Journal of Geographic Information System, 2011, 3, 312-317
doi:10.4236/jgis.2011.34028 Published Online October 2011 (http://www.SciRP.org/journal/jgis)
Porphyry Copper Mineral Prospectivity Mapping Using
Interval Valued Fuzzy Sets Topsis Method in Central Iran
Ali Reza Jafari Rad1,2*, Wolfgang Busch2
1Geomatics Department, Geological Survey of Iran, Tehran, Iran
2Institute of Geotechnical Engineering and Mine Surveying, Clausthal University of Technology,
Clausthal-Zellerfeld, Germany
Received July 31, 2011; revised September 2, 2011; accepted September 14, 2011
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. Ex-
cellent 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 Sar-
cheshmeh 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 fea-
tures those are effective in mineralization. These features can be recognized respect to Critical Genetic Fac-
tors (CGF’s) using Critical Recognition Criteria (CRC) for each type of mineralization. CGF’s can be em-
ployed 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 short-
est distance from the positive ideal points (A*) and the farthest distance from negative ideal points (A–).
Keywords: Mineral Prospectivity Mapping, Porphyry Copper, IVFSs TOPSIS, Iran
1. Introduction
Iran like other developing countries relies on the use of
their natural resources to support their economic devel-
opment. It is clear that, oil is the most important natural
resources but, utilization of mineral deposits has a long
antiquity in Iran. In particular, the exploration of mineral
resources has traditionally been a significant component
of the Iran economy.
Increasing of the base metal prices like Copper, Iron,
Lead and Zinc especially at the recent years causes to
attention for finding new resources more and more be-
cause one reason for decline in economic development of
many countries is decrease of known mineral deposits. In
the study area of this research, there are some reports
about geology, geophysics, geochemistry, ore deposits…
but, many of them were prepared in hard copy format
and some others don’t have standard database or have
disparate datasets, also some datasets are not reliable.
Beside that these information haven’t considered in re-
gional scale. Therefore development of a geomanage-
ment system using sufficient geosciences data for crea-
tion standard datasets that can be useable in GIS envi-
ronment is vital [1]. Also apply a sufficient integrating
method that cover different aspects of MPM is very im-
portant [2]. TOPSIS presented by Hwang and Yoon for
the first time in 1981 [3]. Malczewski combined GIS and
multi-criteria decision making approach [4]. This method
is more suitable for raster structure [5,6]. IVFSs TOPSIS
method in an intuitionistic fuzzy [7]. Zadeh and et al.
recommend fuzzy logic for management of uncertainty
[8].
Chen (2000) describes the rating of each alternative
and the weight of each criterion by linguistic terms,
which can be expressed in triangular fuzzy numbers [9].
Ting-Yu Chen and Chueh-Yung Taso (2007) applied

A. R. J. RAD ET AL.313
the Interval Valued Fuzzy Sets TOPSIS method in deci-
sion analysis [10].
1.1. Study Area, Data Layers and Software
Iran is located in Alpine-Himalaya orogenic and metal-
logenetic belt formed after Tethys collision, and there-
fore has a high potential for different types of minerals
[11]. Conventionally a unique Volcano-Plutonic-Arc
(VPA) is considered to be formed by subduction of
Mesozoic Tethys oceanic crust, but new evidences show
that there are different oceanic basins, and associated
arcs. One of the most important VPA is Kalkafi Sar-
cheshmeh–Kharestan (Samani & Ashtari, 1992) [12],
where the study area of this research is a part of this
VPA. The study area is located at northwest of Kerman
province in central Iran (Figure 1).
The utilized data include digital geology maps, AS-
TER and LANDSAT ETM imagery, airborne geophysics
(magnetics, radiometrics, and electromagnetics), geoche-
mical stream samples and heavy minerals data.
ARCGIS, ENVI and GEOSOFT software were de-
veloped for data preparation, analysis and modeling in
this research.
1.2. Geological and Metallogenetic Setting
The geological formations of the study area consist of
ranging from the Cretaceous up to the very recent Qua-
ternary sedi ments.
The most significant features, related to mineralization,
are the sedimentation, magmatic activity and structural
displacement that occurred during the Tertiary. The
granodiorite and diorite are the most common intrusive
rocks. The porphyry copper mineralization is related to
regional scale faults (more than 20 km length) and the
most important trends in the study area are N-S, NE-SW,
E-W, and NW-SE respectively. At places, where two
fault systems intersect, the intrusive bodies are fre-
quently hydrothermally altered [13]. These locations
have the best situation for porphyry mineralization.
