Prospecting of gold mineralization in Saqez area (NWIran) using geochemical, geophysical and geological studies based on multifractal modelling and principal component analysis

ORIGINAL PAPER

Prospecting of gold mineralization in Saqez area (NW Iran) usinggeochemical, geophysical and geological studies basedon multifractal modelling and principal component analysis

Alireza Almasi & Alireza Jafarirad & Peyman Afzal &Mana Rahimi

Received: 3 May 2014 /Accepted: 4 September 2014 /Published online: 16 September 2014# Saudi Society for Geosciences 2014

Abstract The aim of this study is to find gold prospectingareas based on geological, geochemical and geophysical datain Saqez area, NW of Iran. In order to achieve this task data,the analysis of 448 stream sediment samples are used todetermine elemental correlations using bivariate and multivar-iate correlation methods, such as Spearman and principalcomponent analysis (PCA). Afterwards, concentration–num-ber multifractal model is used to delineate anomaly thresholdsfrom the background for Au, As, Bi, Hg and first factor of thePCA. Moreover, for studying structures such as faults, frac-tures, and lithological contacts and their relationship with Auanomalies, the edges of magnetic anomalies are detected fromaeromagnetic data based on an enhanced multilevel edgedetection method. Subsequently, the geochemical anomaliesand the extracted lineaments are correlated with geologicalparticulars which are located in the catchment basins of anom-alous areas to specify favourable host rocks and to studypossible types of gold mineralization.

Keywords Gold prospecting . Elemental correlation .

Concentration area .Multifractal modelling . Aeromagneticdata . Multiscale edge detection

Introduction

Separation of background and anomalies is a fundamentalissue in geochemical exploration. Conventional statisticalmethods, which were used for decades, assumed that theconcentration of elements in the crust follows a normal orlognormal distribution (Davis 2002; Rose et al. 1979).Although it is now well understood that geochemical dataare characterized by their spatial distribution, traditionalmethods only emphasized on the frequency distribution ofelemental concentration and ignore spatial variability; in ad-dition, conventional statistical techniques contain disadvan-tages like rejection of outliers and normalization of raw dataso that it is better to be replaced by modern techniques (Afzalet al. 2010; Afzal and Ebadi Rajoli 2010; Hassanpour andAfzal 2013; Wei and Pengda 2002).

In recent years, due to the inhomogeneous and irregulardistribution of elements in the crust, fractal models which arederived from fractal geometry are widely developed and usedto determine thresholds of geochemical anomalies. Fractalmodels, which are established by Mandelbrot (1983), arecommonly applied in dealing with the elemental concentra-tions and including number–size model (N–S: Agterberg1995: Mandelbrot 1983; Turcotte 2002; Wang et al. 2010),radial–density model (Blenkinsop 1994; Carlson 1991;Carranza 2009; Feder 1988; Mandelbrot 1983; Raines2008), concentration–area model (C–A: Cheng et al. 1994),concentration–distance model (C–D: Li et al. 2003), concen-tration–number (C–N: Hassanpour and Afzal 2013), andmultifractal modelling (Afzal et al. 2010; Agterberg et al.

A. Almasi (*) :A. JafariradDepartment of Geology, Islamic Azad University, Science andResearch branch, P.O. Box 1477893855, Tehran, Irane-mail: [email protected]

A. Jafarirade-mail: [email protected]

P. AfzalDepartment of Mining Engineering, Faculty of Engineering, IslamicAzad University, South Tehran branch, Tehran, Irane-mail: [email protected]

P. AfzalCamborne School of Mines, University of Exeter, Penryn, UK

M. RahimiGeoinformatics Group, Department of Geomatics, GeologicalSurvey of Iran (GSI), Tehran, Irane-mail: [email protected]

Arab J Geosci (2015) 8:5935–5947DOI 10.1007/s12517-014-1625-2

1996; Cheng 1999; Deng et al. 2007, 2008; Wang et al. 2008;Zuo 2011).

Additionally, in order to calculate elemental correlations,which can assist in identifying paragenesis, different methodshad been developed such as Spearman and Pearson correlationcoefficients, hierarchical analysis and principal componentanalysis (PCA: Akbarpour et al. 2012; Ziaii et al. 2011).Hence, in this paper, the Spearman correlation method, PCAand C–N fractal model were applied on elemental concentra-tion data to study Au paragenesis and to delineate geochem-ical anomaly thresholds in order to find gold prospects inSaqez area, NW of Iran.

