GeolCarp_Vol64_No5_409

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GEOLOGICA CARPATHICA

, OCTOBER 2013, 64, 5, 409—416 doi: 10.2478/geoca-2013-0028

Introduction

A geochemical anomaly occurs by various natural processes
related to different geological events (e.g. tectonics, mineral-
ization: Zhao 1999; Cheng 2007; Cheng & Agterberg 2009;
Wang et al. 2012). It may reveal important changes either in
geological  characteristics  and/or  mineralization  processes.
Separation of geochemical anomalies from background is an
important operation in mineral exploration. However, recog-
nition and delineation of the anomalies to predict the occur-
rence of mineral deposits need the knowledge of geo-anomaly
according  to  mineralization  types,  grade  distributions  and
geneses and knowledge of advanced methods for their quan-
titative  mapping.  In  the  past  decades,  two  basic  methods
have been commonly utilized to analyse geochemical explo-
ration data consisting of frequency analysis and spatial anal-
ysis (Grunsky & Smee 1999; Harris et al. 2000; Xu & Cheng
2001;  Pereira  et  al.  2003;  Wang  et  al.  2012).  Separation  of
anomalies  from  background  is  the  most  significant  purpose
of  geochemical  exploration  operations  especially  for  metal-
lic ore deposits. Stream sediment and lithogeochemical stud-
ies  are  essential  for  prospecting  different  types  of  ore
deposits (Hawkes & Webb 1979). Several methods are used
for  geochemical  data  interpretation  and  modelling  such  as
classical statistics (e.g. Tukey 1977; Hawkes & Webb 1979;
Reimann  et  al.  2005),  fractal  and  multifractal  modelling
(Cheng et al. 1994; Agterberg et al. 1996; Cheng 1999; Li et
al. 2003; Zuo et al. 2009; Afzal et al. 2010) and singularity

Correlation between Cu mineralization and major faults

using multifractal modelling in the Tarom area (NW Iran)

REZA NOURI

, MOHAMMAD REZA JAFARI

, MEHRAN ARIAN

, FARANAK FEIZI

and PEYMAN AFZAL

3,4

Department of Geology, North Tehran Branch, Islamic Azad University, Tehran, Iran; reza_noor2002@yahoo.com

Department of Geology, Science and Research Branch, Islamic Azad University, Tehran, Iran

Department of Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

Camborne School of Mines, University of Exeter, Penryn, United Kingdom

(Manuscript received January 4, 2013; accepted in revised form June 5, 2013)

Abstract: The Tarom 1 : 100,000 sheet is located within the Cenozoic Tarom-Hashtjin volcano-plutonic belt, NW Iran.
Reconstruction of the tectonic and structural setting of the hydrothermal deposits is fundamental to predictive models of
different ore deposits. Since fractal/multifractal modelling is an effective instrument for separation of geological and
mineralized zones from background, therefore Concentration-Distance to Major Fault (C-DMF) fractal model and dis-
tribution of Cu anomalies were used to classify Cu mineralizations according to their distance to major faults. Applica-
tion of the C-DMF model for the classification of Cu mineralization in the Tarom 1 : 100,000 sheet reveals that the main
copper mineralizations have a strong correlation with their distance to major faults in the area. The distances of known
copper mineralizations having Cu values higher than 2.2 % to major faults are less than 10 km showing a positive
correlation between Cu mineralization and tectonic events. Moreover, extreme and high Cu anomalies based on stream
sediments and lithogeochemical data were identified by the Number-Size (N-S) fractal model. These anomalies have
distances to major faults less than 10 km and validate the results derived via the C-DMF fractal model. The C-DMF
fractal modelling can be utilized for the reconnaissance and prospecting of magmatic and hydrothermal deposits.

Key words: Multifractal, stream sediment, lithogeochemical, copper mineralization, Concentration-Distance to Major
Fault (C-DMF).

modelling (Cheng 2007; Wang 2012). Fractal theory has been
established  by  Mandelbrot  (1983)  as  an  important  non-Eu-
clidean branch in geometry. Several methods and models have
been  proposed  and  developed  based  on  fractal  geometry  for
application  in  the  geosciences  since  the  1980s  (Agterberg  et
al. 1993; Sanderson et al. 1994; Cheng 1999; Turcotte 1997,
2002; Gonçalves et al. 2001; Monecke et al. 2005; Gumiel et
al.  2010;  Afzal  et  al.  2011;  Zuo  2011;  Sadeghi  et  al.  2012;
Yasrebi et al. 2013).

