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Using TOPSIS approaches for predictive porphyry Cu potential mapping: A case study in Ahar-Arasbaran area (NW, Iran)
Computers & Geosciences 49 (2012) 62–71
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Using TOPSIS approaches for predictive porphyry Cu potential mapping: A case study in Ahar-Arasbaran area (NW, Iran) Kaveh Pazand a,n, Ardeshir Hezarkhani b, Mohammad Ataei c a
Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Department of Mining, Metallurgy and Petroleum Engineering, Amirkabir University, Hafez Ave. no. 424, Tehran, Iran c Department of Mining, Geophysics and Petroleum Engineering, Shahrood University of Technology, 7th tir Sq., PO Box 36155-316, Shahrood, Iran b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 February 2012 Received in revised form 19 May 2012 Accepted 21 May 2012 Available online 23 June 2012
In this article, by using TOPSIS technique we propose a new method for mineral potential mapping that commonly used to exploration mineral deposits. TOPSIS is a practical and useful technique for ranking and selection of a number of externally determined alternatives through distance measures. We used TOPSIS and GIS to providing prospectivity maps for porphyry copper mineralization on the basis of criteria derived from geological, geochemical, and geophysical controls, and remote sensing data including alteration and faults in Ahar-Arasbaran area in North West Iran. This Method allowed a mixture of quantitative and qualitative information with group decision. The results demonstrate the acceptable outcomes for copper porphyry exploration. & 2012 Elsevier Ltd. All rights reserved.
Keywords: TOPSIS Potential mapping Cu porphyry Ahar-Arasbaran
1. Introduction Mineral exploration is a multidisciplinary task requiring the simultaneous consideration of numerous geophysical, geological, and geochemical data sets (Moon et al., 2000). Advances in computer technologies play an increasingly important role in the exploration and assessment of mineral deposits. Geographic Information Systems 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; Carranza, 2008; Pan and Harris., 2000). Selection of the appropriate target area is a complex problem and requires an extensive evaluation process that considers the requirements of the metallogenetic processes involved during the formation of mineral deposits. Furthermore, many potential criteria, such as geology setting, geochemical anomaly, geophysical evidence, tectonic and alteration evidence must be considered for the selection procedure of a target area. Therefore, target area selection can be viewed as a multiple criterion decision-making (MCDM) problem. A MCDM method allows the analyst and the decision-makers to understand the problem, the feasible alternatives, different outcomes, conflicts between the criteria and level of the data uncertainty (Mergias et al., 2007). MCDM method deals with the process of making decisions about
n
Corresponding author. Tel: þ98 912 405 5244; fax: þ98 021 4460 3072. E-mail address: [email protected] (K. Pazand).
0098-3004/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.cageo.2012.05.024
the presence of multiple criteria or objectives. A decision-maker is required to choose among quantifiable or non-quantifiable criteria. The objectives of decision-makers are usually conflicting and therefore, the solution is highly dependent on the preferences of each decision-maker. Several methods exist for MCDM (Cheng et al., 2002; Opricovic and Tzeng, 2004). There are no better or worse techniques, but some techniques are better suited to particular decision problems than others do (Mergias et al., 2007). The advantage of MCDM methods is that they can account for different factor impacts. Among these methods, the most popular ones are analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) (Dagdeviren et al., 2009). These methods have been used in many scientific and industrial studies (Ataei et al., 2008, 2009; Wang, 2007; Bilsel et al., 2006). Because the AHP method has important advantages for weight calculation procedures based on a pairwise comparison, it has been used for mineral potential mapping (Wilkinson et al., 1999; Carranza, 2008; Hosseinali and Alesheikh, 2008; Pazand et al., 2011). The technique for order preference by similarity to ideal solution (TOPSIS), which is one of the well known classical MCDM methods. TOPSIS, is a widely accepted multi-attribute decision-making technique due to its sound logic, simultaneous consideration of the ideal and the anti-ideal solutions, and easily programmable computation procedure. This technique is based on the concept that the ideal alternative has the best level for all attributes, whereas the negative ideal is the one with all the worst attribute values (Onut and Soner, 2008). The TOPSIS is a powerful method to evaluate several selected
K. Pazand et al. / Computers & Geosciences 49 (2012) 62–71
cases (such as potential mapping) to identify a suitable design solution. However, it has not been used yet for mineral potential mapping. In this paper, we report the results of mapping porphyry copper potential in the Ahar-Arasbaran area. 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 (Mollai et al., 2004, 2009; Hezarkhani and Williams-Jones, 1996, 1997, 1999; Hezarkhani, 2006, 2008). The aim here is to demonstrate the application of TOPSIS for processing relevant data and producing a porphyry copper prospective map. Moreover, the output prospective map is evaluated as to how well it has predicted the known Cu prospects.
