A spatial approach to mineral potential modelling using decision tree and logistic regression analysis

Logistic regression analysis and classification methods using decision tree analysis were used to generate two quantitative mineral potential maps for the Lake Ambrose area (NTS 12A/10) of central Newfoundland. The response variable consisted of 47 surface mineral occurrences plus 49 randomly selected sites representing nonmineral occurrences. Mineral deposit models and regional exploration methods were used to choose a set of predictors consisting of geology, fault proximity, till and lake sediment geochemistry, and surficial geology. A spatial weighting function predictor was developed to account for the clustering of the mineral occurrences.; The predictors were analyzed and recoded to derive a set useful in developing the quantitative models. The categorical geology predictor was converted into two binary predictors; felsic volcanics and mafic volcanics. Fault proximity was analyzed by the weights of evidence method to determine the optimal buffer threshold to convert the continuous distance values to a binary measure 'close to faults' versus 'far from faults'.; Mineral potential reliability maps were generated using the mutually exclusive and exhaustive regions from the decision tree analysis and the joint probability model for the logistic regression analysis. (Abstract shortened by UMI.)...