Bayesian inference of earthquake rupture models using polynomial chaos expansion
KAUST DepartmentApplied Mathematics and Computational Science Program
Computational Earthquake Seismology (CES) Research Group
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Earth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/628386
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AbstractIn this paper, we employed polynomial chaos (PC) expansions to understand earthquake rupture model responses to random fault plane properties. A sensitivity analysis based on our PC surrogate model suggests that the hypocenter location plays a dominant role in peak ground velocity (PGV) responses, while elliptical patch properties only show secondary impact. In addition, the PC surrogate model is utilized for Bayesian inference of the most likely underlying fault plane configuration in light of a set of PGV observations from a ground-motion prediction equation (GMPE). A restricted sampling approach is also developed to incorporate additional physical constraints on the fault plane configuration and to increase the sampling efficiency.
CitationCruz-Jiménez H, Li G, Mai PM, Hoteit I, Knio OM (2018) Bayesian inference of earthquake rupture models using polynomial chaos expansion. Geoscientific Model Development 11: 3071–3088. Available: http://dx.doi.org/10.5194/gmd-11-3071-2018.
SponsorsResearch reported in this publication was supported in part by research funding from King Abdullah University of Science and Technology (KAUST). The first author thanks KAUST for all the support during his postdoctoral fellowship. Earthquake rupture and ground-motion simulations have been carried out using the KAUST Supercomputing Laboratory (KSL) and we acknowledge the support of the KSL staff.
JournalGeoscientific Model Development
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