Abstract
Accurate mapping of potentially mineralized zones is best undertaken on the basis of multi-source exploration data-sets. Mineral potential mapping is more difficult when only geological data are available. A 'wildcat' method of predictive mapping is presented for use in this situation. Scores of proximity classes of indicative geological features are employed in principal-component analysis to extract a favourability function that can be interpreted as a representation of mineral potential. The method was tested in the Baguio district of the Philippines. Knowledge of the spatial associations between the gold deposits and the indicative geological features in the test district was not applied in the predictive mapping. The maps delineated some 60% of the known gold deposits correctly.
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