A Comparative Analysis of Index Overlay and Topsis (Based on Ahp Weight) for Iron Skarn Mineral Prospectivity Mapping, a Case Study in Sarvian Area, Markazi Province, Iran

Abstract

The aim of this research is to compare index overlay and TOPSIS (based on AHP weight) for predictive Skarn potential map. In this paper, for Calcic Iron Skarn mineralization, criteria and subcriteria introduced and ranked for generating mineral prospectivity map. The values of fi nal prospecting maps for Skarn deposit by index overlay and TOPSIS methods was specifi ed by dividing the prospectivity values into 10 classes. For better comparison, values assign to classes base on their priority in mineral exploration. The comparative analyses of index overlay and TOPSIS integration methods, has been performed by selecting four GCPs for fi eld checking. Field observation in GCP 1, 2 and 3, confi rmed Iron mineralization in the contact of intrusive bodies with sedimentary units, where the contact metamorphism was obvious but there is no observable mineralization in GCP4. Although high magnetic is distinct in mentioned GCP. Based on the fi eld checking in Sarvian area, the TOPSIS method has more accuracy compared to index overlay approach. Therefore, the TOPSIS method recommends for Calcic Iron Skarn Mineral Prospectivity Mapping in Sarvian and adjacent area.

Investigating for hydrocarbon potential in the Sarvak and Ilam Formations using Fuzzy logic in the Fars Region, Iran

“…1 and 2) (Mo'tamedi and Tahmasebi, 2009). The Relationship between sedimentary basins (e.g., Khavari et al, 2009;, 2012; Arian and Aram, 2014;Ehsani and Arian, 2015;Aram and Arian, 2016) and basement faulting (e.g., Arian, 2012;Nouri et al, 2013a,b;Mansouri et al, 2017;Nouri and Arian, 2017;Mansouri et al, 2018;Nabilou et al, 2018) indicates the role of faults in the control of sedimentary basins in Iran. In terms of lithofacies, especially the role of tectonics in the Iran sedimentary basin, Arian (2015), Razaghian et al (2018) and Taesiri et al (2020) divided all rocks into several large tectonic-stratigraphic units.…”

A spatial reconnaissance survey for gold exploration in a schist belt

“…The TOPSIS model initially developed by Hwang and Yoon [66], can be regarded as a multi criteria approach with extensive applications in mineral predictive mapping [26,27,67,68,69]. Usually, it is considered a simplistic knowledge driven model due to its ability in handling problems that relates to significant number of alternatives [70].…”

Prediction of gold mineralization zones using spatial techniques and geophysical data: A case study of the Josephine prospecting licence, NW Ghana

“…Knowledge-driven approaches that are mostly applied in the predictive modeling of a particular natural resource such as gold, copper, iron, hydrocarbons, groundwater etc. Comprise the analytical hierarchy process [ 34 , 76 , 84 ], analytical network process [ 68 ], ELECTRE [ 2 , 3 ], fuzzy analytical hierarchy process [ 14 , 20 , 45 , 51 ], best-worst method [ 42 ], PROMETHEE [ 4 ] and TOPSIS [ 59 ]. It is worthy to note that, knowledge-driven data integration methods are best suited in areas with little or no known occurrence of the sought-after mineral [ 45 ].…”