The aim of structural analysis applied to mineralization is to

identify what deformation influenced the increase or decrease
of permeability in rocks, both spatially and over time. There is
a positive correlation between tectonic and hydrothermal min-
eralization.  Such  understanding  can  contribute  to  predictive
models of deposit geometry and extensions to known depos-
its, and also to exploration models (Craw & Campbell 2004;
Micklethwaite  et  al.  2010).  The  purpose  of  this  study  is  to
classify Cu mineralizations according to their distance to ma-
jor faults by Concentration-Distance to Major Fault (C-DMF)
fractal  model  and  distribution  of  Cu  anomalies  by  using  a
multifractal method, in the 1 : 100,000 Tarom sheet, NW Iran.

Geological setting of the Tarom 1 : 100,000 sheet

The Tarom 1 : 100,000 sheet, located in Zanjan Province

NW Iran, was chosen as the study area because there are Cu-Au
polymetallic deposits which are part of the Tarom polyme-

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tallic zone that lie in the Alpine-Himalayan Mountain Range
(Fig. 1). There are two great intrusive masses in the same di-
rection of the volcanic rocks and other small outcrops within
basic, acidic and intermediate compositions (Mousavi 2012).
One of the noticeable features of magmatic highlands in the
study area is the presence of large granitic and granodioritic
bodies, which have intruded into the Eocene pyroclastic

Table 1: Characterization of the major faults in the Tarom area.

Name Type

Length

(km)

Strike

Zanjan

Thrust — Inverse

Zanjan1: N104
Zanjan2: N114
Zanjan3: N123
Zanjan4: N127

North Abhar Thrust — Inverse

Abhar1: N129
Abhar2: N111
Abhar3: N122

Thrust — Inverse

F3-1: N97
F3-2: N103
F3-3: N94
F3-4: N107
F3-5: N116
F3-6: N129
F3-7: N153
F3-8: N140

Thrust — Inverse

6.7

F4-1: N114
F4-2: N125
F4-3: N161
F4-4: N117

rocks (Karaj Formation). These represent post-Eocene intru-
sive  bodies  of  the  Pyrenean  orogenic  phase  intruding  along
the direction of deep NW-SE major fault zones in the Tarom
Mountain  Range  (Table 1  and  Fig. 2).  Alteration  halos  in
Eocene volcaniclastic rocks are one of the characteristic con-
sequences  of  these  intrusive  events.  There  are  sub-volcanic
intrusions with silicic, argillic, propylitic and sericitic hydro-
thermal  alteration.  Similar  plutons  and  intrusions  are  com-
mon in the Alborz—Azarbaijan structural zone of Iran, and it
is likely that there are concealed plutons related to this exten-
sive  Cenozoic  magmatism  (Karimzadeh  Somarin  2006).  The
intrusions contain ore minerals of Cu, Pb, Zn, Au, Ag and Fe
such as chalcopyrite, chalcocite, malachite, magnetite, galena
and  sphalerite.  Mineralized  Cu  veins  including  malachite,
chalcocite,  bornite  and  azurite  occur  in  Eocene  ignimbrites
and tuffs which have similar trends to the major faults in the
area,  especially  NNW-SSE.  Mineralization  of  gold,  copper,
lead—zinc,  and  kaolinite  are  associated  with  these  hydrother-
mal alteration halos (Azizi et al. 2010).

Tectonic setting

Based on the physiographic-tectonic zoning map of Iran’s

sedimentary  basins  (Arian  2011),  the  dominant  structural
trend  in  the  Western  Central  Alborz  and  Lesser  Caucasus
province (No. 9) is NW-SE (Fig. 1). It corresponds to a de-
formed  zone  (fold  and  thrust  belt)  of  the  Cimmerian  mini-
plate that formed in the northern active margin until the Late

Fig. 2. a – Fault map of the study area. b – Rose diagram
of major faults.