0
where I is associated with the positive criteria, and I00 is associated with the negative criteria. Step 5: Calculate the separation measures, using the n-dimensional Euclidean distance. The separation of each alternative from the positive-ideal solution ðDjþ Þ is given as ffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xn þ 2 Djþ ¼ ðv v Þ j ¼ 1,2,. . ., J ð6Þ ij i i¼1 Similarly, the separation of each alternative from the negativeideal solution ðD j Þ is as follows: ffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X n Þ2 D ðv v j ¼ 1,2,. . ., J ð7Þ ij j ¼ i i¼1 Step 6: Calculate the relative closeness to the ideal solution and rank the performance order. The relative closeness of the alternative Aj can be expressed as
2. The TOPSIS method CC jþ ¼ The TOPSIS was first developed by Hwang and Yoon (1981). According to this technique, the best alternative would be the one that is nearest to the positive-ideal solution and farthest from the negative ideal solution (Ataei et al., 2008; Samimi Namin et al., 2008). The ideal solution (also called the positive ideal solution) is a solution that maximizes the benefit criteria/attributes and minimizes the cost criteria/attributes, whereas the negative ideal solution (also called the anti-ideal solution) maximizes the cost criteria/attributes and minimizes the benefit criteria/attributes. The so-called benefit criteria/attributes are those for maximization, while the cost criteria/attributes are those for minimization. The TOPSIS method consists of the following steps (Dagdeviren et al., 2009): Step 1: Establish a decision matrix for the ranking. The structure of the matrix can be expressed as follows: F1 2 A1 f 11 A2 6 6 f 21 : 6 6: : 6 6: 6 D ¼ A3 6 6 f i1 : 6 6 6: : 6 6: AJ 4 f J1
...
F2 f 12
...
f 22 : :
...
Fj f 1j
...
...
f 2j
...
. . .. . . . . .. . .
: :
. . .. . . . . .. . .
f i2
...
f ij
...
:
...
:
...
:
...
:
...
f J2
...
f Jj
...
j ¼ 1,2,. . ., J;
f 1n
3
f 2n 7 7 7 : 7 7 : 7 7 7 f in 7 7 7 : 7 7 : 7 5 f Jn
j ¼ 1,2,. . ., J;
n
i ¼ 1,2,. . .,n
i ¼ 1,2,. . .,n
0
j ¼ 1,2,. . ., J
ð8Þ
þ þ Since D j Z 0 and Dj Z 0, then clearly CC j A ½0,1. The larger the index value, the better the performance of the alternatives.
3. Study area The Ahar-Arasbaran area is located in the East Azarbaijan province, NW Iran, in the northern part of the Cenozoic UrumiehDokhtar magmatic arc (Figs. 1 and 2) and covers an area of
Selection of potential area for Cu porphyry mineralization
ð1Þ
Criteria for evaluating the alternatives
ð2Þ
ð3Þ
where wi represents the weight of the ith attribute or criterion. Step 4: Determine the positive-ideal and negative-ideal solutions. n n 0 þ A ¼ v1þ ,v2þ ,. . .,viþ ¼ ðmaxvij 9iEI Þ,ðminvij 9i A I00 Þ ð4Þ n
þ D j
Determine the alternatives
Step 3: Calculate the weighted normalized decision matrix by multiplying the normalized decision matrix by its associated weights. The weighted normalized value vij is calculated as V ij ¼ wi r ij ,
D j Djþ
Fn
where Aj denotes the alternatives j, j¼1, 2, y,J; Fi represents ith attribute or criterion, i ¼1, 2,y, n, related to ith alternative; and fij is a crisp value indicating the performance rating of each alternative Ai with respect to each criterion Fj. Step 2: Calculate the normalized decision matrix R (¼ [rij]). The normalized value rij is calculated as f ij ffi, r ij ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn 2 j ¼ 1 f ij
63
00 A ¼ v 1 ,v2 ,. . .,vi ¼ ðminvij 9iEI Þ,ðmaxvij 9j A I Þ
ð5Þ
Data collection
Create Decision Matrix & Weights of Criteria
Evaluation of alternatives with TOPSIS method
Final ranking
Fig. 1. The steps of the proposed method for Cu porphyry potential area selection.
