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Stochastic data assimilation of the random shallow water model loads with uncertain experimental measurements

Abstract : This paper is concerned with the estimation of a parametric probabilistic model of the random displacement source field at the origin of seaquakes in a given region. The observation of the physical effects induced by statistically independent realizations of the seaquake random process is inherent with uncertainty in the measurements and a stochastic inverse method is proposed to identify each realization of the source field. A statistical reduction is performed to drastically lower the dimension of the space in which the random field is sought and one is left with a random vector to identify. An approximation of the vector components is determined using a polynomial chaos decomposition, solution of an optimality system to identify an optimal representation. A second order gradient-based optimization technique is used to efficiently estimate this statistical representation of the unknown source while accounting for the non-linear constraints in the model parameters. This methodology allows the uncertainty associated with the estimates to be quantified and avoids the need for repeatedly solving the forward model.
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Contributor : Christophe Desceliers Connect in order to contact the contributor
Submitted on : Friday, November 9, 2012 - 10:55:17 AM
Last modification on : Monday, July 18, 2022 - 1:24:39 PM
Long-term archiving on: : Sunday, February 10, 2013 - 3:40:38 AM


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  • HAL Id : hal-00750190, version 1


L. Mathelin, Christophe Desceliers, M.Yussuf Hussaini. Stochastic data assimilation of the random shallow water model loads with uncertain experimental measurements. Computational Mechanics, Springer Verlag, 2011, 47 (6), pp.603-616. ⟨hal-00750190⟩



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