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, APRIL 2013, 64, 2, 153—163 doi: 10.2478/geoca-2013-0011
Introduction
Identification of mineralized zones plays a significant role in
exploration of porphyry deposits. The conventional geological
methods to identify mineralized zones in porphyry deposits
are mineralogical and petrographical studies (Schwartz
1947; Lowell 1968; Lowell & Guilbert 1970; Beane 1982;
Cox & Singer 1986; Sillitoe 1997; Melfos et al. 2002; Berger
et al. 2008). Fluid inclusion and sulphur isotope studies (e.g.
Roedder 1971; Nash 1976; Ulrich et al. 2001; Wilson et al.
2007; Asghari & Hezarkhani 2008) are other methods which
have been employed. However, in all of the models, ore
grades were not considered to distinguish different mineral-
ized zones and it is believed that ore grades vary in relation
to changes in the geological properties such as mineralogy,
lithology and alterations. Various geological interpretations
can define different boundaries of mineralized zones in por-
phyry deposits which potentially result in the ore element
grade distribution (Afzal et al. 2011).
In recent years, models based on fractal geometry pro-
posed by Mandelbrot (1983) have been widely applied in
different branches of earth sciences since various geochemi-
cal processes can be characterized by changes in fractal di-
mensions resulting from analysis of relevant geochemical
Correlation between geology and concentration-volume
fractal models: significance for Cu and Mo mineralized zones
separation in the Kahang porphyry deposit (Central Iran)
AMIR BIJAN YASREBI
1
, PEYMAN AFZAL
1,2
, ANDY WETHERELT
1
, PATRICK FOSTER
1
and
REZA ESFAHANIPOUR
3
1
Camborne School of Mines, University of Exeter, Cornwall Campus, TR10 9EZ Penryn, United Kingdom;
aby203@exeter.ac.uk
2
Department of Mining Engineering, South Tehran branch, Islamic Azad University, Tehran, Iran
3
National Iranian Copper Industries Co. (NICICO)
(Manuscript received May 15, 2012; accepted in revised form October 19, 2012)
Abstract: This study identifies the major mineralized zones including supergene enrichment and hypogene enrichment
in the Kahang Cu-Mo porphyry deposit which is located in Central Iran based on subsurface data and utilization of the
concentration-volume (C-V) fractal model. Additionally, a correlation between results achieved from a C-V fractal
model and geological models consisting of zonation, mineralography and alteration have been conducted in order to
have an accurate recognition and modification of the main mineralized zones. Log-log plots indicate five geochemical
populations for Cu and Mo in the deposit which means that mineralization commences with 0.075 % and 13 ppm for Cu
and Mo (as the first thresholds) respectively. The main mineralization began for Cu 0.42 % and Mo 100 ppm and also
enriched mineralization containing Cu 1.8 % and Mo 645 ppm which is located in the central part of the deposit.
According to the C-V model, the main Cu-Mo mineralized zones occur in the hypogene zone, especially in the central,
NW and NE parts of the Kahang deposit. The supergene enrichment zone derived via the C-V model is smaller than that
in the geological model and is located in the central and eastern parts of the deposit. Results analysed by the C-V fractal
model certify that the interpreted zones based on the fractal model are accurate. To certify this, a logratio matrix has
been employed to validate the C-V fractal model for the Cu and Mo main mineralized zones.
Key words: Iran, Kahang, mineralized zones, copper-molybdenum porphyry deposit, concentration-volume (C-V)
fractal model.
data (e.g. Turcotte 1986; Lucido et al. 1991; Agterberg et al.
1993; Cheng et al. 1994; Sim et al. 1999; Goncalves et al.
2001; Shen & Zhao 2002; Li et al. 2003; Lima et al. 2003;
Ortega et al. 2006; Ali et al. 2007; Carranza 2009; Zuo et al.
2009; Afzal et al. 2010; Wang et al. 2011; Zuo 2011).
