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Evidence in support of seismic hazard following Poisson distribution

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  • Wang, J.P.
  • Chang, Su-Chin

Abstract

Unlike earthquake frequency that was proved following the Poisson distribution, seismic hazard (the annual rate of earthquake ground motions) is assumed to be the same type of random variables without tangible support. Instead of using total-probability algorithms currently employed, this study applied Monte Carlo Simulation (MCS) to obtain the probability function of seismic hazard, and then compared it to the Poisson distribution to see if it is really close to the model prediction as assumed. On the basis of a benchmark calculation, the analysis shows a very good agreement between the two, providing some evidence for the first time that seismic hazard should follow the Poisson distribution, although the relationship has been commonly employed in earthquake studies.

Suggested Citation

  • Wang, J.P. & Chang, Su-Chin, 2015. "Evidence in support of seismic hazard following Poisson distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 207-216.
  • Handle: RePEc:eee:phsmap:v:424:y:2015:i:c:p:207-216
    DOI: 10.1016/j.physa.2015.01.026
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    References listed on IDEAS

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    1. Noh, Yeelyong & Chang, Kwangpil & Seo, Yutaek & Chang, Daejun, 2014. "Risk-based determination of design pressure of LNG fuel storage tanks based on dynamic process simulation combined with Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 129(C), pages 76-82.
    2. Cadini, F. & De Sanctis, J. & Bertoli, I. & Zio, E., 2013. "Monte Carlo simulation of radionuclide migration in fractured rock for the performance assessment of radioactive waste repositories," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 241-247.
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    Cited by:

    1. Luigi-Ionut Catana & Vasile Preda, 2022. "A New Stochastic Order of Multivariate Distributions: Application in the Study of Reliability of Bridges Affected by Earthquakes," Mathematics, MDPI, vol. 11(1), pages 1-14, December.
    2. Zhang, Zhikun & Dai, Min & Wang, Xiangjun, 2023. "Statistical inference for mixed jump processes by Markov switching model with application to identify seismicity levels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    3. Y. Bulent Sonmezer & Ilker Kalkan & Selcuk Bas & S. Oguzhan Akbas, 2018. "Effects of the use of the surface spectrum of a specific region on seismic performances of R/C structures," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(3), pages 1203-1229, September.

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