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Fig. 2. Major structural zones of Iran (after Nabavi, 1976) and the location of the Ahar-Arasbaran area in these zones and a modified and simplified geologic map of it (after Mahdavi and Amini Fazl (1988), Asadian (1993), Asadian et al. (1994), Faridi and Haghfarshi (2006), Amini (1994), Mehrpartou, 1997; Mehrpartou et al., 1992; Mehrpartou, 1999; Babakhani and Nazer, 1991).
Fig. 3. Layer of total magnetic intensity.
about 23135 km2. Continental collision between the Afro-Arabian continent and the Iranian micro-continent during 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 the entire known porphyry copper mineralization in Iran occur in the
Urumieh-Dokhtar orogenic belt (Fig. 2). 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. The Sarcheshmeh deposit is the only one of these being mined, and contains 450 million tons of sulfide ore with an average grade of 1.13% Cu and
K. Pazand et al. / Computers & Geosciences 49 (2012) 62–71
0.03% Mo (Waterman and Hamilton, 1975). The Sungun deposit, which contains 500 million tons of sulfide reserves grading 0.76% Cu and 0.01% Mo (Hezarkhani and Williams-Jones, 1998), is currently being developed. In the Ahar-Arasbaran area, there are 47 occurrences of economical and sub-economical porphyry copper deposits are all associated with Mid- to late-Miocene diorite/ granodiorite to quartz-monzonite stocks (Fig. 2).
4. Application of the TOPSIS method to porphyry copper potential mapping The data used in this study were selected according to relevance with respect to porphyry copper exploration criteria. The five main criteria as input map layers include airborne
65
magnetic surveys, stream sediment geochemical data, geology, structural data, and remote sensing. At regional to local scales, airborne magnetic surveys, which are rapid and economic, have been traditionally employed for exploration of porphyry copper deposits. Porphyry intrusions and related alteration systems may have a characteristic magnetic signature, which can form a distinctive anomalous pattern in regional magnetic data. These patterns 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 used to identify magnetic lineaments, faults and intrusive bodies. Map of total magnetic intensity provided as main criterion and was classified and coded into ten classes according to their intensity (Fig. 3).
Fig. 4. Geological layers of intrusive and volcanic rocks.
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Geological data inputs to the GIS were derived and compiled from a 1:100,000 scale geological map, from which lithologic units were handed-digitized into a vector (segment) format. Each polygon was labeled according to the name of each litho-stratigraphic formation and two host rock evidential maps were prepared to include intrusive and volcanic rocks (Fig. 4). There are 5260 geochemical samples of the 80 mesh (0.18 mm) fractions of stream sediment that have been analyzed
by AAS (atomic absorption spectrophotometry). After normalization, the data were assigned to five classes. Values equal to or less than the mean are considered low background. Values between the mean and mean plus one standard deviation (X þs.d.) is high background. Values greater than X þs.d. however less than or equal to X þ2 s.d. are slightly anomalous. Values greater than X þ2 s.d. but less than or equal to X þ3 s.d. are moderately anomalous and values greater than X þ3 s.d. are highly anomalous (Woodsworth, 1972; Rubio
Fig. 5. Geochemical layers of Cu, Mo, Au, Zn, Sb, As and Pb.
K. Pazand et al. / Computers & Geosciences 49 (2012) 62–71
67
Fig. 5. (continued)
et al., 2000; Hongjin et al., 2007). This classification was applied to data for Cu, Mo, Pb, Zn, As, Sb, Ba as pathfinder elements porphyry copper mineralization and geochemical evidence maps were prepared for each of these elements (Fig. 5).
