Simplified Prediction Method for PGA Amplification Factors Corrected by Site Conditions
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摘要:
地表峰值加速度(peak ground-motion acceleration,PGA)是直观反映地震动强度的一个物理量,概念清晰且工程应用方便. 场地条件校正的PGA及其校正方法是特定工程抗震设计需要解决的问题. 为此,选取日本具有地表和井下记录KiK-net台网的32个台站及地震数据,通过对实测地震数据分析,提出了一种场地校正的PGA放大系数(
f PGA)的简化估计方法. 该方法通过数据回归给出f PGA概率密度函数参数与场地特征参数线性组合之间的线性和二次函数形式的拟合公式;并采用f PGA概率预测模型,提出了多超越概率水平地表PGA值的预测方法. 数据分析结果显示:场地f PGA具有随机不确定性,可以采用对数正态分布函数模拟,其概率密度函数的参数(均值和标准差)与单一场地特征参数相关性较小,但与场地特征参数的线性组合相关性较大. 模型预测值与实测数据吻合较好,验证了简化估计方法的可行性.Abstract:Peak ground-motion acceleration (PGA) directly reflects ground shaking intensity with the advantages in conceptual clarity and engineering application. Prediction of PGA, along with site condition correction, needs to be handled in site-specific seismic design. In this work, 32 stations and the earthquake data recorded are collected from the KiK-net strong motion array, Japan, so as to propose a simplified method to predict PGA amplification factors (
f PGA) corrected by site condition. Linear and quadratic empirical formulae of thef PGA possibility model parameters with respect to the combinations of site characteristic parameters are obtained via regression analysis. Using thef PGA model, ground surface PGA values, corrected by site conditions, can be predicted under different exceedance probability levels. Data analysis indicates thatf PGA is variable but can be simulated by a log-normally distributed function, of which mean and standard deviation are less correlated with a single site characteristic parameter but have good correlation with the linear combinations of the site characteristic parameters. The reasonable agreement between the predictions and records testifies the feasibility of the proposed method.-
Key words:
- seismic data /
- PGA amplification factor /
- site correction /
- simplified method /
- KiK-net
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表 1 所选Kik-net台站场地特征参数
Table 1. Site characteristic parameters from KiK-net
台站 Vs30/(m•s−1) Vse/( m•s−1) D/m T/s 台站 Vs30/(m•s−1) Vse/( m•s−1) D/m T/s AOMH17 378.4 196.6 8 0.163 IWTH26 371.1 228.2 10 0.175 FKSH09 584.6 244.2 10 0.164 IWTH27 670.3 150.0 4 0.107 FKSH12 448.5 357.1 22 0.244 KMMH02 576.7 218.4 6 0.110 FKSH19 338.1 255.0 20 0.314 KMMH16 279.7 229.2 41 0.533 IBRH11 242.5 197.1 30 0.495 KSRH03 249.8 213.2 34 0.523 IBRH13 335.4 288.0 24 0.318 KSRH10 212.9 185.9 36 0.644 IBRH14 829.1 180.0 2 0.044 MYGH04 849.8 220.0 4 0.073 IBRH16 626.1 205.9 5 0.097 MYGH05 305.3 120.0 2 0.067 IBRH18 558.6 432.0 15 0.139 MYGH06 593.1 200.0 2 0.040 IWTH04 455.9 314.3 15 0.191 MYGH09 358.2 315.8 38 0.400 IWTH05 429.2 276.9 9 0.130 MYGH10 347.5 329.6 34 0.386 IWTH18 891.6 180.0 2 0.044 MYGH11 859.2 210.0 3 0.057 IWTH20 288.8 283.4 46 0.629 TCGH07 419.5 343.8 22 0.253 IWTH21 521.1 326.5 12 0.168 TCGH12 343.7 305.1 50 0.523 IWTH23 922.9 370.0 4 0.043 TCGH14 849.0 275.0 4 0.058 IWTH24 486.4 360.0 10 0.111 TKCH08 353.2 312.0 36 0.390 表 2 选取台站拟合系数