Among geophysical data, aeromagnetic data is known asan important source of information for studying lineamentsand subsurface structures (Bierlein et al. 2006; Cooper andCowan 2006, 2008; Henson et al. 2010; Neawsuparp et al.2005). Methods for detecting the edges of magnetic anomalieshave been widely regarded as an effective tool in explorationtechnologies (Li 2013). Many edge detection filters such asanalytic signal, vertical derivative, total horizontal derivative(THD) and tilt derivative (TDR) are available to accomplishthis task (Ferreira et al. 2011; Ma and Li 2012; Miller andSingh 1994; Verduzco et al. 2004). Multiscale edges, orworms, of potential-field data, can provide additional infor-mation on the vertical orientation of geological contacts andother planar features (Archibald et al. 1999; Hornby et al.1999). The approach is based on two key assumptions.Firstly, that the maximum horizontal gradient in potential-field anomalies indicates a boundary between underlying unitsof differing physical properties, and, secondly, that upwardcontinued levels of data tend to be influenced by geologicalstructure at increasing depth (Henson et al. 2010; Jacobsen1987). A thorough example of using multiscale edges is aresearch project which was carried out by Henson et al. (2010)in order to study 4D architecture and tectonic evolution of theLaverton region, eastern Yilgarn Craton, Western Australia.However, in this research, the concept of multiscale edgeswith applying an enhanced edge detection methodwas used inorder to identify lineaments in different levels in magneticdata and to study structures around gold anomalies.

Finally, to check the anomalous areas with geological map,locations of stream sediment samples and their catchmentbasins were considered to find proper host rocks for goldmineralization.

Geological setting

The study area covers about 1557 Km2 and is located in thenorthern part of the Sanandaj–Sirjan structural zone (SSZ),NWof Iran. The northern part of SSZ hosts different types ofgold mineralization such as intrusion-related, Au-rich VMS,Carlin-like, epithermal and orogenic (Aliyari et al. 2009;

Heidari et al. 2013; Fig. 1). Among these gold deposits/occur-rences, Qolqoleh, Kervian and Ghabaghlujeh are located inthe south and are nearest to the study area. These deposits/occurrences are of ductile to brittle shear zone type and arelocated within or adjacent to the major deep Saqez–Sardashtthrust fault and other confining normal faults (Afzal et al.2013; Aliyari et al. 2012).

As displayed in the geological map (Fig. 2), the study areacomprises wide range of rocks from mafic to felsic intrusions,Precambrian, Paleozoic and Mesozoic metamorphics and awhole range of carbonates and sedimentary rocks from differ-ent periods. Cretaceous units have the most distribution espe-cially in the center of the study area accompanied with meta-morphic units and intrusions in the east and west.

Considering nearby gold deposits/occurrences, ZarshuranCarlin-like gold deposit, with black shale (especially where itis faulted and fractured) being its important host rock (Asadiet al. 2000) in the east, Kharapeh with the host rock oflimestone, especially in the contact between andesitic dykeand limestone (Niroomand et al. 2011) in the NW, Barika withhost rock of Cretaceous meta-andesite and tuff (Tajeddin et al.2004, 2006; Yarmohammadi 2006) in the west and Saqez–Sardasht orogenic gold zone (Qolqoleh, Kervian andQabaqloujeh), with mafic to felsic meta-volcanic and meta-sedimentary rocks as their major host rocks (Aliyari et al.2007, 2009) in the south, and at the presence of the majorityof these rock units in the study area, they can be assumed asfavourable host rocks in finding gold prospects.Characteristics of the mentioned gold deposits/occurrencesare summarized in Table 1.

Geochemical data and elemental correlations

The study area was geochemically surveyed by the GeologicalSurvey of Iran (GSI) in 2006. Four hundred forty-eight streamsediment samples were collected and analyzed in GSI labora-tories for 11 elements consisting of As, Au, Be, Bi, Cd, Ce, Cr,Cu, Hf, Hg and Ni, which were utilized in this study to findnew gold prospects (Fig. 3).

In order to find elemental correlation coefficients and goldparagenesis, bivariate and multivariate statistical analysis wasapplied on the geochemical data using SPSS statistical soft-ware package. Bivariate matrix of correlation coefficientsusing the Spearman correlation method was calculated be-cause this coefficient is relatively independent of distributionmodel and outliers (Beus and Grigorian 1977; Fig. 4).Correlation analysis reveals similarities in behavior of pairsof elements but does not conveniently identify groups ofelements which behave similarly. Consequently, multivari-ate statistical analysis was used to classify data intosimilar groups.

5936 Arab J Geosci (2015) 8:5935–5947

When a large geochemical data set is generated, it maybe helpful to use a principal component analysis (PCA) inorder to reduce the number of observed variables to asmaller number of factors, providing insight into the struc-ture of the variance of the parameters (Muller et al. 2008;Yousefi et al. 2012). In the PCA, the concentration datasetis divided into subsets, represented by different factors.The elements in each subset are correlated with oneanother and are largely independent of the elements in

the other subsets (Fig. 4). Here, factors should be repre-sentative of the underlying geological and metallogenicalprocess that created the correlations among these variables(Deng et al. 2010).

