Spatial Variability Analysis of Cu Content

Abstract

Conventional variogram has been widely applied to study spatial variability of geochemical data. In case of data is not normally distributed, the conventional estimator is biased. In this study, Cressie variogram and Moran correlogram were used to identify the degree of spatial variabilty of Cu content using 1341 stream sediment samples in Jiurui copper mining area. Cressie variogram was applied to reduce the influences of high values in identifying spatial variability in different directions. Moran correlogram was employed to study spatial correlation at different distances and the influences of data distribution on the results in quantitative ways. It was found that Cressie variogram yields stable robust estimates of the variogram with the maximum spatial variability of 12km for all directions; Moran correlogram provided more information, directly viewed and stable than variogram. Moran correlogram identified a strong positive spatial correlation at distances below 6km for the raw data and a strong positive spatial correlation at distances below 11km for Box-Cox transformed data.

Analysis of Spatial Dependence of Ore-Forming Elements Using Geostatistics and Moran Correlogram

“…Let consider a spatial stochastic process * ( ) +, where is a fixed subset of . Assume that this stochastic process is ergodic and also satisfies the hypothesis of intrinsic stationarity, the expected (E) and variance (Var) values of ( ) are given by Hoang, et al [25] in Equations 1 and 2:…”

“…In Figure 2, from an observed study site , an aiming line is drawn in the direction determined by angle , the angular tolerance parameter determines the search zone (grey) laterally whereas parameter sets the tolerance along the aiming line for each distance class . Hoang, et al [25] indicated that the distance at which the variance levels off is referred to as the range (or maximum spatial variability), a; beyond that distance, the sampling units are not spatially correlated. The parameter for the nugget effect is and the spatially structured component is represented by ; the sill, , is equal to Figure 3.…”

“…Characteristic features a spherical semivariogram model. Source: Legendre and Legendre[24], Hoang, et al[25].…”