Adeyinka Olasehinde
The present work presents integrated data-driven approach using geochemistry, aeromagnetic data, airborne radiometric data and Landsat ETM Imagery in the study. The study is aimed at producing mineral potential exploration model of the study area, which would serve as a model for other Younger Granite Complexes in the Province with unknown mineralization. The various exploratory dataset involve creating spatial database of the geology, geochemical data, the aeromagnetic (total field magnetic intensity), airborne radiometric (K, Th, U count), and Remote Sensing (Landsat ETM imagery). Principal Component Analysis (PCA) of fortytwo exploratory variables was employed in organizing and producing the mineral potential zones. The first three components of the Principal Component Analysis, account for over 50% of the total variability. The high, moderate to low moderate loading on the components represents background population of the study area and also reflects the combined effect of the geochemical, geophysical and the lithology, and hence mineral potential of the complex especially. The PC1 of the Riruwai data variability of 21.54% reflect alteration associated with mineralization of Sn, Zn, Nb, U, Th within the granitic rocks. The PC2 of the study area has data variability of 20.84% signifies increases in alteration within the lithology especially the biotite granites and albite arfvedsonite granite. Data variability of 10.78 is accounted by PC3 and its significance is interpreted as pyrochlore mineralization within the albite arfvedsonite granite. The study, not only predict known areas of mineral occurrence (Sn, Nb, Ta, Zn, Zr, U, Th), but also identified areas of favourable mineralization potential. This approach can thus be applied to areas with similar geological setting but unknown mineralization.
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