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Pré-publication, Document de travail

Automatic variogram modeling by iterative least squares. Univariate and multivariate cases.

Nicolas Desassis 1, * Didier Renard 2
* Auteur correspondant
1 Équipe Géostatistique
GEOSCIENCES - Centre de Géosciences
Abstract : In this paper, we propose a new methodology to automatically find a model that fits on an experimental variogram. Starting with a linear com- bination of some basic authorized structures (e.g spherical, exponential,...), a numerical algorithm is used to compute the parameters which minimize a distance between the model and the experimental variogram. The initial values are automatically chosen and the algorithm is iterative. After this first step, parameters with a negligible influence are discarded from the model and the more parsimonious model is estimated by using the numerical algorithm again. This process is iterated until no more parameter can be discarded. A procedure based on a profiled cost function is also developped in order to use the numerical algorithm for multivariate data sets (possibly with a lot of variables) modeled in the scope of a linear model of coregionalization. The efficiency of the method is illustrated on several examples (including variogram maps) and on a multivariate case study.
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Pré-publication, Document de travail
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00707887
Contributeur : Nicolas Desassis <>
Soumis le : mardi 25 septembre 2012 - 15:45:25
Dernière modification le : jeudi 24 septembre 2020 - 16:34:06
Archivage à long terme le : : vendredi 31 mars 2017 - 13:36:13

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  • HAL Id : hal-00707887, version 2

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Nicolas Desassis, Didier Renard. Automatic variogram modeling by iterative least squares. Univariate and multivariate cases.. 2012. ⟨hal-00707887v2⟩

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