Empirical prediction models of time-averaged shear wave velocity vS20 and vS30 in Sichuan and Yunnan areas
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摘要: 利用四川和云南地区共973个工程场地钻孔资料，分别基于常速度外推模型、对数线性模型和条件独立模型的经验外推方法建立了该区域20 m和30 m平均剪切波速vS20和vS30的经验预测模型。研究表明常速度外推模型的预测误差最大，当波速资料深度小于10 m时，常速度外推方法会显著低估实际场地平均波速。基于对数线性外推方法建立了四川和云南地区波速经验预测模型，对比结果表明四川和云南地区平均波速预测结果与北京和加州地区较接近，明显低于日本地区。基于三种不同外推方法的预测误差对比分析结果表明条件独立性模型的预测结果在不同深度时误差均为最小，建议优先采用该方法建立的区域波速预测模型。Abstract: The time-averaged shear wave velocity of overburden soil is an important parameter for site classification and reflecting site effects on ground motion, which is widely used in earthquake ground motion prediction models. Using the lithology and wave velocity profile data of 973 boreholes in Sichuan and Yunnan, we study the regional prediction model of the average shear wave velocity. Based on the bottom constant velocity （BCV） model, log-linear model and Markov independent model, the empirical prediction models of vS20 and vS30 in this region were established. The results show that, the BCV method has the largest prediction error. When the depth of the shear wave velocity is less than 10 m, this method will significantly underestimate the average wave velocity of the actual site. Based on the log-linear model of Boore method, we establish an empirical prediction model. By comparison, we find that the average wave speed prediction results in Sichuan and Yunnan are close to those in Beijing and California, and significantly lower than those in Japan. Through the comparative analysis of prediction error of three different extrapolation methods, we find that the prediction results based on Markov independence model have the smallest error at different depths, and it is preferred to use this method to set up regional prediction model.
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图 1 收集的四川和云南地区973个钻孔的位置分布
Ⅰ ： 扬子准地台；Ⅱ ： 秦岭—大别造山带；Ⅲ ： 松潘—甘孜造山带；Ⅳ ： 羌塘地带；Ⅴ ： 中缅地块；Ⅵ ： 改则—那曲造山带；Ⅶ ： 右江造山带
Figure 1. Location of 973 borehole sites of Sichuan and Yunnan Provinces used in this study
Ⅰ ： Yantze paraplatform；Ⅱ ： Qinling-Dabie orogenic belt；Ⅲ ： Songpan-Garze orogenic belt；Ⅳ ： Qiangtang block；Ⅴ ： China-Myanmar block；Ⅵ ： Greze-Nakchu orogenic belt；Ⅶ ： Youjiang orogenic belt
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图 2 收集的四川和云南地区973个钻孔数据的第四系沉积物厚度分布
Figure 2. Distribution of Quaternary sediment depth from 973 boreholes in Yunnan and Sichuan Provinces
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图 3 四川和云南地区典型钻孔柱状图
（a） 四川挖角乡；（b） 云南坝心乡
Figure 3. Typical drilling column map in Sichuan and Yunnan region
（a） Wajiao township in Sichuan；（b） Baxin township in Yunnan
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图 4 基于不同深度钻孔数据采用BCV方法的预测值vS20est与实际平均波速vS20的对比
Figure 4. Comparison between the estimated vS20est and the measured vS20 in BCV model at different depths
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图 5 基于不同深度钻孔资料采用BCV法的预测值vS30est与实际波速vS30的对比
Figure 5. Comparison between the estimate vS30est and the measured vS30 in BCV model at different depths
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图 8 基于条件独立模型得到不同深度下
