The image shown on the cover of Computers & Geosciences shows the results after integrating a multivariate analysis of geochemical data with digital topographic data.
Multi-element geochemical data can be effectively analyzed and interpreted through the use of multivariate data analysis, 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: represented by recent volcanic ash, and a saprolitic soil profile containing a mineralized zone of Cu associated with mafic volcanic rocks.
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 prepared using aqua regia digestion and a analyzed using ICP-EOS finish.
The results of the application of multivariate methods highlight common element associations and distinct sample populations from which an index of lithology soil type (lithological discrimination between saprolite and ash) and an index of potential Cu mineralization were observed. Two distinctive populations are displayed in Figure 2; one group showing a trend towards Cu enrichment along the Y-axis. Along the X-axis, another group of samples show little dispersion relative to those samples associated with Cu-enrichment. This group represents material that is interpreted to be volcanic ash that overlies the saprolitic soils.
Figures 3 and Figures 4 show the results of interpolating and imaging these two indices over the 1665 samples. Figure 3 shows a clear northwest trending pattern associated with Cu enrichment and mafic rocks. This pattern is coincident with the regional stratigraphy, which also trends northwesterly. Figure 4 is a plan view of the volcanic ash indices, and is difficult to interpret in the context of known stratigraphy, alteration or and mineralization.
The interpretation of these images is significantly enhanced when integrated with a digital elevation model of the area. The elevation ranges from 1180 to 1350 meters. Figure 5 displays a zone of elevated Cu enrichment associated with mafic volcanic rocks trending northwesterly along the western slopes and coincident with the regional stratigraphy. Figures 6 shows that the population of samples, interpreted to be volcanic ash from the right side of Figure 2 occurs along hill tops and the eastern slopes of the hills. This interpretation is supported by observations of the sampled materials and reports by geologists working in Indonesia where this phenomenon is commonly observed. The application of fractal methods also shows that the distribution of the ash represents a unique spatial pattern which that is distinct from the pattern associated with the regional stratigraphy.
This example highlights the effective use of multivariate statistical methods for distinguishing between different sample media as well as the isolation of geochemical trends that define zones of possible mineralization. The use of these types of multivariate methods isolates relationships of the elements that are difficult or impossible to see by examining individual elements. The application of multivariate techniques integrated with digital elevation models provides a more effective way of visualizing and interpreting ICP produced analyses.the data.
Additional details about the application of these methods will be made available in the future. For further information contact the authors. The image that is used for the cover for Computers & Geosciences is shown in Figure 7.
