Bayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogate
Le Maître, Olivier P.
Mandli, Kyle T.
Dawson, Clint N.
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Earth Science and Engineering Program
Physical Sciences and Engineering (PSE) Division
KAUST Grant NumberCRG3-2156
Online Publication Date2017-04-07
Print Publication Date2017-08
Permanent link to this recordhttp://hdl.handle.net/10754/623798
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AbstractThis work addresses the estimation of the parameters of an earthquake model by the consequent tsunami, with an application to the Chile 2010 event. We are particularly interested in the Bayesian inference of the location, the orientation, and the slip of an Okada-based model of the earthquake ocean floor displacement. The tsunami numerical model is based on the GeoClaw software while the observational data is provided by a single DARTⓇ buoy. We propose in this paper a methodology based on polynomial chaos expansion to construct a surrogate model of the wave height at the buoy location. A correlated noise model is first proposed in order to represent the discrepancy between the computational model and the data. This step is necessary, as a classical independent Gaussian noise is shown to be unsuitable for modeling the error, and to prevent convergence of the Markov Chain Monte Carlo sampler. Second, the polynomial chaos model is subsequently improved to handle the variability of the arrival time of the wave, using a preconditioned non-intrusive spectral method. Finally, the construction of a reduced model dedicated to Bayesian inference is proposed. Numerical results are presented and discussed.
CitationGiraldi L, Le Maître OP, Mandli KT, Dawson CN, Hoteit I, et al. (2017) Bayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogate. Computational Geosciences. Available: http://dx.doi.org/10.1007/s10596-017-9646-z.
SponsorsThis work is supported by King Abdullah University of Science and Technology Award CRG3-2156.