Geochemical data from soils in mineralised areas commonly have skewed and non-normal distributions. As such, raw soil geochemical data cannot be used for direct geostatistical analysis of spatial variability and interpolation without introducing additional uncertainties to any interpretation. The non-normal distributions will influence the robustness and fitting of Variograms to the data, and negatively influence the accuracy of any interpolations produced from these data. Therefore, prior to assessment, the dataset must be transformed to ensure that it has a normal distribution. Three transformations, namely the logarithmic, Boxa??Cox and Johnson transformations, were applied to As, Cd, Hg, Pb and Zn soil geochemical data from the Tongling metallogenic district, part of the Yangtze metallogenic belt, Anhui Province, China. The results of these transformations were analysed to determine the skewness of the data; and, using a Kolmogorova Smirnov test, how closely the transformed data approximate a normal distribution.
|Pages (from-to)||227 - 235|
|Number of pages||9|
|Journal||Applied Earth Science: Transactions of the Institutions of Mining and Metallurgy: Section B|
|Publication status||Published - 2010|