Subsurface Clay Prediction in an Arid Area

This paper attempts to estimate and map the clay content of soil Substrata by using some innovative inferences in UK system. Therefore, robustness is inferred in trend analysis and variography of UK system. The variogram parameters are estimated also by maximum likelihood (ML) and restricted maximum likelihood (REML) methods to reduce the probability of biasness of robust variography. A restriction is added to UK system not to predict outside the physical range. Interpolating the clay amount (%) in second soil layer was carried out on transformed (arc sin (y1/2)) data. The landform map was the only remaining fixed effect in the regression model with R2 = 58. The x-validation analysis proved that using RE(ML) methods to estimate the covariance parameters gives more realistic results and avoid the bias existing in robust methods of parameter estimation. Due to non-linearity of back transformation formula of kriging variance, instead of calculating the standard error image the lower and upper confidence interval boundaries of predicted variance was calculated for a 0.975 probability.