Based on PCA, the elements were classified intothree factors. The first factor is important because Au,As, Hg and Bi are situated on this factor and As, Biand Hg are paragenesis of Au in gold deposits/occurrences of northern SSZ (Table 1).

Fig. 1 Main tectonic units of Iran showing the location and broaddistribution of various types/styles of the major gold deposit/prospectsin the northern and southern Sanandaj–Sirjan Zone, its Mesozoic mag-matic assemblage related to the Urumieh–Dokhtar magmatic belt (afterAlavi 1994; Ghasemi and Talbot 2006). 1 Kharapeh, 2 Barika, 3 Saqez–

Sardasht greenstone-hosted orogenic gold zone (Qolqoleh, Kervian andQabaqloujeh), 4 Aghdarreh–Zarshuran, 5 Tuzlar, 6 Sari Gunay, 7Akhtarchi, 8Astaneh–Sarband, 9Muteh, 10 Zartorosht (based on Aliyariet al. 2012)

Arab J Geosci (2015) 8:5935–5947 5937

5938 Arab J Geosci (2015) 8:5935–5947

Fig. 2 Geological map of the study area northern part of Saqez 1:100 000 map (based on Babakhani et al. 2003)

The C–N fractal model

Hassanpour and Afzal (2013) employed the elemental con-centration–number (C–N) method, as a branch of C–Amodel,which is used to define the geochemical background andanomaly threshold values. The model has the general form:

N ≥ρð Þ∞ρ−β ð1Þ

Where N(≥ρ) denotes the sample number with concentra-tion values greater than the ρ value, ρ is concentration ofelement and β is the fractal dimension. In this method, geo-chemical data has not undergone pre-treatment and evaluation(Hassanpour and Afzal 2013).

Fractal methods can illustrate the relationship between theobtained results and the geological, geochemical and miner-alogical information (Carranza 2009; Cheng 1999; Goncalveset al. 1998, 2001; Li et al. 2003; Sim et al. 1999). Geologicaldifferences can be shown in log–log plots resulting fromfractal methods such as C–A, C–P and C–N. The breaksbetween straight-line segments on the plots and the corre-sponding values of elemental concentration have been usedas thresholds to separate geochemical values into differentcomponents, representing different causal factors, such aslithological differences and geochemical processes (Limaet al. 2003; Wang et al. 2008; Afzal et al. 2010).

Application of C–N fractal model and PCA

Since Au, As, Bi and Hg are located in the first factor of thePCA, and Spearman analysis showed their low to moderatecorrelations, and also because As, Bi and Hg accompanyingAu in different types of hydrothermal gold mineralization innorthern SSZ, C–N fractal analysis was applied on Au, As, Bi,Hg and factor 1 of the PCA. Log–log plots employed torecognize geochemical thresholds for As, Au, Bi, Hg andfactor 1 which illustrated their multifractal nature in the studyarea (Fig. 5). The breaks between straight-line segments onthese plots and the corresponding values represent back-ground, halo and high-grade populations.

Based on the log–log plots, 9 geochemical populationswere identified for Au and factor 1 and also 10, 6 and 11populations were recognized for As, Bi and Hg, respectively.High and extreme Au anomalies were commenced from 646and 692 ppb and also, the highly and extremely anomalies ofAs started in 85 and 91 ppm in the area (Fig. 5).

In order to generate Au, As, Bi, Hg and factor 1 geochem-ical maps, firstly, Surfer 11 software was employed to inter-polate values between sample locations using ordinaryKriging method. Afterwards, thresholds from fractal model-ling were applied on these maps to identify the location ofT

able1

Characteristicsof

major

gold

depositsandoccurrencesin

northern

SSZ

Deposit/prospect

Hostrocks

Genetictype

Age

ofmineralization

Enrichedelem

ent(s)/

structuralfeatures

Resource(t)

grade(g/t)

Datasource

Qolqoleh

Maficto

interm

ediate(andesite

toandesitic

basalt)

meta-

volcanicrocksandsericiteschist

Orogenic(ductileto

brittleshearzone)

Upper

Cretaceous–Tertiary

Hg/intersectin

gdeep

faults

Upto

3Mtat3

.5g/t

Aliy

arietal.(2007,2009);

Tajeddin,(2011)

Kervian

Felsicto

maficmeta-volcanic

andmetasedim

entary

rocks

Orogenic(ductile

shearzone)

Upper

Cretaceous–Tertiary

AsandBi/intersecting

deep

faults

Unknown

Heidari,(2004);

Heidarietal.(2006);

Tajeddin

(2011)

Qabaqloujeh

Upper

Cretaceousmeta-volcanic

phyllite,schistandmylonitic

Orogenic(ductileto

brittleshearzone)

