Publications 2017

Bayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surroga
. Giraldi, O. Le Maitre, K. Mandli. C. Dawson, I. Hoteit, and O. Knio
Computational Geosciences, doi:10.1007/s10596-017-9646-z, 2017
L. Giraldi, O. Le Maitre, K. Mandli. C. Dawson, I. Hoteit, and O. Knio
Uncertainty quantification, Bayesian inference, Polynomial chaos expansion, Noise model, Low-rank representation, Shallow water equation, Tsunami, Earthquake inversion
​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.

DOI: 10.1007/s10596-017-9646-z