${\rm{lg}}v_{{\rm{S}}[{\textit{z}}{\text{，}}30]}$ 与${\rm{lg}}v_{{\rm{S}}}{\text{（}}{\textit{z}}{\text{）}}$ 的拟合回归分析结果Figure 8. Regression results of
${\rm{lg}}v_{{\rm{S}}[{\textit{z}}{\text{，}}30]} $ and${\rm{lg}}v_{{\rm{S}}}{\text{（}}{\textit{z}}{\text{）}} $ at different depths based on the conditional independence property model![]()
图 9 基于条件独立模型得到不同深度下
${\rm{lg}}v_{{\rm{S}}[{\textit{z}}{\text{，}}20]} $ 与${\rm{lg}}v_{{\rm{S}}}{\text{（}}{\textit{z}}{\text{）}} $ 的拟合回归分析结果Figure 9. Regression results of
${\rm{lg}}v_{{\rm{S}}[{\textit{z}}{\text{，}}20]} $ and${\rm{lg}}v_{{\rm{S}}}{\text{（}}{\textit{z}}{\text{）}} $ at different depths based on the conditional independence property model![]()
图 10 采用BCV模型、Boore对数线性模型和Markov条件独立模型建立的四川和云南地区vS20 （a）和vS30 （b）预测模型的误差对比
Figure 10. Comparison of the estimation errors of vS20 （a） and vS30 （b） for the BCV model，the Boore log-linear model，and the conditional independence property model （labeled as Markov process） in Sichuan and Yunnan regions
表 1 钻孔深度统计表
Table 1 Drilling depth statistics
钻孔深度d /m 钻孔个数 0＜d＜5 3 5≤d＜20 330 20≤d＜30 369 d≥30 271 
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表 2 采用BCV方法的预测值vS20est与实测值vS20的相关系数r及BCV方法的预测误差标准差σRES
Table 2 List of correlation coefficients r and standard deviation σRES of vS20est and vS20 by BCV method
深度/m r σRES 深度/m r σRES 6 0.811 3 22.06 13 0.986 2 2.89 7 0.867 0 17.79 14 0.989 7 1.50 8 0.902 6 15.13 15 0.993 7 1.24 9 0.937 5 10.61 16 0.995 7 0.67 10 0.952 9 8.24 17 0.997 8 0.36 11 0.968 1 3.47 18 0.999 0 0.53 12 0.977 7 3.33 19 0.999 8 0.32 注：σRES为预测误差（估计值—实际值）的标准差，下同。
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表 3 采用BCV方法的预测值vS30est与实测值vS30的相关系数r及BCV方法的预测误差标准差σRES
Table 3 List of correlation coefficients r and standard deviation σRES of vS30est and vS30 by BCV method
深度/m r σRES 深度/m r σRES 6 0.733 8 26.303 18 0.976 0 2.594 7 0.767 6 23.926 19 0.990 2 0.245 8 0.813 6 21.711 20 0.991 7 0.224 9 0.882 2 12.613 21 0.992 5 0.275 10 0.899 7 10.458 22 0.994 1 0.581 11 0.925 4 6.408 23 0.996 3 0.758 12 0.940 2 4.946 24 0.996 5 0.922 13 0.952 2 3.752 25 0.997 9 1.167 14 0.962 8 0.897 26 0.997 6 0.272 15 0.968 0 1.221 27 0.999 0 0.179 16 0.970 5 0.637 28 0.999 8 0.016 17 0.979 5 0.375 29 1.000 0 0.007
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表 4 类比Boore （2004）给出的vS30预测方法 ［ 式（5） ］ 建立四川和云南地区vS20预测经验关系的回归分析结果
Table 4 Regression results of vS20 predictive empirical relationships for Sichuan and Yunnan regions based on Boore （2004） method of equation （5）
深度/m a0 a1 相关系数 r σRES 深度/m a0 a1 相关系数 r σRES 6 0.824 0.537 0.745 0.072 13 1.025 −0.010 0.962 0.029 7 0.880 0.394 0.795 0.066 14 1.026 −0.021 0.974 0.025 8 0.925 0.277 0.835 0.059 15 1.024 −0.024 0.983 0.020 9 0.963 0.176 0.872 0.053 16 1.020 −0.021 0.990 0.016 10 0.992 0.097 0.902 0.047 17 1.014 −0.014 0.994 0.012 11 1.007 0.051 0.926 0.041 18 1.009 −0.010 0.997 0.008 12 1.021 0.009 0.947 0.035 19 1.005 −0.007 0.999 0.004 注：σRES为预测误差（此处取估计值相对于实际值的对数残差，即lg估计值−lg实际值）的标准差，下同。
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表 5 基于Boore （2004）方法 ［ 式（5） ］ 建立四川和云南地区vS30预测经验关系的回归分析结果
Table 5 Regression results of vS30 predictive empirical relationships for Sichuan and Yunnan regions based on Boore （2004） method