Fractal analysis is able to indicate and correspondingly
justify the differences within mineralization, alteration, li-
thology and zonation of ore deposits especially in hydrother-
mal occurrences such as porphyry Cu deposits (Goncalves et
al. 2001; Cheng 2007; Carranza 2008; Carranza et al. 2009;
Cheng & Agterberg 2009; Afzal et al. 2011, 2012). However,
proper knowledge of the geological and geochemical aspects
of a deposit is important in order to identify characteristics of
geochemical populations on the basis of fractal analysis
(Cheng 1999; Sim et al. 1999; Goncalves et al. 2001; Li et
al. 2003; Carranza 2009; Carranza & Sadeghi 2010). In other
words, variations of fractal dimensions in geochemical data
can present applicable criteria to recognize mineralized
zones and barren host rocks within a study area.
The aim of this study is to use a concentration-volume (C-V)
fractal model to delineate Cu and Mo mineralized zones in the
Kahang porphyry deposit of Central Iran, and to correlate and
validate the results with geological models consisting of zonation
and alteration by logratio matrix as proposed by Carranza (2011).
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Significance and merits of fractal approach and
dimensions
Euclidian geometry classifies geometrical features with an
integer dimension say 1D, 2D, 3D, etc. However, there are
many spatial objects, whose dimensions cannot be mathemati-
cally defined by integers but by real numbers or fractions
called fractals which describe complexity in data distribution
by estimation of their fractal dimensions. Different geochemi-
cal processes can be explained on the basis of variations in
fractal dimensions obtained from analysis of relevant
geochemical data. Fractals are determined by a scaling law
that relates two variables: the scale factor (frequency) and the
object being measured (size). Fractal dimensions in geological
and geochemical processes correspond to differences in physi-
cal characteristics such as lithology, vein density or orientation,
fluid phase, alteration phenomena, structural feature or domi-
nant mineralogy (Takayasu 1990; Lauwerier 1991; Sim et al.
1999; Ortega et al. 2006). Conventional methods based upon
surface geological studies and analysis of cores from boreholes
in order to separate the mineralized zones do not have the high
accuracy particularly in porphyry deposits. The fractal geome-
try has a distinctive power to distinguish the natural populations
like various ore grades within a deposit.
These fractal methods proved their superiority to the clas-
sical statistical and conventional geological methods which
are used commonly. The reasons of their superiority to other
methods are as follows (Agterberg et al. 1993; Cheng et al.
1994; Sim et al. 1999):
1. In classical statistics, for the purpose of determining the
boundaries in mineralized zones, frequency distribution of a
related element in an intended area must adhere to normal dis-
tribution. This requirement is not always met so the data must
be normalized.
2. Those values which are not within the range (outliers)
must be identified and eliminated accordingly; otherwise
they lead to the intended study having unreal results.
3. Spatial distribution of the samples is important in classi-
cal statistics which means that all samples are considered in-
dependently and this is not reasonable.
4. The geometrical shape of anomalies does not receive at-
tention in any other way, so that the area covered by each
specific grade is not significant.
Concentration-volume (C-V) fractal model
The C-V fractal model, which was proposed by Afzal et al.
(2011) for division of mineralized zones and barren host
rocks in porphyry deposits, can be addressed as:
V(
)
—a1
;
V(
)
—a2
(1)
Where V(
) and V(
) illustrate two volumes with
concentration values less than or equal to and greater than or
equal to the contour value ; indicates the threshold value of
a mineralized zone (or volume); and a1 and a2 are characteris-
tic exponents. Elemental threshold values in this model repre-
sent boundaries between different mineralized zones and host
rocks of mineral deposits. To calculate V(
) and V(
),
which are the volumes enclosed by a contour level in a 3D
block model, the borehole data of ore element concentrations
were interpolated by utilization of geostatistical estimation.
The selection of breakpoints as threshold values appears to
be an objective decision because geochemical populations are
defined by different line segments in the C-V log-log plot. The
straight fitted lines were obtained based on least-square re-
gression (Agterberg et al. 1996; Spalla et al. 2010). In other
words, the intensity of element enrichment is depicted by each
slope of the line segment in the C-V log-log plots.
The C-V fractal model is applied for separating enrichment
zones, especially the supergene enrichment zone from the hy-
pogene zone and wall rocks using the concentration values of
the zones in combination with characteristic features of their
geometrical shapes such as ore veins. The C-V model is appli-
cable to ore elements in porphyry deposits such as Cu and Mo
for which the spatial patterns of concentration values satisfy a
multifractal model (Daneshvar et al. 2012).
