Document Type: Research Paper
Geo-Exploration Targeting Lab (GET-Lab), School of Mining Engineering, College of Engineering, University of Tehran, Iran
School of Mining Engineering, College of Engineering, University of Tehran, Iran
Geo-Exploration Targeting Lab (GET-Lab), School of Mining Engineering, College of Engineering, University of Tehran, Iran.
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.