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 classes separately is achieved at levels of ca. 80% by MARS and > 70% by CART; much of the misclassification is between the kimberlite and lamproite classes. These results probably represent the maximum probable level of discrimination on chemical criteria, given that ascending magmas may sample both mantle and crustal rocks. The CART approach produces a classification tree that requires no further computation to classify a given grain; the MARS approach requires the use of a simple software package. Tests on known samples illustrate the high level of accuracy of the methods in an exploration context, as well as the useful petrogenetic conclusions that can be drawn from some ‘misclassifications’.