Geological setting of Kahang deposit
The Kahang deposit of about 20 km
2
is located about
73 km NE of Isfahan in Central Iran. This deposit contains
more than 80 million tonnes of sulphide ore with an average
grade of 0.5 % Cu and 90 ppm Mo according to the latest ex-
ploration results. The deposit is situated in the Cenozoic
Urumieh-Dokhtar magmatic belt of the Zagros orogen ex-
tending from NW to SE Iran depicted in Fig. 1 (Stocklin
1977; Berberian & King 1981; Alavi 1994; Dargahi et al.
2010). The Iranian large Cu-Mo-Au porphyry deposits such
as Sarcheshmeh, Sungun, Meiduk and Darehzar are located
on the belt (Shahabpour 1994). The Kahang Cu-Mo porphyry
deposit was explored by remote sensing, geophysical methods
and drilling operations which are being carried out in a de-
tailed exploration stage (Tabatabaei & Asadi Haroni 2006;
Afzal et al. 2010, 2012).
This deposit is mainly composed of Eocene volcanic-pyro-
clastic rocks, which were intruded by quartz monzonite,
monzogranite-diorite to dioritic intrusions in Oligo-Miocene
rocks (Fig. 1). The extrusive rocks, including tuffs, breccias
and lavas are dacitic to andesitic composition.
On the other hand, these intrusions are roots of acidic to in-
termediate domes in the Kahang porphyry deposit. The main
structural features are two fault systems trending NE-SW and
NW-SE. The major alteration zones of potassic, phyllic, argil-
lic and propylitic types were accompanied by vein to veinlets
fillings of quartz, quartz-magnetite and Fe-hydroxides. Miner-
alization within intrusive bodies and their surrounding host
rocks consists of chalcocite, chalcopyrite, pyrite, malachite,
magnetite, limonite jarosite, goethite and chalcantite in quartz
stockworks and advanced argillic alteration. The eastern part
of the deposit is covered by phyllic and quartz-sericite alter-
ation (Rashidnejad Omran et al. 2011).
According to descriptive models (Cox & Singer 1986), cop-
per ores existing in the supergene zone consist of two ore min-
erals such as covellite (CuS) and chalcocite (Cu
2
S) which
contain 65 % to 80 % of copper. Bornite is found in this zone
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Fig. 1. Geological map of the Kahang study area, scale:
1 : 10,000 (Afzal et al. 2012) within the Urumieh-Dokhtar vol-
canic belt in the structural map of Iran (Alavi 1994).
but the amount of chalcopyrite is low. In the hypogene zone,
the amounts of chalcocite and covellite are decreased while
chalcopyrite and then bornite are considered as the main min-
erals. Potassic alteration consists of orthoclase (Kf) and biotite
which occur in the central parts of porphyry deposits, accord-
ing to the model of Lowell & Guilbert (1970). Phyllic is the
main alteration in mineralized parts of the porphyry deposits
which include quartz, muscovite, sericite and chlorite.
Geological 3D models
The 3D geological models have been generated from litho-
logical, mineralogical and alteration data recorded in 48 bore-
holes using RockWorks
™
v. 15 software. The subsurface data
include coordinates of drillcores, azimuth and dip (orientation),
lithology, alteration, mineralography and zonation. The project
dimensions are 600 660 780 m in X, Y and Z direction and
each voxel has a dimension of 4 4 10 m respectively.
Major rock types in the deposit are sub-volcanic acidic
units such as andesitic, trachytic, dacitic and dioritic rocks,
as depicted in Fig. 2. Andesitic and trachytic units surround
other rock types in this deposit. Dacitic rocks contain ores in
the SE part of the area of this study which concludes that
Dacitic units are the host rock of the SE part in the deposit.
The most frequent host rock is porphyric quartz-diorite in-
cluding Cu-Mo ore accumulation which is the index of sub-
volcanic acidic rocks (Fig. 2).
