Compara mètodes

	Simulació Geostadística Condicional	Cokriging	Kriging Universal (Kriging amb Tendència)
Camp	Anàlisi espacial	Anàlisi espacial	Anàlisi espacial
Família	Regression model	Regression model	Regression model
Any d'origen	1997	1963	1969
Autor original	Pierre Goovaerts; geostatistics tradition	Georges Matheron (geostatistics); multivariate extension	Georges Matheron
Tipus	Stochastic spatial simulation	Multivariate geostatistical interpolation	Geostatistical interpolation with spatial trend
Font seminal	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.	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.
Àlies	Sequential Gaussian Simulation, SGS, Stochastic Simulation, Koşullu Simülasyon	co-kriging, multivariate kriging, ortak kriging	kriging with a trend, kriging with drift, trend kriging, evrensel kriging
Relacionats	2	3	3
Resum	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.	Universal kriging generalizes ordinary kriging to data whose mean varies systematically across space — a spatial trend or 'drift'. It models the mean as a function of the coordinates (or covariates) and krigs the residuals, so it can interpolate variables that drift in a preferred direction, such as temperature falling with latitude or a pollutant gradient, while still returning prediction variances.