Upper

Cretaceous–Tertiary

Hg/intersectin

gdeep

faults

1Mtat1

g/t

Tajeddin

(2011)

Barika

Cretaceousmeta-andesiteandtuff

Au-rich

volcanicmassive

sulphide

(VMS)

Low

erCretaceous–Tertiary

AsandHg

Average

0.5to

5g/t

Tajeddin,(2011);

Tajeddin

etal.(2004);

Tajeddin

etal.(2006);

Yarmoham

madi(2006)

Kharapeh

Lim

estone,contactbetween

andesitedyke

andlim

estone

Orogenic(brittle)

Upper

CretaceousTertiary

Cu/intersectin

gdeep

faults

70,000

tat1

.52g/t

Niroomandetal.(2011)

Zarshuran

Black

shalelim

estone

(presence

ofcalcareous

spring

deposits)

Carlin

-like

Olig

ocene-Miocene

As,Biand

Hg/anticlin

e2.5Mtat1

0g/t

Asadi

etal.(2000)

Arab J Geosci (2015) 8:5935–5947 5939

anomalies for each element and to clarify the parageneticrelationship for each of the Au anomalies (Fig. 6).

Using identified Au thresholds, six geochemical anomalieswere recognized on the Au map. Anomaly numbers 1 and 6illustrate high correlation with As, Bi and Hg but it is not validto the four other anomalies. The comparison of these

elemental correlations is summarized in Table 2. As depictedin Fig. 6, the elemental anomalies are mostly located in the SE,NW and SW parts of the study area. However, As and Bianomalies covered greater parts of the study area which is anevidence for their higher mobility as pathfinders in Au min-eralization. High and extreme intensity anomalies of factor 1

Fig. 3 Locations of 448 streamsediment samples in the studyarea, including digital elevationmodel (DEM) in the background

Fig. 4 Bivariate and multivariatestatistical analysis of streamsediment samples data, aSpearman correlation coefficientsand b principal componentanalysis (PCA)

5940 Arab J Geosci (2015) 8:5935–5947

are situated in the SW and western parts of the study area andare associated with the elemental anomalies in these parts(Fig. 6).

Catchment basins of stream sediment samples were pro-duced using digital elevation model (DEM). Each catchmentwas valued based on the zonal statistics of the Au geochemicalmap and classified with applying C–N fractal model thresh-olds (Fig. 7).

Aeromagnetic data and multilevel edge detection

The study area was part of an airborne geophysical surveywhich was conducted by the Atomic Energy Organization ofIran (AEOI) in 1976. Aeromagnetic data in the form of totalmagnetic intensity grid (TMI) of this survey with line spacingand flight elevation of 500 and 120 m, respectively, were used

in this research to determine deep and shallow structures androck unit contacts with employing edge detection methods.

In this research, Oasis Montaj software was used to processairborne geophysical data. After removing IGRF-1976, inorder to place the anomalies above their causative bodies,reduction to magnetic pole (RTP: Baranov 1957) filter wasapplied on TMI. Thereafter, to minimize the effects of shallowmagnetic anomalies, upward continuation (UC) filter with1000-m distance was applied on TMI RTP (Fig. 8).

The edges of magnetic anomalies can represent lith-ological contacts, faults, fractures and crustal disconti-nuities (Bierlein et al. 2006; Henson et al. 2010, Robertet al. 2007) which are essential for gold mineralizationin the study area. In order to detect the edges ofaeromagnetic anomalies, total horizontal derivative(THD), which is used in multiscale edge detectionmethod, is among the mathematical transformations or

Fig. 5 C–N log–log plots of As, Au, Bi, Hg and factor 1 of PCA, breaks between straight-line segments show geochemical thresholds

Arab J Geosci (2015) 8:5935–5947 5941

Fig. 6 Geochemical map of Au, As, Bi, Hg and factor 1 of the PCA (using ordinary Kriging interpolation method), anomaly delineation using C–Nfractal thresholds

5942 Arab J Geosci (2015) 8:5935–5947

filters that have been developed in the last decades forinterpreting and analyzing aeromagnetic data (Archibaldet al. 1999; Austin and Blenkinsop 2008, 2009; Hensonet al. 2010; Holden et al. 2000). Tilt derivative filter(TDR) is also applied on aeromagnetic data for normal-ization and for depth estimation of the magnetic bodies(Ma and Li 2012; Miller and Singh 1994; Salem 2007;Verduzco et al. 2004). Ferreira et al. (2011, 2013)suggested that applying TDR on the THD can assistthe enhancement and better visualization of magneticedges. Therefore, in this research, for edge detection,at first, THD was applied on RTP UC 1000 m (Fig. 5c),subsequently for enhancement and normalization of theedges TDR was applied on UC THD (Fig. 5d).