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Triassic.  It  was  subsequently  rifted  by  extension  forming  a
back-arc basin along the Neo-Tethys subduction zone in the
south margin of the Cimmerian miniplate. Rift development
stopped  in  the  Late  Cretaceous  and  was  renewed  in  the
Eocene by spreading of the submarine arc basin of the Neo-
Tethys  subduction  zone.  In  summary,  this  hinterland  is  the
result of a magmatic arc spreading system in a back-arc ba-
sin setting. Later, the Western Central Alborz and the Lesser
Caucasus  hinterland  were  formed  by  deformation  and  re-
gional uplift which extended from the south western margin
of the Caspian Sea to the south eastern margin of Black Sea.
Recently, the Damavand and Sebalan cones were formed by
late  volcanism  related  to  the  final  subduction  stages  of  the
oceanic slab in the south Caspian Basin toward the south and
southwest. Five dominant orogenic phases and four deforma-
tional events in the Alborz Mountain building processes were
identified by Arian et al. (2011). The first deformation event is
from the collision between the Cimmerian and Eurasian plates
(Late  Triassic)  and  the  remaining  ones  are  from  post-colli-
sion events and deformation of the sedimentary cover result-
ing  from  the  shortening  and  thickening  of  the  passive
continental crust north of the Cimmerian miniplate.

Geological structures

There are many geological structures in the fold and thrust

belt of West—Central Alborz and Lesser Caucasus province.
The  dominant  structural  trend  of  the  main  folds  and  faults
(Fig. 1)  is  NW-SE.  The  results  of  the  analysis  of  fractures
and  veins  are  presented  in  Fig. 1.  In  the  northeast  of  the
Tarom  sheet,  Eocene  rock  units  have  been  affected  by  the
North  Abhar  Thrust  Fault.  Neogene  units  have  also  been
overlaid by the dark grey shale and sandstone of the Shem-
shak Formation (Jurassic), which represented an active fault
system during the Holocene. The major faults run along the
main structural trend in the area (Table 1). The North Abhar
Thrust Fault has a length of 15 km and a NW-SE strike with
a general dip towards the NE. In the southwest of the Tarom
sheet, occurs the Zanjan Fault which has been interpreted as
a  major  thrust  fault.  The  Zanjan  inverse/thrust  Fault  has  a
length of 32 km and a NW-SE strike with a general dip to-
ward the SW. Towards the southeast of the Tarom sheet, older
units  including  granite  porphyry,  andesite,  and  tuff  units
(Eocene) have been affected by the F3 thrust fault, and a mi-
croquartzdiorite  porphyry  body  (Neogene)  has  intruded
along  it.  The  F3  inverse/thrust  Fault  has  a  length  of  28 km
and a NW-SE strike with a general dip toward the NE. To-
wards the northwest of the Tarom sheet, the F4 thrust fault af-
fected Eocene tuffs and has a length of 6.7 km and a NW-SE
strike with a general dip toward the NE.

Materials and methods

Number-size fractal method

The Number-Size (N-S) method, was originally proposed

by Mandelbrot (1983) and can be used to describe the distri-
bution of geochemical populations without pre-processing

the data. The method indicates that there is a relationship be-
tween desirable attributes (e.g. ore elements) and the cumu-
lative number of samples showing those attributes. Based on
this model, Agterberg (1995) proposed a multifractal model
for the determination of the spatial distributions of giant and
super-giant ore deposits. Monecke et al. (2005) used the N-S
fractal model to characterize element enrichments associated
with  metasomatic  processes  during  the  formation  of  hydro-
thermal ores in the Waterloo massive sulphide deposit, Aus-
tralia. A power-law frequency model was proposed to describe
the N-S relationship according to the frequency distribution of
element  concentrations  and  cumulative  number  of  samples
with  those  attributes  (Li  et  al.  1994;  Sanderson  et  al.  1994;
Turcotte  1996;  Shi  &  Wang  1998;  Zuo  et  al.  2009).  The
model  is  expressed  by  the  following  equation  (Mandelbrot
1983; Deng et al. 2010):

) = F

—D

(1)

where denotes element concentration, N(

) denotes the

cumulative  number  of  samples  with  concentration  values
greater than or equal to  , F is a constant and D is the scaling
exponent or fractal dimension of the distribution of element
concentrations.  According  to  Mandelbrot  (1983)  and  Deng
et al. (2010), log-log plots of N(

) versus follow straight

line segments with different slopes —D each corresponding to
different concentration intervals.

