Open image in new window Fig. 2 Permeability model generated using the variogram of Fig. 1.
The absence of a nugget effect in the variograms showed that there is no randomness in the sampled well population.
Bayesian analysis of the kriged estimates of log10(K) at Broom's Barn Farm using the Matérn variogram revealed an observable component of uncertainty due to variogram uncertainty.
Prominent groundwater and contaminant flow direction are revealed from the data sets of α-HCH, determined using the experimental variogram.
We used the theoretical variogram to fit the experimental variogram of spatial and regional residuals and the cross covariance between them.
Hence, the results of the control semi-variogram show the suitability of estimated by Kriging.
We investigated the spatial autocorrelation of the data by a variogram analysis using the Geostatistical Analyst extension of ArcGIS® 9.3 (Environmental Systems Research Institute 2009).
Statistical attributes of the contaminant used for the variogram modeling are given in Table 2 (Thakur 2015).
Also shown are the separate spatial and temporal sample variograms that were estimated using the sample surface, and the corresponding 1-D models.
Variograms were fitted using the 'nugget effect' and 'spherical model' structures.
