Porphyry Copper Mineral Prospectivity Mapping Using Interval Valued Fuzzy Sets Topsis Method in Central Iran

Abstract

Geospatial Information System (GIS) provide tools to quantitatively analysis and combination of datasets from geological, geophysical, remote sensing and geochemical surveys for decision-making processes. Excellent coverage of well-documented and good quality data enables testing of variable exploration modeling in an efficient way. The study area of this research is the most important part of Cu (Mo) porphyry-type mineralization belt in Iran. There are some well-known porphyry copper deposits in this region like Sarcheshmeh and Meiduk mines, but certainly there are same grounds to search for new porphyry deposits. The risks of developing mineral resources need to be known as accurately as possible, with regarding to all features those are effective in mineralization. These features can be recognized respect to Critical Genetic Factors (CGF's) using Critical Recognition Criteria (CRC) for each type of mineralization. CGF's can be employed for designing a Conceptual Genetic Model (CGM). Evidence maps create on the basis of CGM and then integrate together for production of Mineral Prospectivity Map (MPM). This map categorizes the areas based on their exploration importance. There are several techniques for creation of MPM. Interval Valued Fuzzy Sets (IVFSs) TOPSIS method was applied in this research. This method as a knowledge-driven method, allocate appropriate weights to layers on the basis of the effective membership, non membership, and non-certainty. The fundamental concept of TOPSIS is that the chosen alternatives should have the shortest distance from the positive ideal points (A * ) and the farthest distance from negative ideal points (A -).

Mineral potential mapping with a restricted Boltzmann machine

“…Knowledge-driven techniques use mineral-system approaches (Barnicoat, 2007;McCuaig et al, 2010;Wyborn et al, 1994) to identify, spatially quantify and then combine the formation of interested type of mineral deposits. They include Boolean logic (Bonham-Carter et al, 1989;Mukhopadhyay et al, 2003), index overlay (Bonham-Carter et al, 1989;Carranza et al, 1999;Mukhopadhyay et al, 2003), fuzzy logic (An et al, 1991;Brown et al, 2000;Carranza and Hale, 2001a;Chung and Moon, 1990;D'Ercole et al, 2000;Karimi et al, 2008;Knox-Robinson, 2000;Lusty et al, 2012;Molan and Behnia, 2013;Nykänen et al, 2008), wildcat mapping (Carranza, 2010;Carranza and Hale, 2002b), spatial factor analysis (Grunsky and Agterberg, 1989), multiple criteria decision-making (Abedi et al, 2012b(Abedi et al, , 2012c(Abedi et al, , 2013a, interval valued fuzzy sets topsis (Rad and Busch, 2011), and outranking method (Abedi et al, 2013b). These methods are commonly used in situations where there are not enough known mineral deposits in a study area.…”