Robust Estimation Based on Lognormal Kriging Technique for Some Soil Data

This paper dealt with the study of the analysis and interpretation of spatial variability using the Kriging technique in geostatistics.The objectives of this work are; to interpolate the values of regionalized variables; to express the spatial variation after using the logarithmic transformed for the original scale; observations for two groups of soil data; To reduce the level of pollution in the soil by studying the characteristics of the estimates.The ordinary kriging procedure is used to estimate the best linear unbiased estimator.The experimental semi-variogram function is applied as a tool to give the idea of spatial distribution after using the logarithmic transformations of the origin data.This method assumes the isotropy.Also, a robust estimate (Matheron's and Haslett's, Cressie-Hawkins) was applied to minimize some prediction scores.Data adopted in this work is taken from Mosul city in Iraq, for some soil spatial real data.Each data contains (100) real soil data of (PH) and (NO3).Our finding results illustrate the variance is itself for all directions of the compass: East-west, North-south, Northeast, and Northwest.The model describes (94%) nearest the Gaussian model of (PH), and (92%) nearest the spherical model of the total variability of (NO3) after comparing the results models between the original scale and the lognormal data by obtain the fitting model of soil data with the formulas of kriging.In conclusion, we show the qualities of the estimation rely on the ratio distances.Behaviors of continues of the phenomenon or observations and low coefficient of variation, which leads to improved efficiency in spatial distribution The support of results show that Matheron's and Haslett's robust estimators had better performance than Cressie Hawkins's robust soil data comparison with the curves of variogram function, because the small effect of outlier values on the estimates it is clear from this effect that pollution may be large by correctly knowing the weight restrictions for the level of pollution, and reducing the level of pollution depends practically and for a long period on the stationarity of some estimates.All computations are carried out in Matlab language.