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Original scientific paper

https://doi.org/10.17794/rgn.2024.3.10

KNOWLEDGE DRIVEN METHODS FOR CU-AU PORPHYRY POTENTIAL MODELLING; A CASE STUDY OF THE MOKHTARAN AREA, EASTERN IRAN

Moslem Jahantigh ; Faculty of Mining engineering, Amirkabir University of Technology, Hafiz Street, Tehran, Iran
Hamidreza Ramazi ; Faculty of Mining engineering, Amirkabir University of Technology, Hafiz Street, Tehran, Iran


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Abstract

Current research investigates multi-criteria decision methods, consisting of AHP TOPSIS, AHP VIKOR and AHP MOORA, to model porphyry copper potential in the Mokhtaran area in Eastern Iran. Evidential layers in this study include intrusive rocks, volcanic rocks, faults, Geochemical mineralization probability index (GMPI), reduction to the magnetic pole of the total magnetic intensity map, argillic and phyllic alterations. The importance of these evidential layers was calculated using the AHP method. Then, a fuzzy method was applied to the same scale the evidential layers. The threshold values of these layers were discretized with the Fractal method. Then, a weight was assigned to each evidential layer. After weighing all of the evidential layers, different MCDM methods, including AHP TOPSIS, AHP VIKOR, and AHP MOORA, were implemented to combine these layers and outline the Porphyry Copper Prospectivity Models. The predicted models show the same promising areas. The appropriate coincidence can be seen between high potential areas and mine indications. Then the success curve rate was implemented to compare the three predicted models. Based on this method, the AHP TOPSIS has a better performance. Since the success rate curve belongs to AHP TOPSIS, it is placed above the other two methods. Next, AHP VIKOR has a better performance than AHP MOORA. The three MCDM methods produced the same Cu porphyry mineralization areasd along fault zones.

Keywords

AHP; MOORA; VIKOR, Mokhtaran; Porphyry

Hrčak ID:

319104

URI

https://hrcak.srce.hr/319104

Publication date:

4.7.2024.

Article data in other languages: croatian

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