Statistical techniques for the classification of chromites in diamond exploration samples

Abstract

A database of 1267 quality-screened major-and trace-element analyses of chromites (s.l.) from kimberlites, lamproites, ultramafic lamprophyres (UMLs) and crustal sources ('greenstones', including ophiolites, gabbros, basalts and komatiites) has been subjected to statistical analysis, in order to derive discriminants for use in diamond exploration. The techniques used included nearest-neighbour analysis, CART (classification and regression trees) and MARS (multivariate adaptive regression splines). The results show that both CART and MARS approaches can correctly discriminate kimberlite/lamproite chromites from UML/'greenstone' chromites at levels near 90%. Discrimination into the four classe..