In order to build multilevel edges, commonly usedmultiscale edge detection methods use an automated systemlike GOCADTM software for mapping the worms in differentlevels with applying THD on different distances of RTP UC(Archibald et al. 1999; Henson et al. 2010; Hornby et al.1999). Hence, in this research, for multilevel edge detection,THD and TDR were applied, respectively, on RTP UCs withdifferent distances. Lineaments extracted from 1000, 1500,2000, 2500, 3000, 3500, 4000, 4500, and 5000 m upwardcontinuation grids represent features below 380, 630, 880,1130, 1380, 1630, 1880, 2130 and 2380 m of the surface,respectively (after Jacobsen 1987; Fig. 9).

Correlation of multifractal modelling with geophysicaland geological particulars

Considering similar key structural manifestation for gold min-eralization types in northern SSZ, which was deep intersectingstructures (contacts, faults or fractures) in orogenic deposits/occurrences and an anticline in Zarshuran Carlin-like deposit,similar features were mapped using aeromagnetic data andcorrelated with geochemical anomalies (Fig. 10). Extractedlineaments displayed that Au anomalies in the south and eastof the study area are placed over or next to deep bendingintersecting structures and in the NW are located near anoblique deep structure which is similar to an anticline.Subsequently, comparison of geological particulars and catch-ment basins of anomalous samples was produced to identifyfavourable host rocks for gold mineralization (Fig. 11).

In the east and center of the study area (anomaly nos. 2, 3and 4), location of anomalies over deep intersecting bendingstructures; presence of Precambrian metamorphic rock unitssuch as schist, gneiss and acidic tuffs in catchment basins ofanomaly nos. 2 and 3 and attendance of micro conglomerateand grey shale in the basin of anomaly no. 4; and Au corre-lation with As and Bi in anomaly nos. 2 and 4; and only withAs in anomaly no. 3, are comparatively similar with thespecifications of Saqez–Sardasht orogenic gold occurrence/deposits (Qolqoleh, Kervian and Qabaqloujeh) in the south ofthe study area (Figs. 10 and 11).

In the SWof the study area (anomaly no. 1) the presence ofa granitic intrusion accompanying with deep faults (Figs. 2and 10), high correlation of Au with As, Bi and Hg andpresence of black and grey shale as proper host rocks in thisarea, and in the NW (anomaly no. 6) abundance of calcareousspring deposits (travertine) and presence of grey shale andsignificant Au association with As, Bi and Hg are plausible

Table 2 Summery of correlations in elemental anomalies

Anomaly no. As Hg Bi Anomaly no. As Hg Bi

1 ✓ ✓ ✓ 4 ✓ ✓

2 ✓ ✓ 5

3 ✓ 6 ✓ ✓ ✓

Fig. 7 Stream sedimentcatchment basins classified usingC–N fractal thresholds

Arab J Geosci (2015) 8:5935–5947 5943

Fig. 8 The steps of the edge detection method for 1000 m upward continuation, a RTP, b upward continuation with 1000-m distance, c total horizontalderivative (THD) and d Tilt derivative and extracted linear features

Fig. 9 Extracted lineaments fromdifferent upward continuationscombined with RTP grid in thebackground

5944 Arab J Geosci (2015) 8:5935–5947

f ac to r s o f a Car l in - l ike go ld minera l i za t ion .Additionally, in anomaly no. 5, considering no correla-tion between Au, As, Bi and Hg, association with a

deep oblique structure and attendance of micro con-glomerate and grey shale are important aspects of anAu mineralization (Figs. 10 and 11).

Fig. 10 Correlation ofgeochemical anomalies andextracted lineaments fromaeromagnetic data (red areasshow the catchment basins ofanomalous stream sedimentsamples)

Fig. 11 Correlation ofanomalous samples catchmentbasins and geological map of thestudy area

Arab J Geosci (2015) 8:5935–5947 5945

Conclusions

Based on the results obtained by C–N multifractal modellingand PCA analysis, Au anomalies are situated in the SW, NWand SE parts of the study area. Geochemical anomalies of Auand other correlated elements had to be compared and corre-lated with each other and also with rock units of the geologicalmap and structures extracted from aeromagnetic data.Comparing Au, As, Bi and Hg anomaly maps revealed thatthere are different paragenetic relationships in each of the Auanomalies which can be evidence that we are dealing withdifferent styles of gold mineralization in this study area.Considering this information and relatively nearby golddeposits/occurrences these anomalies can be of orogenic orCarlin-like gold mineralization.

Geochemical, structural and geological manifestations ofthe study area clearly demonstrate its high potential for differ-ent types of gold mineralization especially in the east, NWandSW parts of the area. More detailed studies are needed toclarify different genesis of these prospects.

