reza ghezelbash

Definition of the efficient ore-forming processes which are considered as mineralization controls is a fundamental stage in mineral prospectivity mapping. In this contribution, five targeting criteria of geochemical, geophysical, geological, structural and hydrothemal alteration data related to epithermal and Carlin-type Au deposits in Takhte-soleyman district, NW Iran, were integrated. For creation of multi-element geochemical layer, principal component analysis was firstly conducted on stream sediment data of 8 selected elements and it was found that PC2 is the representative of Au-As-Sb elemental association in the study area. Then, 10 fuzzified efficient evidence layers were selected based on area under the curve (AUC) of success-rate curves and prepared for generation of predictive models of Au mineralization. For this purpose, two supervised machine learning algorithms, namely multi-layer perceptron (MLP) neural network and support vector machine (SVM) with RBF kernel were used. Comparison of the generated models demonstrates that the latter is more succeeded in delineating exploration targets than the former one.

Fuzzy analytical hierarchy process (Fuzzy-AHP) technique as a knowledge-driven approach relies on the geologist’s input to weight the importance of each evidence layer is applied for prospectivity modeling of Au mineralization within the Takhte-soleyman district. Firstly, in current study, all of the criteria and sub-criteria which are gained from exploratory data (geochemistry, remote sensing, geology and tectonic) are weighted by using AHP and thereafter are transformed into [0-1] range based on the fuzzy logic. All fuzzified layers according to the calculated weights were synthesized and prospectivity modeling of Au mineralization based on the Fuzzy-AHP technique was generated. In this study, it was used another fuzzy method based on the fuzzy operators (OR and GAMMA) that called “Fuzzy-GAMMA” to generate the map of promising areas of Au mineralization in the study area. Finally, in order to evaluate the ability of the applied methods in delineating reliable promising areas, locations of Au occurrences in the study area were used. Consequently, the results demonstrate the more acceptable outcomes for Fuzzy-AHP model by comparison with Fuzzy-GAMMA model.

Varzaghan district is located in NW of Arasbaran magmatic belt (AMB) which is one of the most highly mineralized region in Iran and host to a significant number of porphyry Cu deposits such as Sungun Cu-Mo porphyry deposit. The main goal of this study is synthesizing diverse raster-based evidence layers including geochemical, alteration and geological geo-data sets for mineral prospectivity modeling (MPM). For this purpose, firstly, continuous values of six favorable evidential maps as main criteria (geochemical signature of PC1 scores, values of proximity to argillic, phyllic and iron-oxide alterations, values of proximity to Oligo-Miocene intrusions and fault density) were divided into reasonable classes by applying concentration-area fractal model and then discretized layers were integrated using analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to generate a final map of porphyry Cu potential within the central part of Varzaghan district. Finally, the success-rate curve of the AHP-TOPSIS model as a quantitative evaluation method according to the locations of known Cu occurrences was drawn. Results revealed the successful performance of AHP-TOPSIS model in portraying the prospective areas related to porphyry Cu mineralization.