一种新的地震危险性表达方法研究

张萌 潘华

张萌,潘华. 2022. 一种新的地震危险性表达方法研究. 地震学报,44(0):1−13 doi: 10.11939/jass.20210106
引用本文: 张萌,潘华. 2022. 一种新的地震危险性表达方法研究. 地震学报,44(0):1−13 doi: 10.11939/jass.20210106
Zhang M,Pan H. 2022. A new method for expressing seismic hazards. Acta Seismologica Sinica,44(0):1−13 doi: 10.11939/jass.20210106
Citation: Zhang M,Pan H. 2022. A new method for expressing seismic hazards. Acta Seismologica Sinica44(0):1−13 doi: 10.11939/jass.20210106

一种新的地震危险性表达方法研究

doi: 10.11939/jass.20210106
基金项目: 中国地震局地球物理研究所基本科研业务费专项(DQJB21K51)资助
详细信息
    通讯作者:

    潘华,email:panhua.mail@163.com

  • 中图分类号:  

A new method for expressing seismic hazards

  • 摘要: 旨在寻找一种精度较高、超越概率范围较广并且便于应用的方式来表达地震危险性。回顾了当前常用的几种地震危险性表达方法,提出基于一个新函数来拟合地震危险性曲线的“特征系数法”,并使用《中国地震动参数区划图》(GB 18306—2015)的基础数据对该函数的拟合效果进行了验证。结果表明,新函数与地震危险性曲线拟合良好,与极值函数相比有明显的提升,能够充分地表达一个场点的地震危险性。另外,本文结果还显示该函数中表征曲线形状的参数k(文中称为特征系数)与场点面临的地震环境有关,k值较低的场点危险性贡献基本来自近场,而k值较高的场点中远距离的贡献是不能忽视的。

     

  • 图  1  某城市PGA危险性曲线和极值函数拟合结果

    Figure  1.  Results of fitting for the PGA hazard curve and extreme value function for a city

    图  2  某城市PGA危险性曲线和新函数的拟合结果

    Figure  2.  Results of fitting for the PGA hazard curve and new function of a city

    图  3  全国0.2°×0.2°间隔2万4 043个场点特征系数k频数分布

    Figure  3.  Distribution of the frequency of the characteristic coefficient k for the national 24 043 sites with interval of 0.2°×0.2°

    图  4  三种拟合方法的拟合残差Res统计对比

    (a)特征系数法;(b)分段直线拟合(c)幂函数拟合

    Figure  4.  Statistical comparison of fitting residuals for three methods at 24 043 sites

    (a) Shape parameter method;(b) Sectional line fitting;(c) Power function fitting

    图  5  四个城市基于特征系数法拟合得到的地震危险性曲线

    (a)北京;(b)上海;(c)兰州;(d)成都

    Figure  5.  Seismic hazard curves based on the characteristic coefficient method for the four cities

    (a) Beijing;(b) Shanghai;(c) Lanzhou;(d) Chengdu

    图  6  某城市基于特征系数法的谱加速度危险性曲线拟合结果

    Figure  6.  Results of fitting for the spectral acceleration hazard curve of a city based on the characteristic coefficient method

    图  7  拉萨和常德两场点PGA年超越概率曲线对比

    Figure  7.  Comparison of the annual PGA exceedance probability curves for the two sites in Lhasa and Changde

    图  8  拉萨和常德两场点PGA危险性曲线分解结果对比(横坐标表示计算时离散化微元到场点的距离,纵坐标代表不同距离微元贡献的百分比即概率密度函数PDF)

    (a)50年超越概率10%的PGA;(b)50年超越概率2%的PGA

    Figure  8.  Comparison of the results of the PGA hazard curve decomposition at the Lhasa and Changde sites

    (a) PGA of exceedance probability to be 10% in the future 50 years;(b) PGA of exceedance probability to be 2% in the future 50 years;