References

Afzal, P., Ebadi Rajoli, M., 2010. Separating geochemical anomalies byapplying fractal concentration-area method, Case study, Kerverporphyry system, SE Iran. In: The 1st International AppliedGeological Congress, Department of Geology, Islamic AzadUniversity, Mashad, Iran, pp. 190–195.

Afzal P, Khakzad A, Moarefvand P, Rashidnejad Omran N, Esfandiari B,Fadakar Alghalandis Y (2010) Geochemical anomaly separation bymultifractal modeling in Kahang (Gor Gor) porphyry system,Central Iran. J Geochem Explor 104:34–46

Afzal P, Dadashzadeh Ahari H, Rashidnejad Omran N, Aliyari F (2013)Delineation of gold mineralized zones using concentration–volumefractal model in Qolqoleh gold deposit, NW Iran. Ore Geol Rev 55:125–133

Agterberg FP (1995) Multifractal modeling of the sizes and grades ofgiant and supergiant deposits. Int Geol Rev 37:1–8

Agterberg, F.P., Cheng, Q.M., Wright, D.F., 1996. Fractal model-ling of mineral deposits. In: Elbrond, J., Tang, X. (Eds.),Proceedings of the International Symposium on theApplication of Computers and Operations Research in theMinerals Industries, Montreal, pp. 43–53

Akbarpour A, Gholami N, Azizi H, Torab FM (2012) Cluster and R-mode factor analyses on soil geochemical data of Masjed–Daghiexploration area, northwestern Iran. Arab J Geosci. doi:10.1007/s12517-012-0596-4

Alavi M (1994) Tectonics of the Zagros orogenic belt of Iran: new dataand interpretation. Tectonophysics 229:211–238

Aliyari F, Rastad E, Zengqian H (2007) Orogenic gold mineralization inthe Qolqoleh deposit, Northwestern Iran. Resour Geol 57(3):269–282

Aliyari F, Rastad E, Arehart GB (2009) Geology and geochemistry of D-O-C isotope systematics of the Qolqoleh Gold Deposit,Northwestern Iran; implications for ore genesis. Ore Geol Rev 36:306–314

Aliyari F, Rastad E, Mohajjel M (2012) Gold deposits in the Sanandaj–Sirjan zone: orogenic gold deposits or intrusion-related gold sys-tems. Resour Geol 62(3):296–315

Archibald NJ, Gow P, Boschetti F (1999) Multiscale edge analysis ofpotential field data. Explor Geophys 30:38–44

Asadi HH, Voncken JHL, Kühnel RA, Hale M (2000) Petrography,mineralogy and geochemistry of the Zarshuran Carlin-like golddeposit, northwest Iran. Mineral Deposita 35(7):656–671

Austin JR, Blenkinsop TG (2008) The cloncurry lineament: geophysicaland geological evidence for a deep crustal structure in the easternsuccession of the Mount Isa Inlier. Precambrian Res 163:50–68

Austin JR, Blenkinsop TG (2009) Local to regional scale structuralcontrols on mineralisation and the importance of a major lineamentin the eastern Mount Isa Inlier, Australia: review and analysis withautocorrelation and weights of evidence. Ore Geol Rev 35:298–316

Babakhani, A.R., Hariri, A., Farjandi, F., 2003. Geological map of Saqez(1:100 000 scale). Geological Survey and Mineral Exploration ofIran (GSI).

Baranov V (1957) New method for interpretation of aeromagnetic maps-pseudo-gravimetric anomalies. Geophysics 22:359–383

Beus, A.A., Grigorian, S.V., 1977. Geochemical exploration methods formineral deposits. Applied Publishing, Wilmette, IL, translated fromRussian, 287 pp.

Bierlein FP, Murphy FC, Weinberg RF, Lees T (2006) Distribution oforogenic gold deposits in relation to fault zones and gravity gradi-ents: targeting tools applied to the Eastern Goldfields, YilgarnCraton, Western Australia. Miner Deposita 41:107–126. doi:10.1007/s00126-005-0044-4

Blenkinsop T (1994) The fractal distribution of gold deposits: two exam-ples from the Zimbabwe Archaean Craton. In: Kruhl JH (ed)Fractals and Dynamic Systems in Geoscience. Springer, BerlinHeidelberg, pp 247–258

Carlson CA (1991) Spatial distribution of ore deposits. Geology 19(2):111–114

Carranza EJM (2009) Controls on mineral deposit occurrence inferredfrom analysis of their spatial pattern and spatial association withgeological features. Ore Geol Rev 35:383–400

Cheng QM (1999) Spatial and scaling modeling for geochemical anom-aly separation. J Geochem Explor 65:175–194

Cheng QM, Agterberg FP, Ballantyne SB (1994) The separation ofgeochemical anomalies from background by fractal methods. JGeochem Explor 51:109–130

Cooper GRJ, Cowan DR (2006) Enhancing potential field datausing filters based on the local phase. Comput Geosci 32:1585–1591

