Geostatistical independent simulation of spatially correlated soil variables

The selection of best management practices to reduce soil and water pollution often requires estimation of soil properties. It is important to find an efficient and robust technique to simulate spatially correlated soils parameters. Co-kriging and co-simulation are techniques that can be used. These methods are limited in terms of computer simulation due to the problem of solving large co-kriging systems and difficulties in fitting a valid model of coregionalization. The order of complexity increases as the number of covariables increases. This paper presents a technique for the conditional simulation of a non-Gaussian vector random field on point support scale. The technique is termed Independent Component Analysis (ICA). The basic principle underlining ICA is the determination of a linear representation of non-Gaussian data so that the components are considered statistically independent. With such representation, it would be easy and more computationally efficient to develop direct variograms for the components. The process is presented in two stages. The first stage involves the ICA decomposition. The second stage involves sequential Gaussian simulation of the generated components (which are derived from the first stage). This technique was applied for spatially correlated extractable cations such as magnesium (Mg) and iron (Fe) in a Canadian watershed. This paper has a strong application in stochastic quantification of uncertainties of soil attributes in soil remediation and soil rehabilitation.