A Monte Carlo approach to seismic hazard analysis

被引:2
|
作者
Ebel, JE [1 ]
Kafka, AL [1 ]
机构
[1] Boston Coll, Dept Geol & Geophys, Weston Observ, Weston, MA USA
关键词
D O I
暂无
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
We have developed a Monte Carlo methodology for the estimation of seismic hazard at a site or across an area. This method uses a multitudinous resampling of an earthquake catalog, perhaps supplemented by parametric models, to construct synthetic earthquake catalogs and then to find earthquake ground motions from which the hazard values are found. Large earthquakes extrapolated from a Gutenberg-Richter recurrence relation and characteristic earthquakes can be included in the analysis. For the ground motion attenuation with distance, the method can use either a set of observed ground motion observations from which estimates are randomly selected, a table of ground motion values as a function of epicentral distance and magnitude, or a parametric ground motion attenuation relation. The method has been tested for sites in New England using an earthquake catalog for the northeastern United States and southeastern Canada, and it yields reasonable ground motions at standard seismic hazard values. This is true both when published ground motion attenuation relations and when a dataset of observed peak acceleration observations are used to compute the ground motion attenuation with distance. The hazard values depend to some extent on the duration of the synthetic catalog and the specific ground motion attenuation used, and the uncertainty in the ground motions increases with decreasing hazard probability. The program gives peak accelerations that are comparable to those of the 1996 U.S. national seismic hazard maps. The method can be adapted to compute seismic hazard for cases where there are temporal or spatial variations in earthquake occurrence rates or source parameters.
引用
收藏
页码:854 / 866
页数:13
相关论文
共 50 条
  • [31] Seismic hazard analysis: An artificial neural network approach
    Arora, M
    Sharma, ML
    CURRENT SCIENCE, 1998, 75 (01): : 54 - 59
  • [32] A Convolutional Neural Network-Monte Carlo approach for petrophysical seismic inversion
    Aleardi, M.
    de Biasi, C.
    BULLETIN OF GEOPHYSICS AND OCEANOGRAPHY, 2022, 63 (02): : 189 - 214
  • [33] Analysis of typhoon wind hazard in Shenzhen City by Monte-Carlo Simulation
    GUO Yunxia
    HOU Yijun
    QI Peng
    JournalofOceanologyandLimnology, 2019, 37 (06) : 1994 - 2013
  • [34] Monte Carlo seismic response analysis of permafrost sites: a case study
    Yu, Xiaobo
    Zhang, Rui
    Cheng, Yushun
    Hu, Yuting
    NATURAL HAZARDS, 2022, 113 (01) : 237 - 259
  • [35] Analysis of typhoon wind hazard in Shenzhen City by Monte-Carlo Simulation
    Yunxia Guo
    Yijun Hou
    Peng Qi
    Journal of Oceanology and Limnology, 2019, 37 : 1994 - 2013
  • [36] Analysis of typhoon wind hazard in Shenzhen City by Monte-Carlo Simulation
    Guo Yunxia
    Hou Yijun
    Qi Peng
    JOURNAL OF OCEANOLOGY AND LIMNOLOGY, 2019, 37 (06) : 1994 - 2013
  • [37] Monte Carlo simulation for seismic analysis of a long span suspension bridge
    Sgambi, L.
    Garavaglia, E.
    Basso, N.
    Bontempi, F.
    ENGINEERING STRUCTURES, 2014, 78 : 100 - 111
  • [38] Monte Carlo analysis of seismic reflections from Moho and the W reflector
    Mosegaard, K
    Singh, S
    Snyder, D
    Wagner, H
    JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH, 1997, 102 (B2) : 2969 - 2981
  • [39] MONTE-CARLO ESTIMATION AND RESOLUTION ANALYSIS OF SEISMIC BACKGROUND VELOCITIES
    KOREN, Z
    MOSEGAARD, K
    LANDA, E
    THORE, P
    TARANTOLA, A
    JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH, 1991, 96 (B12) : 20289 - 20299
  • [40] Monte Carlo seismic response analysis of permafrost sites: a case study
    Xiaobo Yu
    Rui Zhang
    Yushun Cheng
    Yuting Hu
    Natural Hazards, 2022, 113 : 237 - 259