Table 2. Regressive parameters for selected station sites
台站 a1 b1 a2 b2 台站 a1 b1 a2 b2 AOMH17 −0.098 1.724 −0.266 0.741 IWTH26 −0.136 2.085 −0.294 1.163 FKSH09 −0.119 1.994 −0.215 0.945 IWTH27 −0.081 2.134 −0.189 1.005 FKSH12 −0.183 2.654 −0.331 1.855 KMMH02 −0.081 1.617 −0.054 0.473 FKSH19 −0.114 2.266 −0.111 0.740 KMMH16 −0.092 1.924 −0.127 0.854 IBRH11 −0.117 2.332 −0.112 0.966 KSRH03 −0.155 1.754 −0.177 0.567 IBRH13 −0.098 2.311 −0.138 1.118 KSRH10 −0.139 2.036 −0.179 0.816 IBRH14 −0.112 2.128 −0.346 1.427 MYGH04 −0.111 2.023 −0.158 0.943 IBRH16 −0.145 2.421 −0.209 1.386 MYGH05 −0.055 1.149 −0.204 −0.094 IBRH18 −0.103 2.211 −0.154 1.253 MYGH06 −0.022 0.592 −0.068 −0.846 IWTH04 −0.113 1.980 −0.201 0.724 MYGH09 −0.052 1.149 −0.181 −0.046 IWTH05 −0.134 2.212 −0.211 1.026 MYGH10 −0.044 1.614 −0.102 0.157 IWTH18 −0.115 2.168 −0.301 1.314 MYGH11 −0.100 2.002 −0.162 0.859 IWTH20 −0.030 0.831 −0.152 −0.453 TCGH07 −0.145 2.200 −0.257 0.951 IWTH21 −0.120 2.133 −0.114 0.860 TCGH12 −0.081 1.402 −0.132 0.089 IWTH23 −0.090 1.884 −0.201 0.815 TCGH14 −0.251 2.596 −0.636 1.947 IWTH24 −0.027 0.954 −0.075 −0.365 TKCH08 −0.077 1.927 −0.165 0.941 表 3 Vs30、D和T参数组合Z情况下待定系数值
Table 3. Regressive coefficients corresponding to Z in combination of Vs30, D and T
函数类型 待定系数 拟合系数 a1 b1 a2 b2 线性函数 C1 0.001 −0.002 −0.008 −0.003 C2 −0.044 0.045 0.051 0.044 C3 3.905 −3.946 −3.946 −3.935 C4 −0.061 1.456 −0.096 0.049 C5 −0.060 −0.479 0.025 −0.483 二次函数 C1 0.001 −0.003 −0.002 −0.003 C2 0.031 −0.062 −0.003 −0.046 C3 0.409 −3.445 −0.788 −3.235 C4 0.038 −0.589 −0.550 −2.427 C5 −0.265 −1.603 −0.707 −2.311 C6 0.109 −0.228 −0.325 −0.381 表 4 Vse、D和T参数组合Z情况下待定系数值
Table 4. Regressive coefficients corresponding to Z in combination of Vse, D and T
函数类型 待定系数 拟合系数 a1 b1 a2 b2 线性函数 C1 −0.002 −0.003 −0.001 −0.004 C2 0.057 0.060 −0.017 0.071 C3 −3.932 −3.884 −3.922 −3.899 C4 −0.059 1.377 −0.243 0.393 C5 0.081 −0.764 −0.032 −0.587 二次函数 C1 0.004 0.006 −0.001 0.006 C2 0.007 0.043 0.028 0.021 C3 1.020 2.734 −3.786 2.767 C4 0.094 0.651 −0.305 −0.614 C5 −0.347 0.990 −0.306 1.424 C6 0.134 −0.172 −0.143 −0.306 表 5 不同方法中拟合系数的残差平方和
Table 5. Sum of squared residuals of regressive coefficients in different methods
中间变量 函数类型 拟合系数残差平方和 a1 b1 a2 b2 Z(Vs30, D, T) 线性函数 0.060 7.042 0.312 10.459 二次函数 0.054 4.364 0.296 7.734 Z(Vse, D, T) 线性函数 0.060 6.857 0.334 10.937 二次函数 0.051 5.672 0.328 9.708 -
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