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Sensitivity analysis of spatial models using geostatistical simulation

Abstract : Geostatistical simulations are used to perform a global sensitivity analysis on a model Y = f(X1 ... Xk) where one of the model inputs Xi is a continuous 2D-field. Geostatistics allow specifying uncertainty on Xi with a spatial covariance model and generating random realizations of Xi. These random realizations are used to propagate uncertainty through model f and estimate global sensitivity indices. Focusing on variance-based global sensitivity analysis (GSA), we assess in this paper how sensitivity indices vary with covariance parameters (range, sill, nugget). Results give a better understanding on how and when to use geostatistical simulations for sensitivity analysis of spatially distributed models.
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Contributor : Nathalie Saint-Geours <>
Submitted on : Monday, February 6, 2012 - 11:22:48 AM
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Nathalie Saint-Geours, Christian Lavergne, Jean-Stéphane Bailly, Frédéric Grelot. Sensitivity analysis of spatial models using geostatistical simulation. Mathematical Geosciences at the Crossroads of Theory and Practice - IAMG 2011 Conference, Sep 2011, Salzburg, Austria. pp.178-189, ⟨10.5242/iamg.2011.0172⟩. ⟨hal-00666755⟩



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