Comparison of Subjective and Objective Methods for the Spatial Estimation of the Porphyry Cu Potential in Ahar-Arasbaran Area, North-Western Iran

Bollettino di Geofisica Teorica ed Applicata Vol. 57, n. 4, pp. 343-364; December 2016 DOI 10.4430/bgta0181 Comparison of subjective and objective methods for the spatial estimation of the porphyry Cu potential in Ahar-Arasbaran area, north-western Iran K. PAZAND1 and A. HEZARKHANI2 1 Young Researchers and Elite Club, Science and Research Branch, Islamic Azad University, Tehran, Iran 2 Dept. of Mining, Metallurgy and Petroleum Engineering, Amirkabir University, Tehran, Iran (Received: February 1, 2016; accepted: October 18, 2016) ABSTRACT Geographic Information System (GIS) are very useful tools for managing, checking, and organizing spatial information from many sources and of many types in thematic layers. In this paper, using GIS, we describe and compare different models for the estimation of porphyry copper potential maps in Ahar-Arasbaran zone, north-western Iran. Final potential mapping of the study area was assessed according to the advantages and disadvantages of each method and a combination of them. The results demonstrate that the Analytic Hierarchy Process (AHP) method provides the best output with the lowest potential area and the highest point validation coverage, and highlights a new potential area of porphyry copper mineralization confrmed by feld check. Key words: potential mapping, porphyry, Ahar-Arasbaran, GIS. 1. Introduction The presence of mineral resources is an important guarantee for a country to experience a sustainable and fast development of its national economy. It has been a signifcant scientifc topic for research, among scientists all over the world, to obtain a clear view of these resources (Guangsheng et al., 2007). The preliminary quantitative prediction and assessment for mineral resource exploration is based on multi-information (geology, physical geography, geochemistry, and remote sensing), synthetic technology, and metallogenetic theory (Gongwen and Jianping, 2008). Geographic Information Systems (GIS) are very useful tools for managing spatial information and are frequently used to evaluate mineral potential in exploration districts and provide tools to deal with multiple data sets, or layers, of diverse character from various sources (Bonham-Carter, 1994; Pan and Harris, 2000; de Araujo and Macedo, 2002; Roy et al., 2006; Carranza, 2008; Carranza et al., 2008; Pazand et al., 2011, 2012a). Before the construction of a predictive model, which can be defned as representing the favourability or probability of a mineral deposit of the type/style sought to occur, a schematic subdivision has to be drawn depending on the type of inference mechanism considered. The two model types are: 1) knowledge-driven, and 2) data driven (Feltrin, 2008). The former means that evidential weights are estimated subjectively based on one or more expert opinions about the spatial association of target deposits with certain geologic features, whereas the latter means that evidential weights are quantifed objectively with respect to locations of known target deposits (Bonham-Carter, © 2016 – OGS 343 Boll. Geof. Teor. Appl., 57, 343-364 Pazand and Hezarkhani 1994; Moon, 1998; Cheng and Agterberg, 1999; Carranza and Hale, 2001; Porwal et al., 2004; Carranza et al., 2008). Several methods exist for mineral potential mapping. There are no better or worse techniques, but due to the geological complexity, condition, and data for an area, some techniques are better suited to potential mapping than others. In this paper, several methods of both knowledge-driven and data-driven types, such as fuzzy logic, Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Weights Of Evidence (WOE), neural network, and a hybrid of the methods, are used to build a mineral potential mapping for porphyry Cu mineralization in Ahar-Arasbaran zone. The Ahar-Arasbaran area has been studied for several decades because of its mineral potential for metallic ores, especially copper (skarn and porphyry types) and gold sulphide (Hezarkhani and Williams-Jones, 1996; Hezarkhani et al., 1997, 1999; Mollai et al., 2004, 2009; Hezarkhani, 2006, 2008). The aim of this work is to demonstrate the capabilities of these different methods to provide a porphyry copper prospective map, to determine advantages and disadvantages of each, as well as to choose the best method for providing porphyry copper potential mapping in the study area. Finally, using a combination of all the maps, fnal and optimal potential mapping was obtained. 