Hydrothermal alteration zoning follows the Lowell and
Guilbert pattern [14].
1.3. Objectives
The main objectives of this research are as follows:
 To define critical genetic factors, critical recognition
criteria and intrinsic parameters for creation of con-
ceptual genetic model for porphyry copper minerali-
zation in the study area.
 To map different types of alteration zones using AS-
TER and LANDSAT Imagery Data.
 To analysis and interpretation of geophysics and
geochemical data, in order to recognition of anoma-
lous region, where related to porphyry copper miner-
alization.
 To define a method for quantifying spatial associa-
tions between known mineral deposits and effective
layers in mineralization as input to geologically-con-
strained predictive mapping of mineral prospectiv-
ity.
 To define a favourable geostatistical method(s) for
integration the evidence maps , this would be app lica-
ble in the comparable areas.
Figure 1. Location of study area in Iran.

A. R. J. RAD ET AL. 314
1.4. Methodology
This research was implemented as following steps.
1.4.1. Data Collection and Entry
Figure 2 has shown how the data turning to a GIS data
set.
1.4.2. Conversion Data to Information
Based on the model the data convert to information.
Figure 3 has shown the steps of information layers
preparation.
1.4.3. Preparation of CGM
Information layers integration has been carried out using
conceptual genetic model. Figure 4 followed the steps of
CGM preparation.
1.4.4. Geospatial Data Processing and Analysis
Using CGM geospatial information layers are processed
Data gathering
Hard copyDigit al format
Entry as acceptable format
in GIS environment
Vectorized and
GIS readyVector Ras ter
Figure 2. Data entry and Data set preparation.
Study area
Raw geosci enc e data
Processing
Data
Information
Standard data sets
Expert
Interpretation Field Informatio n
Information
Optimization
Figure 3. Information layer preparation.
Regional metallogenesis
evaluation
Investigation and
modeling of known
deposi ts
Investigation and
modeling of geological
evolution
Critica l gene t i c fact ors
Quantify spatial
associati on with
mineralization factors
Recognition geological
events related to
mineralization
Critical recognition criteria
Conceptual genetic model
Figure 4 . CGM prepar ation.
and the final weighted layers are prepared (Figure 5).
1.4.5. Preparation of Evidence Maps and Integration
Weighted layers analyse for evidence maps preparation.
These maps are the final layers and are integrated for
Mineral potential mapping (MPM).
Data lay ers
Geology
Ore deposits
Structure
Airborne Geophysics
Geoch e mist ry an omalies
Satellite imagery
Processing and analysis
Source rocks
Host rocks
Alteration zones
Favoura ble ba sins
Shallow bodie s
Ring and linear structures
Ore bearing zones
Figure 5. Data pr ocessing.

A. R. J. RAD ET AL.315
2. Data ANALYSIS and Integration
2.1. Preparation of Evidence Maps
According to geological and metallogenetic setting, all
features that are important in porphyry copper minerali-
zation were recognized and CGM was presented and the
predictor maps were performed. These maps consisting
of five thematic layers as follows:
1) Geological thematic layer: the original geology map
contained 80 different lithologies. On the basis of the
rock types and age, these units were classified to 32
groups and then were reclassified to 6 classes according
to their importance in mineralization.
2) Structural thematic layer: the structure features
were extracted from: a) geological maps; b) satellite im-
agery and c) geophysics data. After selecting regional
faults and calculating the azimuth, they were divided to
different trending and buffered up to 1500 meter. Ac-
cording to buffer distance and trending, they were classi-
fied to 7 classes.
3) Alteration thematic layer: as a result of satellite
imagery interpretation, phyllic, advance argillic, argillic
and propylitic alteration zones were identified and classi-
fied according to Lowell and Guilbert model.
4) Geochemistry anomalies thematic layer: several
geochemical anomalies were found out during geo-
chemical data analysis and classified on the basis of the
zonation of par age nesis e l ements.
5) Geophysics thematic layer: the results of geophysi-
cal data interpretation consist of: intrusive bodies, altera-
tion areas; and lineaments. The anomalies were classified
to 4 classes' base on the existence of zonation, and the
correlation of anomalies with geological features.