Concentration-Distance to Major Fault fractal model

The Concentration-Distance to Major Fault (C-DMF) frac-

tal model is an extension of the N-S model in the study. The
model has the following form:

DMF(

) = F

—D

(2)

where shows element concentration, DMF(

) indicates

cumulative distance from major faults of sampled sites with
concentration  values  greater  than  or  equal  to  ,  F  is  a  con-
stant  and  D  is  the  scaling  exponent  or  fractal  dimension  of
the  distribution  of  element  concentrations.  Based  on  this
model, metallic mines, deposits and occurrences were classi-
fied according to their distance to major faults.

Results and discussion

Application of N-S fractal method in stream sediment

Stream sediment geochemistry was found to be an effi-

cient method for outlining potentially mineralized areas. The
silt  fraction  of  alluvial  sediments  is  representative  of  the
geochemistry  of  the  drainage  pattern  and  reduces  the  “nug-
get  effect”  during  sampling  (Fletcher  1997;  Aichler  et  al.
2008). Once material enters the stream, processes that move
sediment  also  change  its  texture  and  geochemical  composi-
tion.  For  example,  light  mineral  fractions  < 100 µm  tend  to
be  swept  away  in  suspension  whenever  sediment  transport
occurs.  The  geochemical  consequences  of  sediment  sorting

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are not so obvious for elements (e.g. base metals) that are
rather uniformly distributed in different components of the
sediments. However, sorting has important consequences for

Fig. 3. a – Stream sediment. b – Lithogeochemical samples loca-
tion map of the Tarom sheet.

elements such as gold, that are present as constituents of rare
heavy  minerals.  Theory  and  field  studies  show  that  enrich-
ment of these elements on the stream bed is most consistent
for  the  fine  sand  fractions.  Concentrations  in  coarser  size
fractions become increasingly erratic, in both space and time,
depending on local hydraulic conditions. Thus, the finer frac-
tions  are  better  representatives  of  the  geochemistry  of  the
drainage basin and also reduce the nugget effect during sam-
pling (Fletcher 1997).

The analysed samples were sorted in decreasing order of

grades from which cumulative numbers were calculated. Fi-
nally, the log-log plot was generated for Cu (Fig. 3). Break-
ing points between straight-line segments in the log-log plot
represent threshold values separating sets of samples whose

Fig. 4. a – Log-log plots of N-S model for Cu stream sediment
data. b – Cu stream sediment population distribution map based on
the N-S fractal model.

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GEOLOGICA CARPATHICA, 2013, 64, 5, 409—416

geochemical concentration values outline distinct geological
and geochemical processes. Distinct Cu geochemical groups
are separated in this log-log plot. Relying on this approach,
there are five groups for Cu showing high Cu anomalies with
values in excess of 125 ppm.

The area was gridded with cells of 180 180 m

for the in-

terpolation  of  Cu  values.  The  interpolation  method  used  is
the Inverse Squared Distance (ISD) which was used for the
generation  of  maps  of  Cu  concentration.  This  procedure  is
preferred  because  it  reduces  the  undesirable  smoothing  ef-
fects  caused  by  Kriging.  Kriging  also  has  inherently  high
truncation  errors  for  the  upper  and  lower  boundaries  of  ore
grades. The high values of Cu ( 125 ppm) are located in the
central, NW and western parts of the Tarom sheet, as depicted

Fig. 6. Log-log plot from C-DMF model.

Fig. 5. a – Log-log plot of N-S method for Cu lithogeochemical
data. b – Cu lithogeochemical population distribution map based
on the N-S model.

Copper mines, Name

Cu (%)

Distance to major fault (km)

Valider

8.5

Geligeh 2.2 15.6
Sorkh 1.2

14.3

Nughl Abad

19.6

Kale Kash

3.38

21.8

Lohneh 1.8

15.3

Qeshlaq

2.1

8.5

Shilandar 1.8 22.8
Amirabad 5

Kuhian 1 15.5

Table 2: Average Cu grades of known mines and their distance to
the major faults.

