Bayesian analysis of earthquake seismology models under uncertainty

Hugo Cruz Jimenez, Paul Mai, E. E. Prudencio

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we apply a rigorous Bayesian methodology to quantitatively assess and compare, under uncertainty, rupture modeling hypotheses for computational earthquake seismology and advanced ground-motion simulations. Reliable ground motion predictions, with quantified uncertainty, are critical for designing large civil structures and response plans, thus helping to mitigate human and economical losses during earthquakes. With the advent of HPC and state-of-the-art parallel statistical algorithms, comprehensive uncertainty quantification of the expected shaking levels is now becoming possible. To demonstrate the potential of Bayesian methodology for the analysis of earthquake ground-motion simulations, this feasibility study proposes just two "simple" rupture modeling hypotheses, denoted M1 and M 2, which treat as random some of the physical parameters describing the fault properties. For each modeling hypothesis, ground-motions are computed at a dense virtual seismic network. Such ground motions are compared against a chosen empirical ground-motion prediction equation (GMPE, the reference data d). The ranking returns the hypothesis that best reproduces, among the proposed candidates M1 and M2, peak ground velocities with respect to d.

Original languageEnglish (US)
Title of host publication47th US Rock Mechanics / Geomechanics Symposium 2013
Pages3045-3054
Number of pages10
StatePublished - Dec 1 2013
Event47th US Rock Mechanics / Geomechanics Symposium 2013 - San Francisco, CA, United States
Duration: Jun 23 2013Jun 26 2013

Publication series

Name47th US Rock Mechanics / Geomechanics Symposium 2013
Volume4

Other

Other47th US Rock Mechanics / Geomechanics Symposium 2013
CountryUnited States
CitySan Francisco, CA
Period06/23/1306/26/13

Keywords

  • Bayesian analysis
  • Earthquake seismology
  • Model ranking
  • Statistical model calibration
  • Uncertainty quantification

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology

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