方法对比

	条件地质统计学模拟	共克里金
领域	空间分析	空间分析
方法族	Regression model	Regression model
起源年份	1997	1963
提出者	Pierre Goovaerts; geostatistics tradition	Georges Matheron (geostatistics); multivariate extension
类型	Stochastic spatial simulation	Multivariate geostatistical interpolation
开创性文献	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.
别名	Sequential Gaussian Simulation, SGS, Stochastic Simulation, Koşullu Simülasyon	co-kriging, multivariate kriging, ortak kriging
相关	2	3
摘要	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.