2.2. IVFSs T O PS IS M et hod for MPM
IVFSs TOPSIS method has been specified in support of
MPM in this research for the first time. Using this
method all significant factors for a knowledge driven
modeling system, like allocation Fuzzy Membership
(FM), Priority Weights (PW) and predefined targets can
be considered.
This technique can be performed in following steps:
1) Classification of each thematic data layers on the
basis of the CGM.
2) Allocation of FM to each class of data layers (Ta-
ble 1 as an example for geology layer).
3) Assign PW to each data layer (Table 2 ).
4) Multiplication of FM and PW (Table 3).
5) Calculation effective membership (a value), non-
membership (b value) and non-certainty (c value) for each
class of data layers (Table 4).
Table 1. FM of geology layer.
FM_Geology
Class Fuzzy_1 Fuzzy_2
1 0.7 0.9
2 0.5 0.7
3 0.3 0.5
4 0.2 0.25
5 0.05 0.1
Table 2. PW for geology layer.
W_Geology
W_1 W_2
0.8 0.9
Table 3. Multiplication of FM and PW for geology layer.
Geology
a b c
0.56 0.19 0.25
0.4 0.37 0.23
0.24 0.55 0.21
0.16 0.775 0.065
0.04 0.91 0.05
A* 0.56 0.19 0.25
A– 0.04 0.91 0.05
Table 4. “a”, “b” and “c” values for geology layer.
Geology
class F1*W1 F2*W2
1 0.56 0.81
2 0.4 0.63
3 0.24 0.45
4 0.16 0.225
5 0.04 0.09
6) Creation several Raster Images (RI) according to
“a”, “b” and “c” values for each data layer (Figure 6).
7) Calculation of positive ideal point (A*) and nega tiv e
ideal point (A¯) for “a”, “b” and “c” values in each data
layer (Table 4).
8) Measurement distance from (A*) and (A¯) for each
layer. For measuring distance in this research, Szmidt
and Kacprzyk’s [15] equation was specified in the form
of Equations 1 and 2.
S* = 1/2 [|(RI of “a value” – A*) + (RI of “b value” –
A*) – (RI of “c value” – A*)|] (1)
S– = 1/2 [|(RI of “a value” – A–) + (RI of “b value” –
A–) – (RI of “c value” – A–)|] (2)
9) Calculation of closeness for preparation MPM using
Equatio n 3:
i
i
S
CSS





 (3)

A. R. J. RAD ET AL. 316
Figure 6. Raster images basis on “a” (g reen), “b” (blue) and
“c” (orange) values.
3. Results
Developing mineral resources should start at the
pre-discovery stage and continue through feasibility to
the development stage. Integrating of predictor maps
using GIS allows more probabilistic data analysis tech-
niques and reduces costs and time. On the basis of the
IVFSs TOPSIS method, calculation of closeness at the
end step of procedure present a MPM that demonstrates
the favorable area for pre-discovery exploration (Figure
7).
The original MPM includes different numerical
classes. It can be reclassified to descriptive values based
on the big jumps in numerical values (Figure 8). First
class targets of this research contain 22 regions (0.76
percent of study area); include 13 old mining areas and 9
new areas. Setting of all old mining area s inside the first
Figure 7. MPM using IVFSs TOPSIS method.
Figure 8. Descriptive priority map.

A. R. J. RAD ET AL. 317
class targets, and field observations proves the efficiency
of this method. This method can be used in the similar
geological and metallogenetic locations in north-west-
wards and south-eastward s of the study area in Iran.
4. Conclusions
Analysis and investigation of first class prospect areas
after field checking prove that these areas have charac-
teristics as follows:
 They are located mainly in the intermediate Oligo-
cene_Miocene intrusive bodies, or Eocene volcanic-
sedimentary complex.
 The porphyry copper mineralization is related to re-
gional scale faults (length more than 10 km). The
most important trends for mineralization are N-S,
NE-SW, E-W, and NW-SE respectively.
 Hydrothermal alteration is extensive and typically
zoned on a deposit scale. The main alteration types
are:
o Advanced argillic alteration
o Argillic alteration
o Phyllic alteration
 Normally in porphyry copper mineralization, copper
and molybdenum geochemical anomalies in center
part, and lead, zinc, silver, bismuth, and magnesium
geochemical anomalies in outer part of alteration ha-
loes can be detected.