深度/m a0 a1 相关系数r 标准差σRES 深度/m a0 a1 相关系数r 标准差σRES 6 1.458 0.440 0.445 0.101 1 18 −0.056 1.057 0.906 0.047 7 7 1.364 0.479 0.454 0.100 6 19 −0.064 1.058 0.921 0.044 0 8 1.191 0.553 0.492 0.098 3 20 −0.065 1.055 0.933 0.040 6 9 0.946 0.657 0.553 0.094 0 21 −0.062 1.052 0.944 0.037 2 10 0.734 0.746 0.607 0.089 7 22 −0.059 1.048 0.952 0.034 5 11 0.578 0.809 0.661 0.084 7 23 −0.060 1.046 0.961 0.031 4 12 0.455 0.859 0.706 0.079 9 24 −0.064 1.046 0.969 0.028 1 13 0.324 0.912 0.748 0.074 9 25 −0.064 1.044 0.975 0.025 0 14 0.208 0.958 0.789 0.069 3 26 −0.061 1.040 0.981 0.022 0 15 0.102 1.000 0.827 0.063 4 27 −0.053 1.035 0.985 0.019 3 16 0.021 1.031 0.861 0.057 4 28 −0.042 1.029 0.989 0.016 7 17 −0.040 1.053 0.888 0.051 9 29 −0.032 1.023 0.992 0.014 1
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表 6 基于条件独立模型（式6）得到的
$v_{{\rm{S}}[{\textit{z}}{\text{，}}30]} $ 与$v_{{\rm{S}}}{\text{（}}{\textit{z}}{\text{）}} $ 之间的经验关系Table 6 Regression results for
$v_{{\rm{S}}[{\textit{z}}{\text{，}}30]} $ and$v_{{\rm{S}}}{\text{（}}{\textit{z}}{\text{）}} $ empirical relationships based on the conditional independence property model of equation 6深度/m ${c}_{0}$ ${c}_{1}$ 相关系数r 标准差σRES 深度/m ${c}_{0}$ ${c}_{1}$ 相关系数r 标准差σRES 6 1.038 0.608 0.649 0.153 18 0.338 0.878 0.885 0.118 7 0.882 0.669 0.666 0.150 19 0.265 0.908 0.928 0.118 8 0.651 0.762 0.723 0.146 20 0.255 0.909 0.934 0.118 9 0.481 0.831 0.821 0.141 21 0.295 0.893 0.930 0.117 10 0.499 0.822 0.823 0.134 22 0.313 0.885 0.927 0.116 11 0.561 0.796 0.849 0.136 23 0.326 0.879 0.933 0.114 12 0.530 0.806 0.843 0.136 24 0.387 0.854 0.907 0.118 13 0.426 0.847 0.861 0.134 25 0.289 0.892 0.944 0.094 14 0.494 0.820 0.877 0.128 26 0.279 0.895 0.943 0.095 15 0.442 0.840 0.878 0.125 27 0.154 0.942 0.960 0.080 16 0.522 0.808 0.865 0.123 28 0.063 0.977 0.979 0.064 17 0.359 0.870 0.895 0.120 29 0.067 0.975 0.986 0.057
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表 7 基于条件独立模型 ［ 式（6） ］ 得到
$v_{{\rm{S}}[{\textit{z}}{\text{，}}20]} $ 与$v_{{\rm{S}}}{\text{（}}{\textit{z}}{\text{）}} $ 之间的经验关系Table 7 Regression results for
$v_{{\rm{S}}[{\textit{z}}{\text{，}}20]} $ and$v_{{\rm{S}}}{\text{（}}{\textit{z}}{\text{）}} $ empirical relationships based on the conditional independence property model of equation （6）深度/m ${c}_{0}$ ${c}_{1}$ 相关系数r 标准差σRES 深度/m ${c}_{0}$ ${c}_{1}$ 相关系数r 标准差σRES 6 1.161 0.648 0.698 0.152 13 0.591 0.915 0.923 0.110 7 1.012 0.712 0.725 0.147 14 0.717 0.878 0.925 0.107 8 0.826 0.791 0.778 0.140 15 0.690 0.903 0.936 0.099 9 0.679 0.855 0.850 0.132 16 0.792 0.882 0.939 0.099 10 0.712 0.847 0.852 0.128 17 0.777 0.913 0.952 0.089 11 0.807 0.815 0.875 0.126 18 0.768 0.951 0.962 0.081 12 0.739 0.849 0.884 0.123 19 0.838 0.978 0.984 0.061
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陈国兴,丁杰发,方怡,彭艳菊,李小军. 2020. 场地类别分类方案研究[J]. 岩土力学,41(11):3509–3522. Chen G X,Ding J F,Fang Y,Peng Y J,Li X J. 2020. Investigation of seismic site classification scheme[J]. Rock and Soil Mechanics,41(11):3509–3522 (in Chinese).
李小军. 2013. 地震动参数区划图场地条件影响调整[J]. 岩土工程学报,35(增刊):21–29. Li X J. 2013. Adjustment of seismic ground motion parameters considering site effects in seismic zonation map[J]. Chinese Jour nal of Geotechnical Engineering,35(S2):21–29 (in Chinese).
