Abstract

The development of low-cost, rapid multielement analytical techniques has generated large geochemical databases in many exploration programs. When a sampling program consists of several thousand samples, the resulting data matrix is enormous and effective interpretation using all of the elements individually becomes burdensome.

CIMM Annual Meeting Abst.

Sorry, CIMM Annual Meeting proceedings not available to the public.

using multivariate methods and digital topography.

Abstract

Multi-element geochemical data can be effectively interpreted through the application of multivariate statistical techniques, imaging methods and integration with digital topographic information. These techniques have been applied to a suite of 1665 soil samples collected in a sampling program from the central Sumatra area of Indonesia. The selected samples were analyzed for Au, Cu, Pb, Zn, As, Sb, Ba, Ca, Cd, Co, Cr, Fe, Ga, K, La, Li, Mg, Mn, Nb, Ni, Sc, Sr, Ti, V, Y, Zr and Hg using aqua-regia digestion followed by ICP–IES determination.

Multi-element geochemical data can be effectively interpreted through the use of multivariate statistical techniques, imaging methods and merging with digital topographic information. This is illustrated using the results of a geochemical sampling program in Indonesia. Difficulties were encountered when the interpretation of selected elements was attempted. Patterns appeared to be discontinuous and erratic. However the application of multivariate statistical methods identified two distinct geochemical associations: recent volcanic ash, and a saprolitic soil profile containing a_mineralized zone of Cu associated with mafic volcanic rocks. Maps and figures are shown on Page 20.