A clustering approach for mineral potential mapping: A depositscale porphyry copper exploration targeting

This work describes a knowledge–guided clustering approach for mineral potential mapping (MPM), by which the optimum number of clusters is derived form a knowledge–driven methodology through a concentration–area (C–A) multifractal analysis. To implement the proposed approach, a case study at the North Narbaghi region in the Saveh, Markazi province of Iran, was investigated to discover porphyry Cu–bearing favorability zones. Whereby, various exploratory indicators were extracted from a multidisciplinary geospatial data set comprising of geology, geophysics and geochemistry criteria. Those indicators were prepared from magnetometry and geo– electrical survey, lithogeochemical samples and geological field operation. The optimum number of clusters was obtained by running the knowledge–based methods of index overlay and fuzzy gamma operators, indicating five clusters from the C–A multifractal curve. Accessing to exploratory drilling lets us to find out the most efficient synthesized favorability map that was generated by a fuzzy algebraic sum operator (or a gamma value equal to one). Assuming the optimum number of clusters, three clustering methods, namely fuzzy C–means (FCM), K–means and self–organizing map were examined for MPM. Note that the FCM as an unsupervised data– driven methodology, had superiority over other clustering analyses by generating mineral favorability map in close association with drilling results.