Bayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogate

Handle URI:
http://hdl.handle.net/10754/623798
Title:
Bayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogate
Authors:
Giraldi, Loic; Le Maître, Olivier P.; Mandli, Kyle T.; Dawson, Clint N.; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Knio, Omar
Abstract:
This 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Physical Sciences and Engineering (PSE) Division
Citation:
Giraldi 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.
Publisher:
Springer Nature
Journal:
Computational Geosciences
KAUST Grant Number:
CRG3-2156
Issue Date:
7-Apr-2017
DOI:
10.1007/s10596-017-9646-z
Type:
Article
ISSN:
1420-0597; 1573-1499
Sponsors:
This work is supported by King Abdullah University of Science and Technology Award CRG3-2156.
Additional Links:
http://link.springer.com/article/10.1007/s10596-017-9646-z
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorGiraldi, Loicen
dc.contributor.authorLe Maître, Olivier P.en
dc.contributor.authorMandli, Kyle T.en
dc.contributor.authorDawson, Clint N.en
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorKnio, Omaren
dc.date.accessioned2017-05-31T11:23:06Z-
dc.date.available2017-05-31T11:23:06Z-
dc.date.issued2017-04-07en
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.en
dc.identifier.issn1420-0597en
dc.identifier.issn1573-1499en
dc.identifier.doi10.1007/s10596-017-9646-zen
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.en
dc.description.sponsorshipThis work is supported by King Abdullah University of Science and Technology Award CRG3-2156.en
dc.publisherSpringer Natureen
dc.relation.urlhttp://link.springer.com/article/10.1007/s10596-017-9646-zen
dc.subjectBayesian inferenceen
dc.subjectEarthquake inversionen
dc.subjectLow-rank representationen
dc.subjectNoise modelen
dc.subjectPolynomial chaos expansionen
dc.subjectShallow water equationen
dc.subjectTsunamien
dc.subjectUncertainty quantificationen
dc.titleBayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogateen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalComputational Geosciencesen
dc.contributor.institutionUniversité Paris-Saclay, Paris, , Franceen
dc.contributor.institutionDepartment of Applied Physics and Applied Mathematics, Columbia University, 500 W. 120th St., New York, NY, 10027, , United Statesen
dc.contributor.institutionInstitute for Computational Engineering and Science, University of Texas at Austin, 201 E 24th ST. Stop C0200, Austin, TX, 78712-1229, , United Statesen
kaust.authorGiraldi, Loicen
kaust.authorHoteit, Ibrahimen
kaust.authorKnio, Omaren
kaust.grant.numberCRG3-2156en
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