Cooper GRJ, Cowan DR (2008) Edge enhancement of potential-fielddata using normalized statistics. Geophysics 73:H1–H4

Davis JC (2002) Statistics and data analysis in geology, 3rd edn. Wiley,New York, pp 342–353

Deng J, Wang QF, Wan L, Yang LQ, Liu XF (2007) Singularity of Audistribution in alteration rock type deposit, an example fromDayingezhuang gold ore deposit. In: Zhao PD, Agterberg F,Cheng QM (eds) The 12th Conference of the InternationalAssociation for Mathematical Geology. China University ofGeosciences Press, Wuhan, pp 44–47

Deng J, Wang QF, Wan L, Yang LQ, Zhou L, Zhao J (2008) The randomdifference of the trace element distribution in skarn and marblesfrom Shizishan orefield, Anhui Province, China. J China UnivGeosci 19(4):123–137

Deng J, Wang Q, Yang L, Wang Y, Gong Q, Liu H (2010) Delineationand explanation of geochemical anomalies using fractal models inthe Heqing area, Yunnan Province, China. J Geochem Explor105(3):95–105

Feder, J., 1988. Fractals. Plenum, New York, 283 ppFerreira F, de Castro L, Bongiolo A, de Souza J, Romeiro M (2011)

Enhancement of the total horizontal gradient of magnetic anomalies

5946 Arab J Geosci (2015) 8:5935–5947

using tilt derivatives: Part II — Application to real data. SEG TechProgram Expanded Abstr 2011:887–891

Ferreira F, de Souza J, Bongiolo A, de Castro L (2013) Enhancement ofthe total horizontal gradient of magnetic anomalies using the tiltangle. Geophysics 78:J33–J41

Ghasemi A, Talbot CJ (2006) A new tectonic scenario for the Sanandaj–Sirjan Zone (Iran). J Asian Earth Sci 26:683–693

Goncalves, M.A., Vairinho, M., Oliveira, V., 1998. Study of geochemicalanomalies in Mombeja area using a multifractal methodology andgeostatistics. In: Buccianti, A., Nardi, G., Potenza, R. (eds) IVIAMG ’98. De Frede, Ischia Island, Italy, pp 590–595

Goncalves MA, Mateus A, Oliveira V (2001) Geochemical anomalyseparation bymultifractal modeling. J Geochem Explor 72(2):91–114

Hassanpour S, Afzal P (2013) Application of concentration–number (C–N) multifractal modeling for geochemical anomaly separation inHaftcheshmeh porphyry system, NW Iran. Arab J Geosci 6:957–970

Heidari, M., 2004. Mineralogy, geochemistry and fabrics of gold miner-alization in the Kervian ductile shear zone (southwest of Saqez,Kordestan province). M.Sc. Dissertation, University of TarbiatModares, Tehran, Iran, 245 pp

Heidari M, Rastad E, Mohajjel M, Shamsa MJ (2006) Gold mineraliza-tion in ductile shear zone of Kervian (southwest of Saqez-Kordestanprovince). Geosciences 58:18–37

Heidari M, Ghaderi M, Afzal P (2013) Delineating mineralized phasesbased on lithogeochemical data using multifractal model in Touzlarepithermal Au-Ag (Cu) deposit, NW Iran. Appl Geochem 31:119–132

Henson PA, Blewett RS, Roy IG, Miller JML, Czarnota K (2010) 4Darchitecture and tectonic evolution of the Laverton region, easternYilgarn Craton, Western Australia. Precambrian Res 183(2):338–355

Holden D, Archibald NJ, Boschetti F, Jessell MW (2000) Inferringgeological structures using wavelet-based multiscale edge analysisand forward models. Explor Geophys 31:617–621

Hornby P, Boschetti F, Horowitz FG (1999) Analysis of potential fielddata in the wavelet domain. Geophys J Int 137:175–196

Jacobsen BH (1987) A case for upward continuation as a standardseparation filter for potential-field maps. Geophysics 52:1138–1148

Li L (2013) Improved edge detection tools in the interpretation of poten-tial field data. Explor Geophys 44(3):128–132

Li C, Ma T, Shi J (2003) Application of a fractal method relatingconcentration and distances for separation of geochemical anoma-lies from background. J Geochem Explor 77:167–175

Lima A, De Vivo B, Cicchella D, Cortini M, Albanese S (2003)Multifractal IDW interpolation and fractal filtering method in envi-ronmental studies: an application on regional stream sediments of(Italy), Campania region. Appl Geochem 18(12):1853–1865

Ma G, Li L (2012) Edge detection in potential fields with the normalizedtotal horizontal derivative. Comput Geosci 41:83–87

Mandelbrot BB (1983) The fractal geometry of nature. W. H. Freeman,San Fransisco, 468 pp

