A variety of regional geoscience datasets from Nova Scotia have been co-registered and analyzed using a geographic information system (GIS). The datasets include bedrock and surficial geological maps, airborne geophysical survey data, geochemistry of lake-sediment samples, and mineral occurrence data. A number of line features, including structural lineaments, fold axes and formation contacts, have also been digitized. The GIS uses a quadtree structure, ideally suited to a mixture of polygonal-thematic (e.g., geological maps) and continuous "grey-scale" (e.g., remote sensing, airborne geophysics) raster images. The goal of the study was to create a map showing areas favorable for gold mineralization, based on the distribution of 70 known gold occurrences. Initially, a multi-element geochemical signature was generated using a regression analysis to find the linear combination of geochemical elements that best predict lake catchment bas ins containing a gold occurrence. A predicted gold occurrence map, based on the geochemistry alone, was produced. A method using Bayes' rule was applied to combine other factors important for gold prediction with the geochemical signature. A unique conditions map shows all those areas where a unique set of overlap between the predictor maps occurs. For each unique condition, an a posteriors probability was calculated, resulting in a map depicting probability of gold mineralization. This map confirms that the major known gold districts coincide with areas of high probability. Several new areas of high potential are indicated by the model, although exploration follow-up has not yet been carried out.
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