 Airborne geophysics data can be very definitive in
locating porphyry copper deposits related to hydro-
thermal systems. However no unique technique suf-
fices, it is necessary to utilize two or three techniques
to maximize the probability of finding new deposits.
5. References
[1] F. P. Agterberg, “Estimating the Probability of Occurrence
of Mineral Deposits from Multiple Map Patterns,” In: D. F.
Merriam and H. Kurzl, Ed s., the Use of Microcomputers in
Geology, Plenum Pres s, New York, 1992, p p. 73- 92.
[2] R. K. T. Reddy, F. P. Agterberg and G. F. Bonham-Carter,
“Application of GIS-Based Logistic Models to Base-
Metal Potential Mapping in Snow Lake Area,” Proceed-
ings of Canadian Conference on GIS, Ottawa, 24-26
March 1991, pp. 607-618..
[3] K. Y. Hwang, “Multiple Attribute Decision Making:
Methods and Applications,” Springer, Berlin, Heidelberg,
New York, 1981.
[4] J. Malczewski, “GIS and Multi-Criteria Decision Analy-
sis,” Wiley, New York, 1999.
[5] J. M. C. Pereira and L. Duckstein, “A Multiple Criteria
Decision-Making Approach to GIS-Based Land Suitabil-
ity Evaluation,” International Journal of Geographical
Information Systems, Vol. 7, No. 5, 1993, pp. 407-424.
doi:10.1080/02693799308901971
[6] S. S. Katz, “Emulating the Prospector Expert System with
a Raster GIS,” Computers and Geosciences, Vol. 17, No.
7, 1991, pp. 1033-1050.
do i :10.1016/ 0098- 3004( 91)90 097-W
[7] K. Atanassov and G. Gargov, “Interval Valued Intuition-
istic Fuzzy Sets,” Fuzzy Sets and Systems, Vol. 31, No. 3,
1989, pp . 343-349. doi:10.1016/0165-0114(89)90205-4
[8] L. A. Zadeh and J. Kacprzyk, “Fuzzy Logic for the Man-
agement of Uncertainty,” L. A. and J. Kacprzyk, Eds.,
John Wiley & Sons, New York, 1992.
[9] C. Chen, “Extensions of the TOPSIS for Group Deci-
sion-Making under Fuzzy Environment,” Fuzzy Sets and
Systems, Vol. 114, No. 1, 2000, pp. 1-9.
doi:10.1016/S0165-0114(97)00377-1
[10] T. Y. Chen and C. Y. Tsao, “The Interval-Valued Fuzzy
TOPSIS Method and Experimental Analysis,” Fuzzy Sets
and Systems, Vol. 159, No. 11, 2007, pp. 1410-1428.
doi:10.1016/j.fss.2007.11.004
[11] A. Zarasvandi, S. Liaghat and M. Zentilli, “Porphyry
Copper Deposits of the Urumieh-Dokhtar Magmatic Arc,
Iran,” In: T. M. Porter, Ed., Super Porphyry Copper &
Gold Deposits: A GlobalPerspeclive, PGC Publishing,
Adelaide, 2005, pp. 441-452.
[12] B. Samani and S. Ashtari, “Evaluation of Volcano- Plu-
tonic-Arc in Central Iran, Formed by Subduction of
Mesozoic Tethys Oceanic Crust,” Geosci ences, Geologi-
cal Su rvey of Iran , Vol. 1, No. 4, 1992.
[13] S. R. Titley and R. E. Beane, “Porphyry Copper depos-
its,” In: B. J. Skinner, Ed., Economic Geology Seventy-
fifth Anniversary Volume 1905-1980, Economic Geology
Publishing Co., Littleton, 1981, pp. 214-269.
[14] J. D. Lowell and J. M. Guilbert, “Lateral and Vertical
Alteration-Mineralization Zoning in Porphyry Ore De-
posits,” Economic Geology and the Bulletin of the Society
of Economic Geologists, Vol. 65, No. 4, 1970, pp.
373-408. doi:10.2113/gsecongeo.65.4.373
[15] E. Szmidt and J. Kacprzyk, “Distances between In-
tuitionistic Fuzzy Sets,” Fuzzy Sets and Systems, Vol. 114,
No. 3, 2000, pp. 505-518.
doi:10.1016/S0165-0114(98)00244-9