场地条件校正的PGA放大系数简化估计方法

陈龙伟; 吴晓阳; 唐川

doi:10.3969/j.issn.0258-2724.20200508

场地条件校正的PGA放大系数简化估计方法

doi: 10.3969/j.issn.0258-2724.20200508

陈龙伟^{1, 2,},
吴晓阳^{1, 2},
唐川^{1, 2}

1.
中国地震局工程力学研究所, 黑龙江哈尔滨 150080
2.
中国地震局地震工程与工程振动重点实验室, 黑龙江哈尔滨 150080

基金项目: 国家重点研发计划（2018YFC1504004）

详细信息

作者简介:
陈龙伟（1983—），男，研究员，博士，博士生导师，研究方向为岩土地震工程、土动力学、工程抗震等，E-mail：chenlw@iem.ac.cn

中图分类号: P315.9
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- 被引次数: 0
出版历程
- 收稿日期: 2020-08-04
- 修回日期: 2020-11-25
- 网络出版日期: 2020-12-25
- 刊出日期: 2020-12-25

Simplified Prediction Method for PGA Amplification Factors Corrected by Site Conditions

CHEN Longwei^{1, 2
,},
WU Xiaoyang^{1, 2},
TANG Chuan^{1, 2}

1.
Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
2.
Key Laboratory of Earthquake Engineering and Engineering Vibration, China Earthquake Administration, Harbin 150080, China

摘要

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.
- 地震数据 /
- PGA放大系数 /
- 场地校正 /
- 简化方法 /
- KiK-net
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 the f_PGA possibility model parameters with respect to the combinations of site characteristic parameters are obtained via regression analysis. Using the f_PGA model, ground surface PGA values, corrected by site conditions, can be predicted under different exceedance probability levels. Data analysis indicates that f_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.
- seismic data /
- PGA amplification factor /
- site correction /
- simplified method /
- KiK-net

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图 1 选取台站场地特征参数的统计

Figure 1. Statistic histograms of site characteristic parameters

下载: 全尺寸图片幻灯片

图 2 IWTH20台站实测f_PGA随A_PGAR的分布

Figure 2. Distribution of f_PGA from IWTH20 with respect to A_PGAR

下载: 全尺寸图片幻灯片

图 3 拟合系数与场地特征参数的分布

Figure 3. Scattering of regressive coefficients with respect to site characteristic parameters

下载: 全尺寸图片幻灯片

图 4 拟合系数和Z(V_s30, D, T)线性拟合曲线与实测数据对比

Figure 4. Regressive coefficients linearly predicted by Z(V_s30, D, T) compared with real data

下载: 全尺寸图片幻灯片

图 5 拟合系数和Z(V_s30, D, T)二次函数拟合曲线与实测数据对比

Figure 5. Regressive coefficients quadratically predicted by Z(V_s30, D, T) compared with real data

下载: 全尺寸图片幻灯片

图 6 拟合系数和Z(V_se, D, T)线性拟合曲线与实测数据对比

Figure 6. Regressive coefficients linearly predicted by Z(V_se, D, T) compared with real data

下载: 全尺寸图片幻灯片

图 7 拟合系数和Z(V_se, D, T)二次函数拟合曲线与实测数据对比

Figure 7. Regressive coefficients quadratically predicted by Z(V_se, D, T) comparing with data

下载: 全尺寸图片幻灯片

图 8 多概率水平下地表PGA预测值与实测数据对比（中间变量Z = C₁V_s30 + C₂D + C₃T）

Figure 8. Prediction of surface PGA under different probability levels compared to observed data (Z = C₁V_s30 + C₂D + C₃T)

下载: 全尺寸图片幻灯片

图 9 多概率水平下地表PGA预测值与实测数据对比（中间变量Z = C₁V_se + C₂D + C₃T）

Figure 9. Prediction of surface PGA under different probability levels compared to observed data (Z = C₁V_se + C₂D + C₃T)

下载: 全尺寸图片幻灯片

表 1 所选Kik-net台站场地特征参数

Table 1. Site characteristic parameters from KiK-net

台站	V_s30/(m•s⁻¹)	V_se/( m•s⁻¹)	D/m	T/s	台站	V_s30/(m•s⁻¹)	V_se/( 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

下载: 导出CSV

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

下载: 导出CSV

表 3 V_s30、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 V_s30, D and T

函数类型	待定系数	拟合系数
函数类型	待定系数	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

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表 4 V_se、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 V_se, D and T

函数类型	待定系数	拟合系数
函数类型	待定系数	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

下载: 导出CSV

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
中间变量	函数类型	a₁	b₁	a₂	b₂
Z(V_s30, D, T)	线性函数	0.060	7.042	0.312	10.459
Z(V_s30, D, T)	二次函数	0.054	4.364	0.296	7.734
Z(V_se, D, T)	线性函数	0.060	6.857	0.334	10.937
Z(V_se, D, T)	二次函数	0.051	5.672	0.328	9.708

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文章访问数: 482
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场地条件校正的PGA放大系数简化估计方法

doi: 10.3969/j.issn.0258-2724.20200508

作者简介: 陈龙伟（1983—），男，研究员，博士，博士生导师，研究方向为岩土地震工程、土动力学、工程抗震等，E-mail：chenlw@iem.ac.cn

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Simplified Prediction Method for PGA Amplification Factors Corrected by Site Conditions

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出版历程

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作者简介:
陈龙伟（1983—），男，研究员，博士，博士生导师，研究方向为岩土地震工程、土动力学、工程抗震等，E-mail：chenlw@iem.ac.cn