汪闻韶. 1994. 土工地震减灾工程中的一个重要参量:剪切波速[J]. 水利学报,(3):80–84. doi: 10.3321/j.issn:0559-9350.1994.03.012 Wang W S. 1994. An important parameter in geotechnical engineering for earthquake disaster mitigation:Shear wave velocity[J]. Journal of Hydraulic Engineering,(3):80–84 (in Chinese).
姚冬生,方飞龙,李朝阳. 1990. 四川省区域地质概况及特征[J]. 中国区域地质,(1):8–13. Yao D S,Fang F L,Li C Y. 1990. A general account of the regional geology of Sichuan Province[J]. Regional Geology of China,(1):8–13 (in Chinese).
中华人民共和国住房和城乡建设部, 中华人民共和国国家质量监督检验检疫总局. 2016. GB50011—2010 建筑抗震设计规范[S]. 北京: 中国建筑工业出版社: 18–20. Ministry of Housing and Urban-rural Development of the People’s Republic of China, General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China. 2016. GB50011−2010 Code for Seismic Design of Buildings[S]. Beijing: China Architecture & Building Press: 18–20 （in Chinese）.
Boore D M. 2004. Estimating
${\bar {\Large v}}_{\rm{S30}} $ (or NEHRP site classes) from shallow velocity models (depths＜30 m)[J]. Bull Seismol Soc Am,94(2):591–597. doi: 10.1785/0120030105Boore D M,Thompson E M,Cadet H. 2011. Regional correlations of
${\bar {\Large v}}_{\rm{S30}} $ and velocities averaged over depths less than and greater than 30 meters[J]. Bull Seismol Soc Am,101(6):3046–3059. doi: 10.1785/0120110071Bozorgnia Y,Abrahamson N A,Atik L A,Ancheta T D,Atkinson G M,Baker J W,Baltay A S,Boore D M,Campbell K W,Chiou B S J,Darragh R B,Day S,Donahue J,Graves R W,Gregor N,Hands T C,Idriss I M,Kamai R,Rowshandel B,Seyhan E,Shahi S,Shantz T,Silva W,Spudich P A,Stewart J P,Watson-Lamprey J,Wooddell K,Youngs R. 2014. NGA-West2 research project[J]. Earthq Spectra,30(3):973–987. doi: 10.1193/072113EQS209M
Building Seismic Safety Council. 2015. NEHRP Recommended Seismic Provisions for New Buildings and Other Structures[S]. Washington D.C.: Federal Emergency Management Agency: 86−87.
Dai Z J,Li X J,Hou C L. 2013. A shear-wave velocity model for vS30 estimation based on a conditional independence property[J]. Bull Seismol Soc Am,103(6):3354–3361. doi: 10.1785/0120130025
Kuo C H,Cheng D S,Hsieh H H,Chang T M,Chiang H J,Lin C M,Wen K L. 2009. Comparison of three different methods in investigating shallow shear-wave velocity structures in Ilan,Taiwan[J]. Soil Dyn Earthq Eng,29(1):133–143. doi: 10.1016/j.soildyn.2008.01.010
Kuo C H,Wen K L,Hsieh H H,Chang T M,Lin C M,Chen C T. 2011. Evaluating empirical regression equations for vS and estimating vS30 in northeastern Taiwan[J]. Soil Dyn Earthq Eng,31(3):431–439. doi: 10.1016/j.soildyn.2010.09.012
Stewart J P,Klimis N,Savvaidis A,Theodoulidis N,Zargli E,Athanasopoulos G,Pelekis P,Mylonakis G,Margaris B. 2014. Compilation of a local vS profile database and its application for inference of vS30 from geologic- and terrain-based proxies[J]. Bull Seismol Soc Am,104(6):2827–2841. doi: 10.1785/0120130331
Wills C J,Clahan K B. 2006. Developing a map of geologically defined site-condition categories for California[J]. Bull Seismol Soc Am,96(4A):1483–1501. doi: 10.1785/0120050179
Xie J J,Zimmaro P,Li X J,Wen Z P,Song Y S. 2016. vS30 empirical prediction relationships based on a new soil-profile database for the Beijing plain area,China[J]. Bull Seismol Soc Am,106(6):2843–2854. doi: 10.1785/0120160053
Yong A L. 2016. Comparison of measured and proxy-based vS30 values in California[J]. Earthq Spectra,32(1):171–192. doi: 10.1193/013114EQS025M
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