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Fig. 2. Geological 3D models including lithology, alteration, zonation and mineralization with the legend of each in the Kahang Cu-Mo
porphyry deposit. Abbreviations see in Appendix. (Scale is in m
3
.)
Fig. 3. Cu and Mo histograms in the Kahang deposit.
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Phyllic is the most frequent alteration in the Kahang de-
posit in terms of its size and expansion as illustrated in
Fig. 2. Potassic alteration is situated at depth and deepens as
it goes to the east part of the deposit although potassic alter-
ation exists near the surface in the west part of this porphyry
deposit. Other alterations namely argillic and propylitic are
small and occur near the surface as illustrated in Fig. 2.
Studies related to zonation in this deposit represent that
the most significant ore mineralization zone is hypogene in
terms of size and high percentage of chalcopyrite which can
be easily seen in the 3D model illustrated in Fig. 2. The su-
pergene enrichment zone and chalcocite are limited with
thicknesses of less than 10 m in the eastern part of the deposit.
Fig. 4. Experimental variograms for Cu (a) and Mo (b).
Fig. 5. C-V log-log plots for Cu (a) and Mo (b).
Chalcocite accumulation is situated in the central
part of the area, as illustrated in Fig. 2. Fe-oxides
are seen at the surface and in the oxidation zone
(Fig. 2). Additionally, pyrite as a fundamentally
important index of a copper porphyry deposit is
present in several parts of the deposit, and mala-
chite ore is very low with regards to volume in
the oxidized zone.
C-V fractal modelling
In this deposit, 7146 samples were collected
from 48 boreholes at 2 m intervals. The samples
were analysed by the ICP-MS method for Cu
and Mo concentrations. The Cu and Mo distribu-
tion functions are not normal, with Cu and Mo
averages of 0.166 % and 28 ppm, respectively
(Fig. 3). The experimental variograms for Cu
and Mo were calculated by SGeMS software as
shown in Fig. 4 and their ranges are 24 and 17 m
for Cu and Mo respectively. Experimental vario-
grams of Cu and Mo, specifically Cu, in the de-
posit indicate “hole effects” which shows there
are different mineralized zones and ore-forming
processes (Journel & Froidevaux 1982). The Ka-
hang deposit was modelled with 489,927 voxels
and each voxel had a dimension of 4 4 10 m
in the X, Y and Z directions based on the geo-
metrical properties of the deposit and grid drilling
dimensions (David 1970). 3D models of the distribution for Cu
and Mo were evaluated by Ordinary Kriging (OK) using
SGeMS and RockWorks software. Ordinary Kriging is a spatial
evaluation technique where the error variance is minimized
which is called the Kriging variance. It is based on the config-
uration of the data and on the variogram (Yamamoto 2000).
The C-V fractal model for Cu has been created according
to the Cu 3D block model. It reveals that there are 5 popula-
tions according to the log-log plot corresponding to 0.075 %,
0.42 %, 1.86 % and 3.24 % in this deposit (Fig. 5 and Ta-
ble 1). The first threshold of 0.075 % represents the begin-
ning of the Cu mineralization in this scenario. As a result of
this, the range of Cu concentrations less than 0.075 % is
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deemed as barren host rock. The second
threshold value of Cu is 0.42 % where the
main Cu mineralization starts. The range
of Cu concentrations higher than 1.86 %
illustrates an enriched zone for Cu. For
these Cu concentrations the slope of the
straight line fit is near to 90°.
Based on the 3D model of Mo distribu-
tion, volumes corresponding to different
Mo grades were considered to generate a
C-V fractal model. Threshold values of Mo
were identified in the C-V log-log plot
which reveals five geochemical popula-
tions and four threshold values equal to 13,
100, 316 and 645 ppm (Fig. 5 and Ta-
ble 1). Enriched mineralized zones are
deemed to have higher than 645 ppm be-
cause with these Mo concentrations the
slope of the straight line fit is close to 90°.
The main Mo mineralization starts from
the second threshold which is 100 ppm in
this kind of scenario. It is important to bear
in mind that the Mo concentration greater
than 13 ppm represents the start of Mo
mineralization so therefore any amount
less than 13 ppm is considered as barren
host rock in terms of Mo distribution. Cu
and Mo log-log plots have a multifractal
nature for the elemental mineralization in
the deposit.
