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Multivariate methods such as factor analysis, hierarquical cluster analysis and k-means cluster analysis were employed to analyze soil geochemical data aiming to identify potential prospects in a gold mineralized area where outcropping ore is no longer available. Microprobe analysis was used to identify the elemental concentrations in each mineral phase. The main objective is to demonstrate the importance of adapting the methods to the specific geological reality at each studied site. An objective approach was adopted using a well-known mining area as control for analysis testing. The control area is surrounded by unexplored terrain, which was the target of this study. This unexplored terrain was covered by a soil grid of 2,908 samples, comprising an area of 88 km. Factor analysis was able to provide 5 correlation factors explaining 71.2% of the total variance. These factors identified distinct elemental associations with high correlations, influenced by the parental materials: ultramafic, mafic, pegmatitic, distal and proximal hydrothermal alteration. Hierarchical cluster analysis was able to correctly distinguish mafic/ultramafic from non-mafic/ultramafic influenced samples. K-means cluster analysis of a sub-dataset composed only of non-mafic samples provided groups of observations representative of country rock, distal alteration and proximal alteration. The above methods allowed for the identification of a geochemical fingerprint, defined by the elemental association Ba, Ca, La, Na, Pb and Sr, for distal alteration zones and enabled the mapping of new target areas for near-mine exploration. Spatial analysis of the clustering results, in comparison with the control area, was very effective in determining the best methods of clustering and data preparation. This process proved adequate for the determination of precise locations in the study

Análise multivariada de dados geoquímicos aplicada à exploração mineral de ouro: estudo de caso no distrito aurífero de Almas, TO, Brasil