Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

Estimation of Space Deformation Models for Non-stationary Random Functions

Francky Fouedjio 1, * Nicolas Desassis 1 Thomas Romary 1
* Auteur correspondant
1 Équipe Géostatistique
GEOSCIENCES - Centre de Géosciences
Abstract : Stationary Random Functions have been successfully applied in geostatistical applications for decades. In some instances, the assumption of a homogeneous spatial dependence structure across the entire domain of interest is unrealistic. A practical approach for modelling and estimating non-stationary spatial dependence structure is considered. This consists in transforming a non-stationary Random Function into a stationary and isotropic one via a bijective continuous deformation of the index space. So far, this approach has been sucessfully applied in the context of data from several independent realizations of a random function. In this work, we propose an approach for non-stationary geostatistical modelling using space deformation in the context of a single realization with possibly irregularly spaced data. The estimation method is based on a non-stationary variogram non-parametric kernel estimator which serves as a dissimilarity measure between two locations in the geographical space. The proposed procedure combines aspects of kernel smoothing, weighted non-metric multi-dimensional scaling and thin-plate spline radial basis functions. These tools allow to transform the original non-stationary Random Function toward a new space where it is stationary and isotropic. Stationary techniques for spatial prediction and simulation can be applied in this new space. The predicted and simulated results are then mapped back into the original space, by simple correspondance. \sep multi-dimensional scaling. On a simulated data, the method is able to retrieve the true deformation. Performances are assessed on both synthetic and real datasets. It is shown in particular that our approach outperforms the stationary approach. Beyond the prediction, the proposed method can also serve as a tool for exploratory analysis of the non-stationarity.
Type de document :
Communication dans un congrès
Liste complète des métadonnées

https://hal-mines-paristech.archives-ouvertes.fr/hal-01067676
Contributeur : Francky Fouedjio <>
Soumis le : mardi 23 septembre 2014 - 18:20:07
Dernière modification le : jeudi 24 septembre 2020 - 16:34:10

Identifiants

  • HAL Id : hal-01067676, version 1

Citation

Francky Fouedjio, Nicolas Desassis, Thomas Romary. Estimation of Space Deformation Models for Non-stationary Random Functions. 21st International Conference on Computational Statistics, Aug 2014, Genève, Switzerland. ⟨hal-01067676⟩

Partager

Métriques

Consultations de la notice

230