Table 3: Extreme and high geochemical anomalous areas and their
distances to major faults in the Tarom sheet.

Cu stream

sediment

anomalies (ppm)

Distance to

major

fault (km)

lithogeochemical

anomalies (%)

Distance to

major

fault (km)

355

1.98

5.6

10.4

355

7.6

7.2

355

6.95

9.1

355

6.29

1.5

10.8

in Fig. 4. The terms of “extreme” and “high” have been uti-
lized in order to explain the high values of Cu anomalies
based on lithogeochemical and stream-sediment data, within
the area. High and extreme anomalies are shown in last seg-
ments (groups) in the log-log plots which have dips near 90°.

Application of N-S fractal method in lithogeochemical data
analysis

After analysis of the stream sediment data, lithogeochemi-

cal samples were collected in the anomalous domains in the
central and northern parts of the area (Fig. 3). The N-S log-
log  plot  for  Cu  show  there  are  six  geochemical  groups,  as
shown  in  Fig. 5.  Extreme  Cu  anomalies  start  at  3.16 %  Cu
and are located in the central parts of the Tarom sheet. Addi-
tionally,  anomalous  domains  derived  via  stream  sediments
were correlated with lithogeochemical extreme anomalies in
the center of the area. High Cu anomalies (0.8—3.16 %) were
situated in the central and NW parts of the area (Fig. 5).

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CORRELATION BETWEEN Cu MINERALIZATION AND MAJOR FAULTS – MULTIFRACTAL MODELLING (IRAN)

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Application of Concentration-Distance to Major Fault
fractal model

By means of C-DMF fractal modelling, six geochemical

groups were separated based on the existence of known cop-
per  mines  in  the  Tarom  1 : 100,000  sheet,  as  shown  in  Ta-
ble 2. The log-log plot illustrates there are two major phases
for  Cu  mineralization  which  have  a  multifractal  character
(Fig. 6). Copper mines with enrichment mineralization have
Cu  values  higher  than  2.2 %.  The  mines  are  located  at  dis-
tances less than 10 km from major faults (Table 3). The re-
sults  indicate  there  is  a  relationship  between  the  increasing
of the copper mine grades and decreasing of the distance be-
tween the major faults and the copper mines in the area.

Geochemical anomalies identified by stream sediments

and lithogeochemical data were used to validate the C-DMF
model’s  results.  All  extreme  Cu  anomalies  based  on  litho-
geochemical data (Cu > 3.16 %) derived via stream sediments
have a distance less than 8 km to the major faults. Moreover,
all  Cu  extreme  (Cu > 3.16 %)  and  high  (0.8 < Cu < 3.16 %)
anomalies  based  on  lithogeochemical  data  occur  at  distances
less  than  11 km  from  major  faults.  Overall,  the  geochemical
anomalies confirmed the results from the C-DMF fractal model.

Conclusion

Fractal/multifractal modelling is an effective instrument to

separate mineralized zones. Application of the C-DMF model
in  the  Tarom  1 : 100,000  sheet  reveals  that  the  main  copper
mineralizations are strongly correlated with their distance to
the major faults in the studied area. The distances of known
copper mineralizations with Cu values higher than 2.2 % to
major faults are less than 10 km showing a positive correla-
tion  between  Cu  mineralization  and  the  tectonic  events.
Moreover, major faults have played the main roles in hydro-
thermal fluids flow and the copper mineralization in the area.
Based  on  the  results,  the  C-DMF  fractal  modelling  can  be
utilized for the reconnaissance and prospecting of magmatic
and hydrothermal deposits.

Acknowledgment:  The  authors  are  grateful  to  Mr.  Beighi,
Mr.  Rahbar  and  Mr.  Haghigat,  Exploration  Department  of
Zanjan  Province  Industry,  Mine  and  Trade  Organization  of
Iran and Pars Peyazma Consulting Engineering Company for
their  sincere  help  and  valuable  discussions.  We  gratefully
acknowledge the review of the anonymous referees for their
constructive comments and suggestions. The careful editorial
handling  and  cheerful  encouragement  by  Assistant  Editor
Igor Petrík and Technical Editor Eva Petríková are gratefully
appreciated.

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