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The geology of any given area is probably the single most important indicator of its mineral potential. In well-explored areas, the qualitative knowledge of the spatial correlations of known mineral deposits with the different geological features is the basis of most exploration programs. However, as the spatial correlation of mineral occurrences with geological features varies from place to place, a qualitative knowledge alone is inadequate for finding new deposits. A quantitative knowledge of the spatial correlation between known mineral occurrences and the different geological features present is equally important in mineral exploration.

Previous work on quantitative methods for mapping mineral potential, based on known mineral occurrences, predominantly used regression techniques (e.g., Chung and Agterberg, 1980; Harris, 1984). The known mineral occurrences in a region are used to develop a multivariate signature for mineralization, expressed as a vector of coefficients for the geological predictor variables. The coefficients are calculated using least squares regression; the resulting equation is used to generate regression scores whose magnitude reflect mineral potential. Regression techniques, however, are weak for a number of reasons. One is that they invariably assume that the relationship of the dependent variable (i.e., location of known mineral occurrences) to the predictor variables (i.e., geological features) is linear, which is not always valid. Another weakness of regression techniques is that, no assumption of, and consequently no test for, conditional independence between the predictor variables is required. 

An alternative approach to mapping mineral potential that avoids the limitations associated with regression techniques is to use Bayes' rule (e.g., Bonham-Carter et al., 1988; Agterberg et al., 1990). The Bayesian approach to the problem of combining multiple predictor variables uses a probability framework, that is, the idea of unconditional (prior) and conditional (posterior) probability. Starting with a prior probability of mineral deposits occurring in a unit area, a posterior probability is calculated based on the weights of evidence for the presence and absence of a predictor variable (Bonham-Carter et al., 1989). The weights of evidences for all predictor variables are combined in order to estimate the conditional probability of mineral occurrence given the presence and absence of all the binary predictor variables. Combining the weights of evidences of the different binary predictor maps requires an assumption that the input maps are conditionally independent. The application of a test for conditional independence of each pair of predictor maps with respect to the known mineral occurrences can lead to the rejection of some input maps.

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http://www.geovista.psu.edu/geocomp/geocomp99/Gc99/013/gc_013.htm

Geologically-constrained probabilistic mapping of gold potential, Baguio District, Philippines