Comparison and correlation of C-V
with geological models
To separate major mineralized zones in-
cluding the supergene enrichment and
hypogene zones, a correlation of the geol-
ogical model (as mentioned in section 4)
with Cu and Mo concentration distribution
models has been constructed and in addi-
tion results from the C-V model were ap-
plied on the combined model where in
consequence the supergene enrichment
zone exists in small parts close to the sur-
face and its Cu concentration value does
not exceed by 1.4 % (Fig. 6). The various
mineralized zones were distinguished by a
mathematical filter facility of RockWorks
software which is called “Boolean data
Table 1: Cu and Mo thresholds defined by C-V model in the Kahang deposit.
Geochemical population
Cu (%) threshold value
Mo (ppm) threshold value
Range Cu (%) Range Mo (ppm)
First (Barren host rock)
–
–
<0.075 <13
Second (Main mineralization starting)
0.075
13
0.075–0.42
13–100
Third
0.42
100
0.42–1.86
100–316
Fourth
1.86 (Enriched zone for Cu)
316
1.86–3.24
316–645
Fifth
3.24
645 (Enriched zone for Mo)
>3.24
>645
Fig. 6. Geological zones (Cu distribution) including supergene enrichment (a) and hypo-
gene (c) with modified zonation models via C-V containing of supergene enrichment (b),
hypogene (d), main hypogene (e) and enriched hypogene (f). (Scale is in m
3
.)
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type”. As a result, the studied mineralized
zones in the 3D model are allocated with bi-
nary codes (zero or one) which represent that
the zones with the code number of zero are
removed and the zones with the code number
of one will remain in the 3D model.
The supergene enrichment zone with
0.42 < Cu concentrations (starting main Cu
mineralization based on C-V model) is lo-
cated in small areas within the central and
eastern parts of the deposit, as depicted in
Fig. 6b, as can be seen, the supergene en-
richment zone derived via the C-V model
has a volume smaller than its geological
equivalent model.
The correlation between the geological hy-
pogene zone and the C-V model indicate that
marginal parts of the geological model have
Cu concentration
0 .075 % and is conse-
quently not considered as a hypogene miner-
alized zone. However, the main hypogene
zone with Cu 0.42 % is located in the cen-
tral, eastern and NW parts of the deposit es-
pecially at depth, but in the NE part of the
deposit it approaches the surface. The en-
riched hypogene zone with Cu 1.8 % is situ-
ated in small parts of the central, NW, NE
and SE of the deposit, as illustrated in Fig. 6.
The Mo distribution model is correlated
with the supergene enrichment and the hypo-
gene zones, as depicted in Fig. 7. The maxi-
mum concentration of Mo in the supergene
enrichment zone is 104 ppm and high values
of Mo are situated in the hypogene zone. The
main Mo mineralization with Mo 100 ppm
in the hypogene zone correlates with the
main hypogene zone (Cu 0.42 %). The en-
riched Mo zone with Mo 645 ppm is located
in the central part of the deposit and associated
with the enriched hypogene zone (Cu 1.8 %),
as shown in Fig. 7. These outcomes indicate
that the enriched mineralized zone is located
within the hypogene zone especially in the
central, NW and NE parts of the deposit.
In order to validate the accuracy of the C-V
model, a correlation between the mineralog-
raphy model (for chalcocite and chalcopy-
rite distributions) and the mineralized zones
Fig. 7. Mo distribution in supergene
enrichment zone (a), hypogene zone
based on Mo C-V model (b), hypogene
with Mo > 100 ppm (c), hypogene with
Mo > 316 ppm (d) and Mo enriched
zone (e). (Scale is in m
3
.)
was carried out by C-V model which showed that chalcocite
is associated with the supergene enrichment zone and chal-
copyrite is also located in the hypogene zone (Fig. 8). In ad-
dition, the chalcopyrite model with Cu 0.42 % has a strong
correlation with the main hypogene zone, as depicted in
Fig. 8e.