A number of fractures and lineaments in the mineralization zone can be guided for exploration because the duct for fluid hydrothermal that are formed porphyry copper deposits. Linear structural features interpreted from aeromagnetic data and
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Fig. 5. (continued)
Fig. 6. Layer faulting density.
remotely sensed data were combined with faults portrayed in the geological map in order to generate a structural evidence map. This map was classified and coded into ten main classes according to fault density per unit area (Fig. 6). Remote sensing data (ASTER) were used for extraction argillic, phyllic, prophyllitic and iron oxide alteration layer (Azizi et al., 2010) as four alteration sub-criteria and for preparing an alteration evidence map (Fig. 7). Each of the evidence maps has been converted to raster with cell size 100 100 m in ArcGIS software. So the final matrix with 2,313,500 row (Aj) cells and 15 columns (Fn) (criteria and subcriteria as geochemistry, geology, alteration, fault and geophysics) was formed. Weights representing relative importance of every criterion were calculated by called expert advice. In this research, we invited experts with backgrounds in porphyry copper deposits to give the corresponding relative importance of each criterion, and then analyzed all the opinions, and finally,
gained the rank of relative importance for each criterion as shown in Table 1. The mapping of potential for porphyry copper mineralization in the Ahar-Arasbaran area, was prepared by Eq. (1–8) and ArcGIS software (Fig. 8).
5. Discussion Each modeling method for predictive mineral potential mapping offers advantages and disadvantages, and this paper has endeavored simply to illustrate possible methodology for producing a mineral prospect map using a GIS. Application of TOPSIS to mineral-potential mapping is knowledge-driven method that is based on expert knowledge of spatial association between known deposits and spatial features representing geologic controls of deposit occurrence. The basic concept behind this method is that the chosen alternative should have the shortest distance from the
K. Pazand et al. / Computers & Geosciences 49 (2012) 62–71
positive ideal solution and the farthest distance from negative ideal solution. In approaches to TOPSIS modeling, one of the most significant procedures is the definition of weight for each criterion. Inaccuracies in determining the criteria weights can cause errors in estimating the potential areas. To avoid this mistake and accurate estimate of potential areas, Knowledge and experience of experts experienced in porphyry copper exploration we used. Thus from geologists with expertise in the copper exploration and
69
geologists who were familiar with the metallogeny of the study area were invited to make the score to prediction criteria. The ultimate test of TOPSIS model for porphyry Cu deposit is the predictive ability of the favorability map. The best and most difficult test is if this prediction leads to new discoveries. A suitable method for measuring the performance of a model for mineral potential maps consists of attempting to predict occurrences of deposits within the study area. As seen in the
Fig. 7. Alteration layers of Iron oxide, Phyllic, Prophyllic and Argillic.
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Fig. 7. (continued)
Table 1 Important factors of the criteria. Criteria
Cu
Mo
Zn
Pb
Sb
Ba
As
Fault
Important factor Criteria Important factor
0.091743 Argillic 0.06422
0.073394 Iron Oxide 0.073394
0.055046 Phyllic 0.091743
0.055046 Prophyllic 0.06422
0.045872 Volcanic 0.073394
0.045872 Intrusive 0.091743
0.055046 Magnetic 0.055046
0.06422
Fig. 8. Potential mapping for Cu porphyry mineralization in Ahar-Arasbaran area.
maps of the total number of the 48 known porphyry copper occurrences in the region, 34 occurrences were located in areas with high potential; this means that model predicts 72.34% of the known porphyry copper deposits, and ability and the accuracy of the method confirmed. Furthermore, a preliminary field study in
eight new areas that introduced in northern and eastern parts of the study area was conducted. In the six area, the direct effects of copper mineralization as malachite and hydrothermal alteration processes were observed and in two others, analyze of rock samples shown high levels concentration of copper (42000 ppm).
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6. Conclusions Exploration strategies for non-renewable resources have been changing rapidly along with the accelerating innovations in computer hardware and information-processing technology. The aim of this research is to construct Topsis model to provide potential mapping. The results demonstrated the following: 1- The methodology combining the TOPSIS with GIS provided an improved method for potential mapping, which enhanced the capability of spatial analysis by the GIS and the capability of multi layer analysis by the Topsis. 2- The application of the TOPSIS method for the predictive mineral potential mapping provides a strong framework for handling the complexity of modeling multiclass evidential maps in a flexible and consistent way. 3- The design of the TOPSIS procedure to obtain the evidence for mapping mineral potential must be based on the knowledge of the geological controls and genesis or the mode of formation of known mineralization in a particular area. 4- Validity of the results was confirmed by the distribution of the known deposits and field checking. 5- This method is useful for exploration of Cu porphyry deposits because of its very significant pathfinder features, such as hydrothermal alteration, and geochemical patterns, and geological setting.
