Comparer des méthodes

	Simulation géostatistique conditionnelle	Cokrigage
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	1997	1963
Auteur d'origine	Pierre Goovaerts; geostatistics tradition	Georges Matheron (geostatistics); multivariate extension
Type	Stochastic spatial simulation	Multivariate geostatistical interpolation
Source fondatrice	Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press. ISBN: 978-0-19-511538-3	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.
Alias	Sequential Gaussian Simulation, SGS, Stochastic Simulation, Koşullu Simülasyon	co-kriging, multivariate kriging, ortak kriging
Apparentées	2	3
Résumé	Conditional Geostatistical Simulation — most commonly implemented as Sequential Gaussian Simulation (SGS) — generates multiple stochastic realizations of a spatial random field that are each consistent with observed sample data and with a fitted variogram model. Unlike kriging, which produces a single smoothed estimate, SGS reproduces the full spatial variability of the phenomenon. It is widely used by geoscientists, mining engineers, petroleum engineers, and environmental scientists who need to propagate spatial uncertainty through downstream models.	Cokriging extends kriging to use one or more correlated secondary variables to improve prediction of a primary variable. When the variable of interest is sparsely sampled but a related, cheaper-to-measure variable is densely sampled, cokriging borrows strength from the secondary variable through their cross-correlation, yielding more accurate interpolations and prediction variances than kriging the primary variable alone.