- Geostatistics, Cressie semi-variogram, Moran correlogram, Geochemistry, Spatial autocorrelation, Spatial dependence, Spatial variability, Jiurui Copper districts (China).
The spatial dependence of data obtained from the geochemical prospecting process can provide useful information for evaluating mineralization potential. This study proposes two approaches to study the spatial dependence of ore-forming elements. To reduce the influence of extreme values and outliers, a semi-variogram was first used to study spatial variability and degree of spatial dependence of geochemical data using Cressie robust semi-variogram estimator. The Moran spatial correlogram was then employed to describe spatial heterogeneity and to test for the presence of spatial autocorrelation in geochemical data. The Moran’s I statistics is strongly sensitive to positively skewed distribution, therefore, geochemical data were Box-Cox transformed before computing spatial correlograms. Results from a case study of Ag and Au elements in Jiurui Copper districts (southeast China) have shown that moderate spatial dependence was found for both of the Au and Ag variables, the maximum spatial variability was 20 km for Au and 10 km for Ag, respectively. The degree of spatial dependence among geochemical data decreases as distances increase. These findings demonstrate that the spatial dependence of ore-forming elements can be effectively measured using geostatistics and Moran correlogram.