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Computers & Geosciences journal homepage: www.elsevier.com/locate/cageo
Using TOPSIS approaches for predictive porphyry Cu potential mapping: A case study in Ahar-Arasbaran area (NW, Iran) Kaveh Pazand a,n, Ardeshir Hezarkhani b, Mohammad Ataei c a
Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Department of Mining, Metallurgy and Petroleum Engineering, Amirkabir University, Hafez Ave. no. 424, Tehran, Iran c Department of Mining, Geophysics and Petroleum Engineering, Shahrood University of Technology, 7th tir Sq., PO Box 36155-316, Shahrood, Iran b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 February 2012 Received in revised form 19 May 2012 Accepted 21 May 2012 Available online 23 June 2012
In this article, by using TOPSIS technique we propose a new method for mineral potential mapping that commonly used to exploration mineral deposits. TOPSIS is a practical and useful technique for ranking and selection of a number of externally determined alternatives through distance measures. We used TOPSIS and GIS to providing prospectivity maps for porphyry copper mineralization on the basis of criteria derived from geological, geochemical, and geophysical controls, and remote sensing data including alteration and faults in Ahar-Arasbaran area in North West Iran. This Method allowed a mixture of quantitative and qualitative information with group decision. The results demonstrate the acceptable outcomes for copper porphyry exploration. & 2012 Elsevier Ltd. All rights reserved.
Keywords: TOPSIS Potential mapping Cu porphyry Ahar-Arasbaran
1. Introduction Mineral exploration is a multidisciplinary task requiring the simultaneous consideration of numerous geophysical, geological, and geochemical data sets (Moon et al., 2000). Advances in computer technologies play an increasingly important role in the exploration and assessment of mineral deposits. Geographic Information Systems 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; Carranza, 2008; Pan and Harris., 2000). Selection of the appropriate target area is a complex problem and requires an extensive evaluation process that considers the requirements of the metallogenetic processes involved during the formation of mineral deposits. Furthermore, many potential criteria, such as geology setting, geochemical anomaly, geophysical evidence, tectonic and alteration evidence must be considered for the selection procedure of a target area. Therefore, target area selection can be viewed as a multiple criterion decision-making (MCDM) problem. A MCDM method allows the analyst and the decision-makers to understand the problem, the feasible alternatives, different outcomes, conflicts between the criteria and level of the data uncertainty (Mergias et al., 2007). MCDM method deals with the process of making decisions about
n
Corresponding author. Tel: þ98 912 405 5244; fax: þ98 021 4460 3072. E-mail address: [email protected] (K. Pazand).
0098-3004/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.cageo.2012.05.024
the presence of multiple criteria or objectives. A decision-maker is required to choose among quantifiable or non-quantifiable criteria. The objectives of decision-makers are usually conflicting and therefore, the solution is highly dependent on the preferences of each decision-maker. Several methods exist for MCDM (Cheng et al., 2002; Opricovic and Tzeng, 2004). There are no better or worse techniques, but some techniques are better suited to particular decision problems than others do (Mergias et al., 2007). The advantage of MCDM methods is that they can account for different factor impacts. Among these methods, the most popular ones are analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) (Dagdeviren et al., 2009). These methods have been used in many scientific and industrial studies (Ataei et al., 2008, 2009; Wang, 2007; Bilsel et al., 2006). Because the AHP method has important advantages for weight calculation procedures based on a pairwise comparison, it has been used for mineral potential mapping (Wilkinson et al., 1999; Carranza, 2008; Hosseinali and Alesheikh, 2008; Pazand et al., 2011). The technique for order preference by similarity to ideal solution (TOPSIS), which is one of the well known classical MCDM methods. TOPSIS, is a widely accepted multi-attribute decision-making technique due to its sound logic, simultaneous consideration of the ideal and the anti-ideal solutions, and easily programmable computation procedure. This technique is based on the concept that the ideal alternative has the best level for all attributes, whereas the negative ideal is the one with all the worst attribute values (Onut and Soner, 2008). The TOPSIS is a powerful method to evaluate several selected
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cases (such as potential mapping) to identify a suitable design solution. However, it has not been used yet for mineral potential mapping. In this paper, we report the results of mapping porphyry copper potential in the Ahar-Arasbaran area. 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 (Mollai et al., 2004, 2009; Hezarkhani and Williams-Jones, 1996, 1997, 1999; Hezarkhani, 2006, 2008). The aim here is to demonstrate the application of TOPSIS for processing relevant data and producing a porphyry copper prospective map. Moreover, the output prospective map is evaluated as to how well it has predicted the known Cu prospects.
