明:1)与传统的基于非线性回归技术的衰减关系类预测方程相比,遗传编程技术无需指定衰减关系的方程形式,可以对PGA、PGV和PGD的复杂行为进行建模,并给出显式公式,以满足地震工程的实际需要。2)与Campbell-Bozorgnia衰减关系相比,基于遗传编程技术的PGA与PGV的预测效果略优,PGD预测模型的RMSE和MAE分别为5.47和1.64,显著小于Campbell-Bozorgnia模型的45.98和4.61。3)所获得的地震动预测方程具备震级效应、场地放大效应和近场大震饱和效应特征,但未能反映软土减震效应,PGA、PGV和PGD的最大场地放大系数约为1.42、2.53和2.64。

Based on NGA database and using genetic programming techniques,we give a set of prediction equations for PGA,PGV and PGD.On the basis of the explicit prediction formula,the reliability test and model comparison are carried out by establishing the correlation between ground motion parameters and key seismological parameters such as magnitude,fault distance,fault mechanism,and site shear wave velocity within 30 meters.The results show that:1)Compared with the traditional decay relation-like prediction equations based on nonlinear regression techniques,genetic programming techniques do not need to specify the equation form of the decay relation,can model the complex behaviors of PGA,PGV and PGD,and give explicit formulas to meet engineering needs.2)Compared with the Campbell-Bozorgnia attenuation relationship,the prediction effect of PGA and PGV based on the genetic programming techniques is slightly better;the RMSE and MAE of the PGD prediction model are 5.47 and 1.64,respectively,which are significantly smaller than those of the Campbell-Bozorgnia model,which are 45.98 and 4.61.3)The obtained ground motion prediction equations are characterized by magnitude effect,site amplification effect,and saturation effect of near-field large earthquakes,but fail to reflect the soft-soil damping effect,and the maximum site amplification coefficients of PGA,PGV and PGD are about 1.42,2.53 and 2.64.