| Summary: | A well-known algorithm of clustering is K-means by which the data are divided into K classes based upon a distance criterion. In this study, we apply the K-means method for classifying data derived from exploration boreholes in the Parkam deposit. The optimum K has been calculated and then the data have been clustered and the relative geochemical behavioral characteristics analyzed. The criterion used for determining the optimum K ranged in number of classes from K = 3 to K = 10, and afterwards, we analyzed the derived classifications in order to choose the optimum K. Results showed that class numbers of K = 3 in the case of Cu and Mo, K = 4 in the case of Cu and Pb, and K = 3 in the case of Cu and Zn were optimized class numbers. After clustering, the increasing Cu grade values resulted in a significant increase in Mo grades, a significant decrease in Pb grades followed by an increase, and the Zn grades varying comparable to Pb. With regard to the relationships between these elements, it can be concluded that the meteoric waters promoted the mobilization of Pb and Zn from the potassic zone to the phyllic, but the meteoric waters were not effective enough to cause the mobilization of Cu, and this element together with Mo remained immobile.
|
|---|