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Estimation of the upper bound of seismic hazard curve by using the generalised extreme value distribution

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  • V. A. Pavlenko

    (University of Pretoria Natural Hazard Centre)

Abstract

The problem considered in this study is that of unrealistic ground motion estimates, which arise in the Cornell–McGuire method when the seismic hazard curve is calculated for extremely low annual probabilities of exceedance. This problem stems from using the normal distribution in the modelling of the variability of the logarithm of ground motion parameters. In this study, the database of the strong-motion seismograph networks of Japan was used to examine the distribution of the logarithm of peak ground acceleration (PGA). The normal distribution and the generalised extreme value distribution (GEVD) models were considered in the analysis, with the preferred model being selected based on statistical criteria. The results of the analysis demonstrated the superiority of the GEVD in the vast majority of considered examples. The estimates of the shape parameter of the GEVD were negative in every considered example, indicating the presence of a finite upper bound of PGA. Therefore, the GEVD provides a model that is more realistic for the scatter of the logarithm of PGA, and the application of this model leads to a bounded seismic hazard curve.

Suggested Citation

  • V. A. Pavlenko, 2017. "Estimation of the upper bound of seismic hazard curve by using the generalised extreme value distribution," 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. 89(1), pages 19-33, October.
    • Handle: RePEc:spr:nathaz:v:89:y:2017:i:1:d:10.1007_s11069-017-2950-z
    DOI: 10.1007/s11069-017-2950-z
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    References listed on IDEAS

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    1. A. Lyubushin & T. Tsapanos & V. Pisarenko & G. Koravos, 2002. "Seismic Hazard for Selected Sites in Greece: A Bayesian Estimate of Seismic Peak Ground Acceleration," 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. 25(1), pages 83-98, January.
    2. V. Pavlenko, 2015. "Effect of alternative distributions of ground motion variability on results of probabilistic seismic hazard analysis," 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. 78(3), pages 1917-1930, September.
    3. Einmahl, J. H.J. & Dekkers, A. L.M. & de Haan, L., 1989. "A moment estimator for the index of an extreme-value distribution," Other publications TiSEM 81970cb3-5b7a-4cad-9bf6-2, Tilburg University, School of Economics and Management.
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    Cited by:

    1. Minakshi Mishra & Abhishek & R. B. S. Yadav & Manisha Sandhu, 2021. "Probabilistic assessment of earthquake hazard in the Andaman–Nicobar–Sumatra region," 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. 105(1), pages 313-338, January.
    2. Meng Zhang & Hua Pan, 2021. "Application of generalized Pareto distribution for modeling aleatory variability of ground motion," 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. 108(3), pages 2971-2989, September.

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