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Capabilities of multivariate Bayesian inference toward seismic hazard assessment

Author

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  • Somayajulu L. N. Dhulipala

    (Idaho National Laboratory)

  • Madeleine M. Flint

    (Virginia Tech)

Abstract

Multivariate Bayesian inference can bring significant benefits to seismic hazard analysis: its multivariate feature enables computing scalar and vector hazard without making any approximations; Correlations between intensity measures are implicitly modeled, permitting direct simulation of ground motion selection tools such as the conditional mean spectrum and the generalized conditioning intensity measure. Its updating feature enables a seamless integration of new ground motion data into the hazard results. In this paper, we first develop a multivariate Bayesian ground motion model through the NGA-West2 database. The model functional form considers fault type, magnitude and distance dependencies, and also the linear and the rock intensity-dependent site response. We use a hybrid Markov chain Monte Carlo sampling to perform Bayesian inference consisting of Gibbs step and a multilevel Metropolis–Hastings step. We then perform several checks on the model to ensure that it is unbiased. Finally, we illustrate the merits of this multivariate Bayesian analysis through practical and contemporary examples, which include: ground motion model updating with ground motion data recorded in the last four years and not part of the NGA-West2 database; computation of scalar and vector seismic hazard using the un-updated and updated ground motion models for Los Angeles, CA; and simulation of the conditional mean spectrum under scalar and vector IM conditioning while accounting for different sources of aleatoric and epistemic uncertainties.

Suggested Citation

  • Somayajulu L. N. Dhulipala & Madeleine M. Flint, 2020. "Capabilities of multivariate Bayesian inference toward seismic hazard assessment," 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. 103(3), pages 3123-3144, September.
    • Handle: RePEc:spr:nathaz:v:103:y:2020:i:3:d:10.1007_s11069-020-04122-5
    DOI: 10.1007/s11069-020-04122-5
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    References listed on IDEAS

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    1. Enrique E. Alvarez, 2005. "Estimation in Stationary Markov Renewal Processes, with Application to Earthquake Forecasting in Turkey," Methodology and Computing in Applied Probability, Springer, vol. 7(1), pages 119-130, March.
    2. Irene Votsi & Nikolaos Limnios & George Tsaklidis & Eleftheria Papadimitriou, 2012. "Estimation of the Expected Number of Earthquake Occurrences Based on Semi-Markov Models," Methodology and Computing in Applied Probability, Springer, vol. 14(3), pages 685-703, September.
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