2. Study area The Ahar-Arasbaran area is located in the east Azarbaijan province, north-western Iran, in the northern part of the Cenozoic Urumieh-Dokhtar magmatic arc (Fig. 1) with an arial extent Fig. 1 - Major structural zones of Iran (after Nabavi, 1976) and a modifed and simplifed geologic map of the study area (after Mahdavi, 1988; Babakhani and Nazer, 1991; Mehrpartou et al., 1992; Asadian, 1993; Amini, 1994; Asadian et al., 1994; Mehrpartou, 1997, 1999; Faridi and Haghfarshi, 2006). The map also indicates the locations of the Cu porphyry deposits (black dots). 344 Spatial estimation of the porphyry Cu potential Boll. Geof. Teor. Appl., 57, 343-364 of about 23,135 km2. Continental collision between the Afro-Arabian continent and the Iranian micro-continent during the closure of the Tethys ocean in the Late Cretaceous resulted in the development of the Urumieh-Dokhtar magmatic arc (Mohajjel and Fergusson, 2000; Babaie et al., 2001; Karimzadeh Somarin, 2005). All of the entire known porphyry copper mineralization in Iran occurs in the Urumieh-Dokhtar orogenic belt (Fig. 1). This belt was formed by subduction of the Arabian plate beneath central Iran during the Alpine orogeny (Berberian and King, 1981; Pourhosseini, 1981) and hosts two major porphyry copper deposits: 1) the Sarcheshmeh deposit and 2) the Sungun deposit. In the Ahar-Arasbaran area, there are 47 occurrences of economical and sub-economical porphyry copper deposits and all are associated with mid- to late-Miocene diorite/granodiorite to quartz-monzonite stocks (Pazand et al., 2012a). In the Arasbaran region, two intrusive magmatic cycles were recognized (Jamali and Mehrabi, 2014): Cycle I is of shoshonitic affnity and is not associated with signifcant mineralization, Cycle II, consisting of two cycles during the Upper Oligocene-Lower Miocene and Upper Miocene- Pliocene, is the signifcant metallogenic magmatic cycle. The earlier sub-cycle is dominantly associated with Cu-Mo porphyry mineralization, while the late sub-cycle is associated with porphyry Cu-Au and epithermal Au mineralization (Jamali and Mehrabi, 2014). 3. Database Five main types of geological data (input layers in the GIS) have been considered relevant for Cu porphyry exploration. They include airborne magnetic, stream sediment geochemical data, geology, structural data, and alteration zone (Pazand et al., 2012a). All data are managed in a GIS environment as an input shapefle, and are considered as a proxy variable of Cu porphyry deposits. The magnetic anomalies identifed by the airborne magnetic surveys may reﬂect the increased concentration of secondary magnetite in potassic alteration zones, or magnetite destruction in other peripheral styles of alteration, or high magnetite in the original intrusive plutons responsible for mineralization (Daneshfar, 1997). Airborne magnetic data were also used to identify magnetic lineation, faults, and intrusive bodies. The map of the total magnetic intensity was classifed and coded into ten main classes. Geological data inputs to the GIS are derived and compiled from the geologic map of 1:100,000 scale, and lithologic units were hand-digitized into vector format. Each polygon was labeled according to the name of each litho-stratigraphic formation, and a host rock evidence map was prepared including intrusive and volcanic rock as two sub-criteria. The geochemical analysis of stream sediment samples using Atomic Absorption Spectrophotometry (AAS) provided the concentrations of some elements considered as pathfnders of Cu porphyry mineralization (Cu, Mo, Pb, Zn, As, Sb, Ba). After normalization, the geochemical data were assigned to fve classes. Values equal to or less than the mean value were considered low background. Values between the mean and mean plus one standard deviation (+σ) are threshold. Values between (+σ) and (+2σ) are slightly anomalous. Values between (+2σ) and (+3σ) are moderately anomalous, and values greater than (+3σ) are highly anomalous (Pazand et al., 2012a). The geochemical evidence maps for each of the elements were prepared as geochemical sub-criteria. Porphyry-Cu deposits in the Urumieh-Dokhtar belt of 345 Boll. Geof. Teor. Appl., 57, 343-364 Pazand and Hezarkhani Fig. 2 - Evidence layer of total magnetic intensity. Fig. 3 - Evidence layers of intrusive and volcanic rocks. 346 Spatial estimation of the porphyry Cu potential Boll. Geof. Teor. Appl., 57, 343-364 Fig. 4 - Geochemical evidence layers of Cu, Mo, Ba, Zn, Sb, As, and Pb. Iran show geochemical halos of indicator elements, mainly Cu, Mo, Au, Ag, Zn, Pb, As, and Sb (Yousef and Carranza, 2014). Linear structural features interpreted from aeromagnetic data and remotely sensed data were combined with faults available in the geological map to generate a structural evidence map as follows: FD = a/A + b/B + c/C (1) where FD is the fault density, a is the number of lines in the cell, b is the number of intersections in the cell, c is the length of the linear in the cell, and A, B, C are averages of a, b, c parameters in the study

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