IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v103y2020i3d10.1007_s11069-020-04122-5.html
   My bibliography  Save this article

Capabilities of multivariate Bayesian inference toward seismic hazard assessment

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-020-04122-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-020-04122-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Votsi, I. & Limnios, N. & Tsaklidis, G. & Papadimitriou, E., 2013. "Hidden Markov models revealing the stress field underlying the earthquake generation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(13), pages 2868-2885.
    2. Md. Asaduzzaman & A. Latif, 2014. "A parametric Markov renewal model for predicting tropical cyclones in Bangladesh," 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. 73(2), pages 597-612, September.
    3. Danisman, Ozgur & Uzunoglu Kocer, Umay, 2021. "Hidden Markov models with binary dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    4. Vlad Stefan Barbu & Nicolas Vergne, 2019. "Reliability and Survival Analysis for Drifting Markov Models: Modeling and Estimation," Methodology and Computing in Applied Probability, Springer, vol. 21(4), pages 1407-1429, December.
    5. 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.
    6. Battarra, Maria & Balcik, Burcu & Xu, Huifu, 2018. "Disaster preparedness using risk-assessment methods from earthquake engineering," European Journal of Operational Research, Elsevier, vol. 269(2), pages 423-435.
    7. D’Amico, Guglielmo & Petroni, Filippo, 2023. "ROCOF of higher order for semi-Markov processes," Applied Mathematics and Computation, Elsevier, vol. 441(C).
    8. Yanxing Zhao & H. Nagaraja, 2011. "Fisher information in window censored renewal process data and its applications," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(4), pages 791-825, August.
    9. He Yi & Lirong Cui & Narayanaswamy Balakrishnan, 2022. "On the Derivative Counting Processes of First- and Second-order Aggregated Semi-Markov Systems," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 1849-1875, September.
    10. William Mohanty & Alok Mohapatra & Akhilesh Verma, 2015. "A probabilistic approach toward earthquake hazard assessment using two first-order Markov models in Northeastern India," 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. 75(3), pages 2399-2419, February.
    11. Elsa Garavaglia & Raffaella Pavani, 2011. "About Earthquake Forecasting by Markov Renewal Processes," Methodology and Computing in Applied Probability, Springer, vol. 13(1), pages 155-169, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:103:y:2020:i:3:d:10.1007_s11069-020-04122-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.