Application of logratio matrix
Carranza (2011) has proposed a logratio matrix to analyse
further calculation of spatial correlations between two binary
models. Using the mineralization model, an intersection op-
eration between a fractal mineralized zone model and differ-
ent zones in geological ore model was performed to obtain
numbers of voxels corresponding to each of the four classes
of overlap zones as shown in the Table 2. Using the obtained
numbers of voxels, Type I error (T1E), Type II error (T2E),
and overall accuracy (denoted as OA) relates to the ability of
the analysis to barren host rocks (background) and mineral-
ized zones of the fractal models were estimated with respect
to the geological ore model. Type I error (denoted as T1E)
relates to the ability of the analysis to barren host rocks
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whereas Type II error (denoted as T2E) relates to the ability
of the analysis to mineralized zones. The lower both errors
(i.e. the higher value for OA) are the better ability of the
analysis meanwhile to barren host rocks and mineralized
zones. The values for OA of C-V fractal and geological mod-
els (alteration and hypogene models) were compared with
one another as follows.
Comparison between the hypogene zone obtained from the
geological model and the main Cu and Mo mineralized
zones from C-V fractal model demonstrates that the hypo-
gene zone has better correlation with the main Cu mineral-
ized zone (Cu > 0.42%) because the number of overlapping
voxels (A) in the main Cu mineralized zone obtained by C-V
model (20,839 voxels) is higher than in
the main Mo mineralized zone (16,990
voxels), as depicted in Table 3. The over-
all accuracy of the main Cu and Mo min-
eralized zone derived via the C-V fractal
model with respect to the hypogene zone
of the geological model is equal to 0.154
and 0.146 respectively.
Alterations play a fundamental role in
zone identification and also in present-
ing geological models, as described by
Lowell & Guilbert (1970). Correlation
(from OA results) between the main Cu
mineralized zone obtained from C-V
model and potassic alteration is higher
than phyllic alteration because the OA
for potassic and phyllic alterations have
been determined as 0.765 and 0.509 re-
spectively (Table 4). As a result, the
higher values for overall accuracy in Ta-
bles 3 and 4 represent the higher overlap
between geological zones with mineral-
ized zones identified by the C-V fractal
model.
Validation between the main Mo min-
eralized zone (Mo > 100 ppm) based on
the C-V fractal model and alteration
zones from the geological model indi-
cates that there is a difference between
the two alteration zones. Overall accuracy
for the potassic and phyllic zones has
been determined as 0.770 and 0.524 re-
spectively (Table 5). According to these
results, the main elemental mineralized
zones have better correlation with the po-
tassic alteration zone.
Conclusion
The results from this study reveal that
the hypogene zone is a major mineralized
zone within the Kahang Cu-Mo porphyry
deposit. According to the C-V fractal
model, the main threshold values for Cu
and Mo are 0.42 % and 100 ppm, respec-
tively. Enriched Cu-Mo mineralized zones with Cu 1.8 %
and Mo 645 ppm are located in the central, NW and NE parts
within the hypogene zone. The supergene enrichment zone ex-
ists in small parts within the deposit, especially in the central
and eastern parts.
The supergene enrichment and hypogene zones delineated
by the C-V model correlate well with the alterations and
mineralogical data shown in the 3D models. The C-V log-
log plots from the Kahang deposit show that there is a multi-
fractal model for Cu and Mo. Correlation between the results
of the C-V model and the chosen geological particulars show
that the supergene enrichment zone has a high correlation
within the chalcocite accumulations within the Kahang de-
Fig. 8. Correlation between chalcocite (a),
chalcopyrite (b) and chalcopyrite 0.42 %
Cu (e) zones with supergene enrichment
zone (c) and main hypogene zone (d) based
on C-V model. (Scale is in m
3
.)
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Table 2: Matrix for comparing performance of fractal modelling results with geological model. A, B, C, and D represent numbers of voxels
in overlaps between classes in the binary geological model and the binary results of fractal models (Carranza 2011).
Geological model
Inside zone
Outside zone
Inside zone
True positive (A)
False positive (B)
Fractal model
Outside zone
False negative (C)
True negative (D)
Type I error = C/(A+C)
Type II error = B/(B+D)
Overall accuracy = (A+D)/(A+B+C+D)
Table 5: Overall accuracy (OA) with respect to potassic and phyllic alteration zones and main Mo mineralized zones obtained through the
C-V fractal model.
