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dc.contributor.authorGiraldi, Loic
dc.contributor.authorLe Maître, Olivier P.
dc.contributor.authorMandli, Kyle T.
dc.contributor.authorDawson, Clint N.
dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorKnio, Omar
dc.date.accessioned2017-05-31T11:23:06Z
dc.date.available2017-05-31T11:23:06Z
dc.date.issued2017-04-07
dc.identifier.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.
dc.identifier.issn1420-0597
dc.identifier.issn1573-1499
dc.identifier.doi10.1007/s10596-017-9646-z
dc.identifier.urihttp://hdl.handle.net/10754/623798
dc.description.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.
dc.description.sponsorshipThis work is supported by King Abdullah University of Science and Technology Award CRG3-2156.
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/article/10.1007/s10596-017-9646-z
dc.subjectBayesian inference
dc.subjectEarthquake inversion
dc.subjectLow-rank representation
dc.subjectNoise model
dc.subjectPolynomial chaos expansion
dc.subjectShallow water equation
dc.subjectTsunami
dc.subjectUncertainty quantification
dc.titleBayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogate
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalComputational Geosciences
dc.contributor.institutionUniversité Paris-Saclay, Paris, , France
dc.contributor.institutionDepartment of Applied Physics and Applied Mathematics, Columbia University, 500 W. 120th St., New York, NY, 10027, , United States
dc.contributor.institutionInstitute for Computational Engineering and Science, University of Texas at Austin, 201 E 24th ST. Stop C0200, Austin, TX, 78712-1229, , United States
kaust.personGiraldi, Loic
kaust.personHoteit, Ibrahim
kaust.personKnio, Omar
kaust.grant.numberCRG3-2156
dc.date.published-online2017-04-07
dc.date.published-print2017-08


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