Improving the accuracy of detecting and ranking favorable porphyry copper prospects in the east of Sarcheshmeh copper mine region using a two-step sequential Fuzzy - Fuzzy TOPSIS integration approach

Abstract

The detection and prioritization of optimal favorable areas for the ground follow-up stage are among the most challenging issues in the early stages of any mineral exploration program. A common approach to identify the favorable mineralized zones is to create and integrate independent evidential predictor layers using knowledge or data driven approaches. The method proposed in current study is not only capable of detecting favorable zones, but also provides reliable ranking of the best favorable areas to focus in the next exploration stage. For this purpose, a two-step sequential Fuzzy-Fuzzy TOPSIS approach, which deploys the merits of both Multi-Criteria Decision Making (MCDM) and Fuzzy logic inference methodologies simultaneously, is proposed. In the first step, the favorable porphyry copper mineralizations in the east of the well-known Sarcheshmeh porphyry copper mine, are detected through combining evidential layers including geological, remote sensing data, geophysical and geochemical data using fuzzy logic integration approach. As a result of the first step, a number of twenty prospects with the highest porphyry copper favorability membership were selected and inputted into the TOPSIS and fuzzy-TOPSIS algorithms. Subsequently, the chosen prospects were prioritized and ranked according to their scores acquired by each technique of the aforementioned approaches separately. The performance of each approach was evaluated thorough comparison with the known ground porphyry copper mineralizations. The results indicated the capability of the proposed approach not only in detecting favorable porphyry copper mineralization prospects consistent with the previously detected porphyry Cu mineralization but also rank them based on their priorities.