Multivariate statistical techniques applied to pisolitic laterite geochemistry at Golden Grove, Western Australia

Abstract

Abstract Multivariate statistical procedures are applied to pisolitic laterite geochemistry in a study of the Golden Grove massive sulphide district. The objective is to optimize identification of geochemical anomalies caused by base metal mineral deposits. The statistical approach used in this paper depends upon geochemical data for appropriate reference groups (or training sets) being available. The target group consists of orientation data from pisolitic laterite about the Gossan Hill Cu-Zn massive sulphide deposit. A group representing background sequence was selected by combining three subareas in a geochemically quiet part of the prospective acid volcano-sedimentary sequence. A multi-element allocation procedure was set up using data from the reference groups. The exploration samples are then allocated, one sample at a time, to either one of the reference group categories, using the probability of group membership . A map showing the relative probability values for each sample site is the final product for interpretation, aided by ancillary use of an index of typicality . The allocation procedures were carried out using different element combinations, these being based on a procedure for subset selection to give maximum separation of reference groups, and on geochemical insight. Whilst many versions of the allocation procedure gave positive identification of the anomaly related to the blind Scuddles Cu-Zn deposit, allocation using only Cu, Pb, Zn and Ag did not. The results emphasize the importance of pathfinder elements in geochemical studies in weathered terrain. The allocation procedure using the most appropriate element combinations provided more positive identification of the main areas of known mineralization than had the previously used empirically derived methods of Smith and Perdrix (1983). The formal allocation procedure has the following additional advantages: results are not markedly affected by a very high value for any single element since robust procedures are incorporated into the analysis; better discrimination appears to be possible for weaker anomalies; separation of target from background can be optimized by formal calculations instead of by trial and error; and better suppression of background variation results.