0
where I is associated with the positive criteria, and I00 is associated with the negative criteria. Step 5: Calculate the separation measures, using the n-dimensional Euclidean distance. The separation of each alternative from the positive-ideal solution ðDjþ Þ is given as ffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xn þ 2 Djþ ¼ ðv v Þ j ¼ 1,2,. . ., J ð6Þ ij i i¼1 Similarly, the separation of each alternative from the negativeideal solution ðD j Þ is as follows: ffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X n Þ2 D ðv v j ¼ 1,2,. . ., J ð7Þ ij j ¼ i i¼1 Step 6: Calculate the relative closeness to the ideal solution and rank the performance order. The relative closeness of the alternative Aj can be expressed as
2. The TOPSIS method CC jþ ¼ The TOPSIS was first developed by Hwang and Yoon (1981). According to this technique, the best alternative would be the one that is nearest to the positive-ideal solution and farthest from the negative ideal solution (Ataei et al., 2008; Samimi Namin et al., 2008). The ideal solution (also called the positive ideal solution) is a solution that maximizes the benefit criteria/attributes and minimizes the cost criteria/attributes, whereas the negative ideal solution (also called the anti-ideal solution) maximizes the cost criteria/attributes and minimizes the benefit criteria/attributes. The so-called benefit criteria/attributes are those for maximization, while the cost criteria/attributes are those for minimization. The TOPSIS method consists of the following steps (Dagdeviren et al., 2009): Step 1: Establish a decision matrix for the ranking. The structure of the matrix can be expressed as follows: F1 2 A1 f 11 A2 6 6 f 21 : 6 6: : 6 6: 6 D ¼ A3 6 6 f i1 : 6 6 6: : 6 6: AJ 4 f J1
...
F2 f 12
...
f 22 : :
...
Fj f 1j
...
...
f 2j
...
. . .. . . . . .. . .
: :
. . .. . . . . .. . .
f i2
...
f ij
...
:
...
:
...
:
...
:
...
f J2
...
f Jj
...
j ¼ 1,2,. . ., J;
f 1n
3
f 2n 7 7 7 : 7 7 : 7 7 7 f in 7 7 7 : 7 7 : 7 5 f Jn
j ¼ 1,2,. . ., J;
n
i ¼ 1,2,. . .,n
i ¼ 1,2,. . .,n
0
j ¼ 1,2,. . ., J
ð8Þ
þ þ Since D j Z 0 and Dj Z 0, then clearly CC j A ½0,1. The larger the index value, the better the performance of the alternatives.
3. Study area The Ahar-Arasbaran area is located in the East Azarbaijan province, NW Iran, in the northern part of the Cenozoic UrumiehDokhtar magmatic arc (Figs. 1 and 2) and covers an area of
Selection of potential area for Cu porphyry mineralization
ð1Þ
Criteria for evaluating the alternatives
ð2Þ
ð3Þ
where wi represents the weight of the ith attribute or criterion. Step 4: Determine the positive-ideal and negative-ideal solutions. n n 0 þ A ¼ v1þ ,v2þ ,. . .,viþ ¼ ðmaxvij 9iEI Þ,ðminvij 9i A I00 Þ ð4Þ n
þ D j
Determine the alternatives
Step 3: Calculate the weighted normalized decision matrix by multiplying the normalized decision matrix by its associated weights. The weighted normalized value vij is calculated as V ij ¼ wi r ij ,
D j Djþ
Fn
where Aj denotes the alternatives j, j¼1, 2, y,J; Fi represents ith attribute or criterion, i ¼1, 2,y, n, related to ith alternative; and fij is a crisp value indicating the performance rating of each alternative Ai with respect to each criterion Fj. Step 2: Calculate the normalized decision matrix R (¼ [rij]). The normalized value rij is calculated as f ij ffi, r ij ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn 2 j ¼ 1 f ij
63
00 A ¼ v 1 ,v2 ,. . .,vi ¼ ðminvij 9iEI Þ,ðmaxvij 9j A I Þ
ð5Þ
Data collection
Create Decision Matrix & Weights of Criteria
Evaluation of alternatives with TOPSIS method
Final ranking
Fig. 1. The steps of the proposed method for Cu porphyry potential area selection.
