MRQP: A windows-based mixed-language program for mineral resource quantitative prediction

Measurements that describe and summarize quantitative predictions relating to the classification of geological variables and samples derived from geochemical and geophysical data sets are important because of the large amount of information contained in such data. Methods considered in mineral resource quantitative prediction include quantification theory, which is able to deal with data including quantitative variables and qualitative variables simultaneously, including characteristic analysis and stepwise regression analysis. There are a wide variety of methods and statistical models that can be used in mineral resource assessment, for example: regression-based methods (Chung and Agterberg, 1980), logistic regression modeling (Agterberg, 1989b, 1974), fuzzy logic (Luo and Dimitrakopoulos, 2003; Bonham-Carter, 1994), Bayesian statistics (Bonham-Carter et al., 1988), indicator favorability modeling (Pan, 1993b), spatial factor analysis (Grunsky and Agterberg, 1992), characteristic analysis (McCammon et al., 1983), discriminant and characteristic analysis (Pan and Harris, 1992), canonical correlation analysis (Pan and Harris, 1992),