Hybrid modeling to predict the economic feasibility of mining undiscovered porphyry copper deposits

• Authors: Michael J. Friedel
• Venue: Applied Soft Computing
• Year: 2013

Few studies attempt to model the economic feasibility of mining undiscovered mineral resources given the sparseness of data; and the coupled, nonlinear, spatial, and temporal relationships among variables. In this study, a type of unsupervised artificial neural network, called a self-organized map (SOM), is trained using data from 203 porphyry copper deposit sites across the world. The sparse data set includes one dependent variable indicating the economic feasibility, and seventy two independent variables from categories describing characteristics of mining method, metallurgy, dimensions, economics, and amount. Analysis of component planes reveals relations and strengths in the underlying SOM multivariate density function which are used to impute missing values. Application of the Davies-Bouldin criteria to k-means clusters of SOM neurons identified 14 regional economic resource units (conceptual models). A best subsets approach applied to median values from these models identified 20 statistically significant combinations of variables. During model fitting by the multiple linear regression technique, only four of the empirical models had variables that were all significant at the 95% confidence level. The best model explained 98% of the variability in economic feasibility and incorporated variables describing distance to natural gas, road, and water; and the total amount of resources. This model was independently validated by comparing predictions of economic feasibility at 68 mine sites not included in the training data. Eighty-four percent of the reported economic feasibility is correctly predicted with 8 false positives and 2 false negative. We demonstrate the application of this model to a permissive copper porphyry tract that crosses a portion of British Columbia and Yukon territories of Canada. The proposed hybrid approach provides an alternative modeling paradigm for translating estimates of contained metal into meaningful societal measures.