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

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    Type
    Article
    Authors
    Giraldi, Loic cc
    Le Maître, Olivier P.
    Mandli, Kyle T.
    Dawson, Clint N.
    Hoteit, Ibrahim cc
    Knio, Omar cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    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
    KAUST Grant Number
    CRG3-2156
    Date
    2017-04-07
    Online Publication Date
    2017-04-07
    Print Publication Date
    2017-08
    Permanent link to this record
    http://hdl.handle.net/10754/623798
    
    Metadata
    Show full item record
    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.
    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.
    Sponsors
    This work is supported by King Abdullah University of Science and Technology Award CRG3-2156.
    Publisher
    Springer Nature
    Journal
    Computational Geosciences
    DOI
    10.1007/s10596-017-9646-z
    Additional Links
    http://link.springer.com/article/10.1007/s10596-017-9646-z
    ae974a485f413a2113503eed53cd6c53
    10.1007/s10596-017-9646-z
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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