Miller HG, SinghV (1994) Potential field tilt—a new concept for locationof potential field sources. J Appl Geophys 32:213–217

Muller J, Kylander M, Martinez-Cortizas A, Wüst RAJ, Weiss D, BlakeK, Coles B, Garcia-Sanchez R (2008) The use of principle compo-nent analyses in characterizing trace and major elemental distribu-tion in a 55 kyr peat deposit in tropical Australia: implications topaleoclimate. Geochim Cosmochim Acta 72:449–463

Neawsuparp K, Charusiri P, Meyers J (2005) New processing of airbornemagnetic and electromagnetic data and interpretation for subsurface

structures in the Loei area, Northeastern Thailand. ScienceAsia 31:283–298

Niroomand S, Goldfarb RJ, Moore F, Mohajjel M, Marsh EE (2011) TheKharapeh orogenic gold deposit: geological, structural, and geo-chemical controls on epizonal ore formation in West AzerbaijanProvince, Northwestern Iran. Mineral Deposita 46:409–428

Raines GL (2008) Are fractal dimensions of the spatial distribution ofmineral deposits meaningful. Nat Resour Res 17:87–97

Robert F, Brommecker R, Bourne BT, Dobak PJ,McEwan CJ, Rowe RR,Zhou X (2007) Models and exploration methods for major golddeposit types. In: Milkereit B (ed) Proceedings of Exploration 07:Fifth Decennial International Conference on Mineral Exploration,691–711

Rose AW, Hawkes HE, Webb JS (1979) Geochemistry in mineral explo-ration, 2nd edn. Academic, New York, 657 pp

Salem A, Williams S, Fairhead JD, Ravat D, Smith R (2007) Tilt-depthmethod: a simple depth estimation method using first-order magnet-ic derivatives. SEG Lead Edge 26(12):1502–1505

SimBL, Agterberg FP, Beaudry C (1999) Determining the cutoff betweenbackground and relative base metal contamination levels usingmultifractal methods. Comput Geosci 25:1023–1041

Tajeddin, H., 2011. Gold ore controlling factors in metamorphic rocks ofSaqez–Sardasht, NW of Sananda–Sirjan metamorphic zone. Ph.D.Dissertation, Tarbiat Modarres University, Tehran, Iran, 436 pp

Tajeddin, H., Dorri, M., Niroomand, S., 2004. Gold mineralization inBarika sheared zone (East of Sardasht, western Azarbaijan). In:22nd Geoscience Symposium, Geological Survey of Iran, p. 54 (InPersian with English abstract).

Tajeddin, H., Shamsa, M.J., Abedian, N., Niroomand, S., 2006. Goldmineralization styles in Saqez–Piranshahr area. In: 24th GeoscienceSymposium, Geological Survey of Iran, p. 31 (In Persian withEnglish abstract).

Turcotte DL (2002) Fractals in petrology. Lithos 65(3–4):261–271Verduzco B, Fairhead JD, Green CM, MacKenzie C (2004) New insights

into magnetic derivatives for structural mapping. Lead Edge 23(2):116–119

Wang QF, Deng J, Wan L, Zhao J, Gong QJ, Yang LQ, Zhou L, Zhang ZJ(2008)Multifractal analysis of the element distribution in skarn-typedeposits in Shizishan Orefield in Tongling area, Anhui province,China. Acta Geol Sin 82(4):896–905

Wang QF, Deng J, Liu H, Yang LQ, Wan L, Zhang RZ (2010) Fractalmodels for ore reserve estimation. Ore Geol Rev 37(1):2–14

Wei S, Pengda Z (2002) Theoretical study of statistical fractal model withapplications to mineral resource prediction. Comput Geosci 28(3):369–376

Yarmohammadi, A., 2006. Mineralogy, geochemistry, structure, textureand genesis of gold (Ag, base metals and barite) in Barika mineralarea (east of Sardasht), Iran. M.Sc. thesis, Tarbiat ModaresUniversity, Tehran, Iran, 199p

Yousefi M, Kamkar-Rouhani A, Carranza EJM (2012) Geochemicalmineralization probability index (GMPI): a new approach to gener-ate enhanced stream sediment geochemical evidential map for in-creasing probability of success in mineral potential mapping. JGeochem Explor 115:24–35

Ziaii M, Carranza EJM, Ziaei M (2011) Application of geochemicalzonality coefficients in mineral prospectivity mapping. ComputGeosci 37(12):1935–1945

Zuo R (2011) Identifying geochemical anomalies associated with Cu andPb-Zn skarn mineralization using principal component analysis andspectrum-area fractal modelling in the Gangdese Belt, Tibet (China).J Geochem Explor 111(1–2):13–22

Arab J Geosci (2015) 8:5935–5947 5947