Compară metode

	Simulare Geostatistică Condiționată	Crinaj universal (Crinaj cu tendință)
Domeniu	Analiză spațială	Analiză spațială
Familie	Regression model	Regression model
Anul apariției	1997	1969
Autorul original	Pierre Goovaerts; geostatistics tradition	Georges Matheron
Tip	Stochastic spatial simulation	Geostatistical interpolation with spatial trend
Sursa seminală	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.
Denumiri alternative	Sequential Gaussian Simulation, SGS, Stochastic Simulation, Koşullu Simülasyon	kriging with a trend, kriging with drift, trend kriging, evrensel kriging
Înrudite	2	3
Rezumat	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.	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.