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

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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.

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Acknowledgments

This work is supported by King Abdullah University of Science and Technology Award CRG3-2156.

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Authors and Affiliations

  1. Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Loïc Giraldi & Omar M. Knio

  2. LIMSI, CNRS, Université Paris-Saclay, Paris, France

    Olivier P. Le Maître

  3. Department of Applied Physics and Applied Mathematics, Columbia University, 500 W. 120th St., New York, NY, 10027, USA

    Kyle T. Mandli

  4. Institute for Computational Engineering and Science, University of Texas at Austin, 201 E 24th ST. Stop C0200, Austin, TX, 78712-1229, USA

    Clint N. Dawson

  5. Division of Physical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Ibrahim Hoteit

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  1. Loïc Giraldi
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  2. Olivier P. Le Maître
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Correspondence to Loïc Giraldi.

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Giraldi, L., Le Maître, O.P., Mandli, K.T. et al. Bayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogate. Comput Geosci 21, 683–699 (2017). https://doi.org/10.1007/s10596-017-9646-z

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  • DOI: https://doi.org/10.1007/s10596-017-9646-z

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