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K. Pazand et al. / Computers & Geosciences 49 (2012) 62–71
Fig. 2. Major structural zones of Iran (after Nabavi, 1976) and the location of the Ahar-Arasbaran area in these zones and a modified and simplified geologic map of it (after Mahdavi and Amini Fazl (1988), Asadian (1993), Asadian et al. (1994), Faridi and Haghfarshi (2006), Amini (1994), Mehrpartou, 1997; Mehrpartou et al., 1992; Mehrpartou, 1999; Babakhani and Nazer, 1991).
Fig. 3. Layer of total magnetic intensity.
about 23135 km2. Continental collision between the Afro-Arabian continent and the Iranian micro-continent during 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 the entire known porphyry copper mineralization in Iran occur in the
Urumieh-Dokhtar orogenic belt (Fig. 2). 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. The Sarcheshmeh deposit is the only one of these being mined, and contains 450 million tons of sulfide ore with an average grade of 1.13% Cu and
K. Pazand et al. / Computers & Geosciences 49 (2012) 62–71
0.03% Mo (Waterman and Hamilton, 1975). The Sungun deposit, which contains 500 million tons of sulfide reserves grading 0.76% Cu and 0.01% Mo (Hezarkhani and Williams-Jones, 1998), is currently being developed. In the Ahar-Arasbaran area, there are 47 occurrences of economical and sub-economical porphyry copper deposits are all associated with Mid- to late-Miocene diorite/ granodiorite to quartz-monzonite stocks (Fig. 2).
4. Application of the TOPSIS method to porphyry copper potential mapping The data used in this study were selected according to relevance with respect to porphyry copper exploration criteria. The five main criteria as input map layers include airborne
65
magnetic surveys, stream sediment geochemical data, geology, structural data, and remote sensing. At regional to local scales, airborne magnetic surveys, which are rapid and economic, have been traditionally employed for exploration of porphyry copper deposits. Porphyry intrusions and related alteration systems may have a characteristic magnetic signature, which can form a distinctive anomalous pattern in regional magnetic data. These patterns 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 used to identify magnetic lineaments, faults and intrusive bodies. Map of total magnetic intensity provided as main criterion and was classified and coded into ten classes according to their intensity (Fig. 3).
Fig. 4. Geological layers of intrusive and volcanic rocks.
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K. Pazand et al. / Computers & Geosciences 49 (2012) 62–71
Geological data inputs to the GIS were derived and compiled from a 1:100,000 scale geological map, from which lithologic units were handed-digitized into a vector (segment) format. Each polygon was labeled according to the name of each litho-stratigraphic formation and two host rock evidential maps were prepared to include intrusive and volcanic rocks (Fig. 4). There are 5260 geochemical samples of the 80 mesh (0.18 mm) fractions of stream sediment that have been analyzed
by AAS (atomic absorption spectrophotometry). After normalization, the data were assigned to five classes. Values equal to or less than the mean are considered low background. Values between the mean and mean plus one standard deviation (X þs.d.) is high background. Values greater than X þs.d. however less than or equal to X þ2 s.d. are slightly anomalous. Values greater than X þ2 s.d. but less than or equal to X þ3 s.d. are moderately anomalous and values greater than X þ3 s.d. are highly anomalous (Woodsworth, 1972; Rubio
Fig. 5. Geochemical layers of Cu, Mo, Au, Zn, Sb, As and Pb.
K. Pazand et al. / Computers & Geosciences 49 (2012) 62–71
67
Fig. 5. (continued)
et al., 2000; Hongjin et al., 2007). This classification was applied to data for Cu, Mo, Pb, Zn, As, Sb, Ba as pathfinder elements porphyry copper mineralization and geochemical evidence maps were prepared for each of these elements (Fig. 5).
