As we know erratic and unstable shape of variograms could be caused by different reasons such as incorrectly chosen lag and/or directional tolerance, the complex geometry of orebody, strongly skewed distribution, containing outliers, preferential sampling from the high grade ore, proportional effect, etc. Between all those reasons outlier values are the main causes of instability of the variogram. Noisy variogram shows less structure and fitting a theoretical model on it is very difficult. Therefore, different procedures developed to mitigate the noisy behavior of variograms including using correlogram, pairwise relative variogram, and transforming the data to a normal standard distribution (µ=0 and σ2=1). Between these techniques the normal score transformation is the best alternative to measure the spatial continuity. However, the variogram of normal score could be used in Gaussian techniques such as Sequential Gaussian Simulation but it cannot be used directly in the ordinary kriging or other types of kriging. Therefore, it is necessary to back-transform the variogram model to the original unit. There are two techniques to back-transform the normal score variogram model to original unit, known as Monte Carlo Simulation and Hermite polynomials. These back-transformation techniques are available in some software packages like Leapfrog and Geovariances but in software that is available for me, Datamine Studio RM, I did not find any of these two techniques.

Instead of back-transforming the normal score variogram model I used the normal score data as well as normal score variogram model to estimate the variable in unsampled locations. Then I back-transformed the estimated distribution to original unit by using the original data distribution and its corresponding normal score data. I am not sure if this procedure is correct. I appreciate if I can get any comment.

Workflow for estimation using normal score data and back-transform it to original unit is presented in the attached picture.

Thanks

Esmaeel Ashrafpour