    图  9  拉萨和常德两场点潜在震源区分布

    Figure  9.  Distribution of potential source areas at the Lhasa and Changde sites

    表  1  4个城市特征系数法拟合的地震危险性曲线相对残差Res结果

    Table  1.   Fitting results of the relative residuals for seismic hazard curves by the characteristic coefficient method for four cities

    超越概率
    50年63%50年50%50年5%50年5%50年0.5%
    北京PGAcal/g0.0640.1040.3470.4830.730
    PGAfit/g0.0680.1090.3500.5000.806
    Res6.70%4.80%0.80%3.50%9.40%
    上海PGAcal/g0.0250.0400.1410.2050.338
    PGAfit/g0.0250.0410.1380.2000.328
    Res2.70%2.40%2.00%2.30%3.00%
    成都PGAcal/g0.0380.0560.1500.2040.305
    PGAfit/g0.0380.0560.1500.2020.301
    Res0.80%1.50%0.20%1.00%1.30%
    兰州PGAcal/g0.0530.0800.2680.3810.586
    PGAfit/g0.0530.0830.2590.3680.585
    Res0.20%4.40%3.10%3.70%0.00%
    下载: 导出CSV

    表  2  拉萨和常德特征系数k结果

    Table  2.   Results of characteristic coefficients k for Lhasa and Changde

    场点名称经度纬度PGA475/gal特征系数k
    拉萨91.4300.17g0.45
    常德111.829.40.18g0.27
    下载: 导出CSV

    表  3  k值场点期望距离计算结果

    Table  3.   Calculated expected distances for low k-value field points

    序号场点名称经纬度PGA475/gkPGA475期望距离/kmPGA2475期望距离/km
    1临沂118.6,350.1990.2524.6317.97
    2宿迁118.4,340.1760.2827.2219.01
    3唐山118.1,39.60.2450.3021.4216.51
    4黄山118.4,29.80.0340.3020.8414.15
    5佛山113,230.0710.3026.3019.99
    6漳州117.6,24.60.1150.2827.7118.28
    7渭南109.6,34.40.1990.2826.6019.37
    8临汾111.6,36.20.2380.2920.9617.50
    9当雄90.6,30.20.4100.3121.3817.93
    10格尔木91.6,360.2560.2827.6720.14
    11若羌县91.4,38.80.2560.2726.6319.93
    12白银104.6,36.80.2600.2925.1319.16
    13常德111.8,29.40.1780.2714.6911.86
    14松原124.8,45.20.1760.3017.0813.55
    15锡林浩特116,440.0540.2921.4416.35
    16赤峰119,420.1420.2819.3214.21
    下载: 导出CSV

    表  4  k值场点期望距离计算结果

    Table  4.   Calculated expected distances for high k-value field points

    序号场点名称经纬度PGA475/gkPGA475期望距离/kmPGA2475期望距离/km
    1济宁116.6,35.40.0640.4346.0839.69
    2淮安119.2,33.40.0570.4150.5539.62
    3开封114.2,34.80.0830.4249.7147.54
    4晋城112.8,35.60.0590.4149.4739.00
    5沧州117.4,38.20.0630.4552.5343.20
    6东营118.6,37.40.0740.4250.9239.77
    7衡水116,37.60.0670.4249.1346.09
    8秦皇岛119.6,400.0690.4249.8847.00
    9鹤壁114.8,35.80.1160.4347.9244.00
    10济南117.2,36.60.0610.4242.6736.23
    11林周县91.4,300.1700.4558.5453.83
    12林芝94.6,300.1760.4343.2536.59
    13那曲地区92.2,31.60.1320.4960.9053.21
    14白银104.4,36.40.1190.4157.6656.34
    15临沧100,23.80.1780.4739.3934.42
    16凉山彝族自治州103.2,27.60.1340.5040.2235.96
    下载: 导出CSV
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  • 网络出版日期:  2022-10-18

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