A number of fractures and lineaments in the mineralization zone can be guided for exploration because the duct for fluid hydrothermal that are formed porphyry copper deposits. Linear structural features interpreted from aeromagnetic data and
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K. Pazand et al. / Computers & Geosciences 49 (2012) 62–71
Fig. 5. (continued)
Fig. 6. Layer faulting density.
remotely sensed data were combined with faults portrayed in the geological map in order to generate a structural evidence map. This map was classified and coded into ten main classes according to fault density per unit area (Fig. 6). Remote sensing data (ASTER) were used for extraction argillic, phyllic, prophyllitic and iron oxide alteration layer (Azizi et al., 2010) as four alteration sub-criteria and for preparing an alteration evidence map (Fig. 7). Each of the evidence maps has been converted to raster with cell size 100 100 m in ArcGIS software. So the final matrix with 2,313,500 row (Aj) cells and 15 columns (Fn) (criteria and subcriteria as geochemistry, geology, alteration, fault and geophysics) was formed. Weights representing relative importance of every criterion were calculated by called expert advice. In this research, we invited experts with backgrounds in porphyry copper deposits to give the corresponding relative importance of each criterion, and then analyzed all the opinions, and finally,
gained the rank of relative importance for each criterion as shown in Table 1. The mapping of potential for porphyry copper mineralization in the Ahar-Arasbaran area, was prepared by Eq. (1–8) and ArcGIS software (Fig. 8).
5. Discussion Each modeling method for predictive mineral potential mapping offers advantages and disadvantages, and this paper has endeavored simply to illustrate possible methodology for producing a mineral prospect map using a GIS. Application of TOPSIS to mineral-potential mapping is knowledge-driven method that is based on expert knowledge of spatial association between known deposits and spatial features representing geologic controls of deposit occurrence. The basic concept behind this method is that the chosen alternative should have the shortest distance from the
K. Pazand et al. / Computers & Geosciences 49 (2012) 62–71
positive ideal solution and the farthest distance from negative ideal solution. In approaches to TOPSIS modeling, one of the most significant procedures is the definition of weight for each criterion. Inaccuracies in determining the criteria weights can cause errors in estimating the potential areas. To avoid this mistake and accurate estimate of potential areas, Knowledge and experience of experts experienced in porphyry copper exploration we used. Thus from geologists with expertise in the copper exploration and
69
geologists who were familiar with the metallogeny of the study area were invited to make the score to prediction criteria. The ultimate test of TOPSIS model for porphyry Cu deposit is the predictive ability of the favorability map. The best and most difficult test is if this prediction leads to new discoveries. A suitable method for measuring the performance of a model for mineral potential maps consists of attempting to predict occurrences of deposits within the study area. As seen in the
Fig. 7. Alteration layers of Iron oxide, Phyllic, Prophyllic and Argillic.
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K. Pazand et al. / Computers & Geosciences 49 (2012) 62–71
Fig. 7. (continued)
Table 1 Important factors of the criteria. Criteria
Cu
Mo
Zn
Pb
Sb
Ba
As
Fault
Important factor Criteria Important factor
0.091743 Argillic 0.06422
0.073394 Iron Oxide 0.073394
0.055046 Phyllic 0.091743
0.055046 Prophyllic 0.06422
0.045872 Volcanic 0.073394
0.045872 Intrusive 0.091743
0.055046 Magnetic 0.055046
0.06422
Fig. 8. Potential mapping for Cu porphyry mineralization in Ahar-Arasbaran area.
maps of the total number of the 48 known porphyry copper occurrences in the region, 34 occurrences were located in areas with high potential; this means that model predicts 72.34% of the known porphyry copper deposits, and ability and the accuracy of the method confirmed. Furthermore, a preliminary field study in
eight new areas that introduced in northern and eastern parts of the study area was conducted. In the six area, the direct effects of copper mineralization as malachite and hydrothermal alteration processes were observed and in two others, analyze of rock samples shown high levels concentration of copper (42000 ppm).
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6. Conclusions Exploration strategies for non-renewable resources have been changing rapidly along with the accelerating innovations in computer hardware and information-processing technology. The aim of this research is to construct Topsis model to provide potential mapping. The results demonstrated the following: 1- The methodology combining the TOPSIS with GIS provided an improved method for potential mapping, which enhanced the capability of spatial analysis by the GIS and the capability of multi layer analysis by the Topsis. 2- The application of the TOPSIS method for the predictive mineral potential mapping provides a strong framework for handling the complexity of modeling multiclass evidential maps in a flexible and consistent way. 3- The design of the TOPSIS procedure to obtain the evidence for mapping mineral potential must be based on the knowledge of the geological controls and genesis or the mode of formation of known mineralization in a particular area. 4- Validity of the results was confirmed by the distribution of the known deposits and field checking. 5- This method is useful for exploration of Cu porphyry deposits because of its very significant pathfinder features, such as hydrothermal alteration, and geochemical patterns, and geological setting.
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