局部场地地震动高频衰减系数估计模型

稂子平 俞瑞芳 肖亮 傅磊 周健

稂子平,俞瑞芳,肖亮,傅磊,周健. 2022. 局部场地地震动高频衰减系数估计模型. 地震学报,44(0):1−10 doi: 10.11939/jass.20220053
引用本文: 稂子平,俞瑞芳,肖亮,傅磊,周健. 2022. 局部场地地震动高频衰减系数估计模型. 地震学报,44(0):1−10 doi: 10.11939/jass.20220053
Lang Z P,Yu R F,Xiao L,Fu L,Zhou J. 2022. An estimation model of high frequency attenuation coefficient of ground motion for local site. Acta Seismologica Sinica,44(0):1−10 doi: 10.11939/jass.20220053
Citation: Lang Z P,Yu R F,Xiao L,Fu L,Zhou J. 2022. An estimation model of high frequency attenuation coefficient of ground motion for local site. Acta Seismologica Sinica44(0):1−10 doi: 10.11939/jass.20220053

局部场地地震动高频衰减系数估计模型

doi: 10.11939/jass.20220053
基金项目: 北京市自然科学基金(No.8212018)、中央级公益性科研院所基本科研业务费专项(DQJB21B36)和国家重点研发计划课题(2017YFC0404901)联合资助
详细信息
    通讯作者:

    俞瑞芳,e-mail:yrfang126@126.com

  • 中图分类号: P315.9

An estimation model of high frequency attenuation coefficient of ground motion for local site

  • 摘要: 采用随机有限断层法进行地震动模拟时,选用合理的参数描述特定局部场地近地表高频衰减特征,对评价地震动模拟结果的正确与否具有重要的实践意义。 在工程场址地震动参数预测中,如何快速确定该参数的取值,是实际应用中亟需解决的问题。首先对场地高频衰减系数κ0和平均剪切波速VS30的相关性进行了分析;然后,选取国内外学者基于计算得到的546个κ0系数,采用一定时窗内的κ0均方根值讨论其随平均剪切波速VS30增大的变化趋势。 结果表明,虽然κ0具有明显的区域差异性,但其均方根值随着VS30的增大,呈现出逐渐减小的趋势。 为了得到合理的κ0估计模型,分别采用线性函数、多项式函数、对数线性函数和双对数线性函数对κ0均方根值和vS30关系进行初步拟合,结果表明,对数线性函数能够较好地描述κ0VS30之间的关系。 最后,基于筛选得到的477个数据,采用最小二乘法对模型参数进行了拟合,建立了适合于工程应用的κ0- vS模型。经过对模型适用性的分析表明,本研究所构建的κ0估计模型在预测工程场址地震动参数时能够合理估计地震动的高频衰减影响。

     

  • 图  1  本文研究采用的κ0值随平均剪切波速 vS30的分布

    Figure  1.  The distribution of κ0 value used in this study with mean shear wave velocity vS30

    图  2  κ0值统计数据箱型图

    Figure  2.  κ0 value statistics box plot

    图  3  四种函数对κrms的拟合情况

    Figure  3.  Fitting of four functions to κrms

    图  4  数据量分布情况图

    Figure  4.  Data distribution diagram

    图  5  对数线性函数拟合以及和其他模型对比

    Figure  5.  Log-linear function fitting and comparison with other models

    图  6  对数线性函数拟合残差图

    Figure  6.  Log-linear Function fit residual plot

    图  7  整体数据校验和MTN台站数据校验情况

    Figure  7.  Overall data check and MTN station data check

    表  1  本文研究采用的κ0数目、来源及相应的VS分布范围

    Table  1.   The number and source of κ0 used in this study and the corresponding distribution range of VS

    序号数据个数 vS30范围(m/s)地区文献
    160106.8—904.2日本Cabas et al2017
    216213.2—744.1日本Cabas et al2017
    350507.7—1 433.4日本Van Houtte et al2011
    4271 106.8— 2 394.0日本Van Houtte et al2011
    54515.7—1 301.3日本Laurendeau et al2013
    614170.6—1 428.1法国Drouet et al2010
    724192.1—747.1瑞士Edwards et al2015
    881 174.0—1 810.5瑞士Edwards et al2011
    916380.1—1 811.5瑞士Edwards et al2011
    1054160.1—942.8中国台湾Huang et al2017
    114233.1—684.8中国台湾Lai et al2016
    1210167.5-496.4中国台湾Lai et al2016
    1329191.8—746.9土耳其Bora et al2017
    1416142.6—1 029.6意大利Bora et al2017
    1551 054.7—1 392.5克罗地亚Stanko et al2017
    164854.1—953.3克罗地亚Stanko et al2017
    1711516.6—715.5中国台湾Van Houtte et al2011
    1838299.6—652.9中国Fu&Li (2017
    1910435.7—1 518.3新西兰Van Houtte et al2018
    209401.0—661.8亚利桑那Kishida et al2014
    216550.9—1 000.3加利福尼亚Van Houtte et al2011
    22117170.9—1531.2中国台湾Chang et al2019
    237263.3—660.3中国Zheng et al2019
    247531.2—912.1日本Zhu (2016
    下载: 导出CSV

    表  2  κ0值在不同 vS30范围的分组统计

    Table  2.   Group statistics of κ0 values in different vS30 ranges

    vS30范围/(m·s-1数量最小值最大值标准差均值
    100—200370.019 60.076 70.014 850.054 48
    200—300650.022 20.072 60.014 580.051 33
    300—400950.009 20.077 20.015 380.043 41
    400—500950.008 80.087 50.016 060.040 43
    500—600730.003 80.067 40.015 160.035 60
    600—700460.003 80.080 90.015 170.029 28
    700—800240.013 00.071 10.014 110.033 48
    800—900190.004 90.073 90.017 840.034 12
    900—1000160.014 30.052 80.010 990.027 61
    1 000—1 10070.015 30.027 00.003 930.022 73
    1 100—1 20070.006 00.025 80.007 520.015 47
    1 200—1 300100.013 60.026 00.004 610.019 67
    1 300—1 40060.010 10.031 50.009 060.020 13
    1 400—1 50090.009 60.026 30.006 150.015 72
    1 500—1 800160.006 90.027 10.006 750.016 06
    1 800—2 100110.002 90.029 30.009 360.013 71
    2 100—2 400100.002 90.025 00.009 300.011 93
    下载: 导出CSV

    表  3  κrms vS30的经验关系

    Table  3.   The empirical relationship between кrms and vS30

    拟合函数(1)线性函数:κrms=avS30+b
    模型参数abSSER2
    −1.557×10−54.478×10−20.003 6010.773 4
    拟合函数(2)多项式函数:κrms=avS302+b vS30+c
    模型参数abcSSER2
    1.284×10−8−4.741×10−50.058 860.000 655 90.958 7
    拟合函数(3)对数线性函数:κrms =algvS30+b
    模型参数abSSER2
    −3.439×10−21.286×10−10.001 4660.907 8
    拟合函数(4)双对数线性函数:lgκrms=algvS30+b
    模型参数abSSER2
    −4.488×10−1−2.72×10−10.002 2450.858 7
    下载: 导出CSV
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