Effect of Number of Exploration Criteria in Data-driven Mineral Potential Mapping Approaches

This paper investigates the effect of the number of input exploration control layers on the prediction ability of the ensuing mineral potential models. For this purpose, several weighted control layers were first produced through different exploration methods. Then, the layers were combined in two individual procedures by using two and three input control layers. The prediction rates were evaluated and compared with the location of known mineral occurrences. In addition, this paper reviews improved multi-class data-driven index overlay for Mineral potential mapping (MPM) and identifying the promising areas, and then, uses the method to identify the exploration targets for lead, zinc, and copper skarn mineralization in the Mahneshan area in Zanjan province, west of Iran. The exploration control layers, including the geological, fault density, and geochemical maps, were produced and integrated for this purpose. A Critical limitation of this method, which is weighting each class of geochemical and fault density maps without expert judgment, was resolved in this paper. After producing the weighted evidential maps of each layer, in order to evaluate the relative importance of different exploration methods, weights were attributed to the layers to evaluate their relative importance in terms of their ability to predict undiscovered Pb-Zn-Cu skarn mineralization. Finally, the three layers were combined using the multi-class index overlay method in which the data were categorized into different classes to determine exploration targets. The areas with high mineralization potential produced in the final mineral potential model properly predict the existing mineral occurrences in the region studied. Also, new areas were identified that could be explored in more detail.

کلیدواژه ها:

Mineral potential mapping ، Multi-Class Data-driven Index Overlay ، Skarn mineralization ، Mineral Potential Model ، Prediction of Undiscovered Pb-Zn-Cu