E. C. Grunsky

Integration of topography with multielement geochemistry

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. Figure 1 shows the soil sampling grid from which 1,665 samples were collected and 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. The samples were analyzed using aqua regiadigestion and an ICP-ES finish. The results of the application of multivariate methods highlight common element associations and distinct sample populations from which an index of soil type (discrimination between saprolite and ash) and an index of potential Cu mineralization were observed. Two distinctive populations are displayed in Figure 2; one group shows a trend towards Cu enrichment along the Y-axis. Along the X-axis, another group of samples...

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The differentiation of sample media types and mineralization from multi-element geochemistry using multivariate methods and digital topography

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. Plan maps of individual elements proved difficult to interpret with several elements displaying bimodal populations. The spatial patterns of individual elements appeared to be discontinuous and raised suspicion that the bimodal population reflected differences in sample media rather than features related to lithology or mineralization. Statistical methods were employed to test the hypothesis of differences in sample media types. Principal components analysis identified several distinct element associations and populations. The scores of the samples for each principal component were interpolated, imaged and plotted as plan maps. The first principal component was difficult to interpret in plan view and did not appear to reflect any lithological or known mineralization trends. It was concluded that the patterns associated with the first component may reflect differences...

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Enhancements in the Interpretation of Geochemical Data using Multivariate Methods and Digital Topography

The development of low-cost, rapid multi-element 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. However, the application of multivariate statistical techniques can extract geochemical patterns related to the underlying geology, weathering, alteration and mineralization. Imaging the results over topography enhances the interpretation of these patterns. Examples of this approach are shown from mineral exploration programs in Canada, Mexico and Indonesia. Introduction There has been a dramatic change in the effectiveness of using geochemical survey data in the past 10 years. This is mostly due to: 1) Lower cost of geochemical analysis, 2) Lower detection limits, 3) New methods of multi-element analysis, 4) A marked decrease in the turnaround time, 5) The intelligent use of computers in presenting geochemical data. Geochemical surveys commonly have two objectives: locating abnormal concentrations of ore-forming or pathfinder elements and characterizing the underlying host lithologies. Geochemical surveys often employ a wide range of sample media that may typically include whole rock lithogeochemistry, various soil horizons, till and basal till sampling, stream sediments, lake sediments, and various forms of weathered regolith. Each of these sample media will...

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Enhancements in the interpretation of geochemical data using multivariate methods and digital topography

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. However, the application of multivariate statistical techniques can extract geochemical patterns related to the underlying geology, weathering, alteration and mineralization. Imaging the results over topography enhances the interpretation of these patterns. Examples of this approach are shown from mineral exploration programs in Canada and Mexico. CIM Bulletin, Volume 96, Number 1068, February 2003,...

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