Table 3: Overall accuracy (OA) with respect to the hypogene zone resulted from the geological model and main Cu and Mo mineralized
zones obtained through the C-V fractal model.
Hypogene zone of Geological model
Inside zone
Outside zone
Inside zone
A
20,839
B
3,348
C-V fractal model of Cu
main mineralized zone
Outside zone
C 411,164 D 54,576
OA 0.154
Hypogene zone of Geological model
Inside zone
Outside zone
Inside zone
A
16,990
B
1,795
C-V fractal model of Mo
main mineralized zone
Outside zone
C 414,954 D 54,674
OA
0.146
Table 4: Overall accuracy (OA) with respect to potassic and phyllic alteration zones and main Cu mineralized zones obtained through the
C-V fractal model.
Potassic alteration zone of Geological model
Inside zone
Outside zone
Inside zone
A
1,699
B
17,086
C-V fractal model of Mo
main mineralized zone
Outside zone
C 95,053 D 374,575
OA 0.770
Phyllic alteration zone of Geological model
Inside zone
Outside zone
Inside zone
A
11,531
B
7,254
C-V fractal model of Mo
main mineralized zone
Outside zone
C 224,919 D 244,709
OA
0.524
posit. The main hypogene zone has an association with the
chalcopyrite distribution model having Cu 0.42 %. Accord-
ing to the correlation between results driven by fractal mod-
elling and geological models by logratio matrix, the main Cu
and Mo mineralized zones generated by the C-V fractal model
have a strong correlation with the potassic alteration zone
with respect to overall accuracy.
Acknowledgments: The authors are grateful to the National
Iranian Copper Industries Co. (NICICO) for their permission
to have access to the Kahang deposit dataset. Additionally,
the authors would like to thank Dr. A. Saad Mohammadi the
former CEO of NICICO for his support. The authors would
like to thank the reviewers of this paper for their comments
and valuable remarks.
Potassic alteration zone of Geological model
Inside zone
Outside zone
Inside zone
A
2,874
B
21,313
C-V fractal model of Cu
main mineralized zone
Outside zone
C 93,484 D 372,256
OA 0.765
Phyllic alteration zone of Geological model
Inside zone
Outside zone
Inside zone
A
10,345
B
13,842
C-V fractal model of Cu
main mineralized zone
Outside zone
C 226,246 D 239,494
OA
0.509
162
YASREBI, AFZAL, WETHERELT, FOSTER and ESFAHANIPOUR
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GEOLOGICA CARPATHICA
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Appendix
Full names of all abbreviations in the manuscript content.
ALL ALLUVIUM
ARG ARGILLIC
ANS ANDESITE
CAL
CALCITIZED
ANS–D ANDESITE–DIORITE
CHL
CHLORITIC
CLS
CORE LOSS
NA
Not Available
DAC DACITE
PHY
PHYLLIC
DIO DIORITE
POT
POTASSIC
GRD GRANODIORITE
PRP PROPYLITIC
QAN QUARTZ
ANDESITE
QCS
QUARTZ–CHLORITE–SERICITE
QAN–D QUARTZ
ANDESITE–DIORITE
SER
SERICITIC
QDI
QUARTZ DIORITE
SLC
SILCIFIC (SILICIC)
Lithology
TUF TUFF
Alteration
HYP HYPOGENE
CCA
CHRYSOCOLLA
LEA LEACHED
CHA
CHALCOCITE
LEA–HYP LEACHED–HYPOGENE
COV
COVELLITE
LEA–OXI LEACHED–OXIDE
CPR
CUPRITE
LEA–SUP LEACHED–SUPERGENE
CPY
CHALCOPYRITE
NA
Not Available
FEX
IRON OXIDE
OXI–SUP OXIDE–SUPERGENE
LIM
LIMONITE
SUP–HYP SUPERGENE–HYPOGENE
MAL
MALACHITE
MDL MOLYBDENITE
NTS NEOTOCITE
PYY PYRITE
Zonation
Mineralization
TRT TENORITE