Introduction

Lithogeochemical data, comprising both major oxide and trace elements, are frequently used in geological mapping and tectonic studies to classify rock types, identify chemical variations due to fractionation trends, and characterize tectonic environments. Lithogeochemical data are also useful for mineral exploration studies by providing chemical information on alteration and mineralization patterns. The ultimate goal of the statistical and spatial analysis of lithogeochemical data for mineral exploration is the detection of zones of elevated concentrations (e.g., geochemical anomalies) of oxide or trace elements that may be reflective of mineral deposits (Harris et al. 1999, 2000). One of the major improvements in mineral exploration has been the recognition of regional-scale alteration patterns in hydrothermal systems related to plutonic and volcanic environments (Franklin 1997; Hannington et al. 2003). Alteration indexes allow for more accurate mapping of hydrothermal alteration in rocks and have been instrumental in the discovery of many base and precious metal (especially Cu porphyry) ore deposits in both submarine and subaerial environments (Sillitoe 1995). In Iran, all known porphyry and skarn mineralization occurs in the Cenozoic Sahand-Bazman orogenic belt (Fig. 1). This belt was formed by subduction of the Arabian plate beneath central Iran during the Alpine orogeny (Berberian and King 1981; Pourhosseini 1981; Niazi and Asoudeh 1978) and hosts two major porphyry Cu deposits. The Sar-cheshmeh deposit contains 450 million tonnes of sulfide ore with an average grade of 1.13% Cu and 0.03% Mo (Waterman and Hamilton 1975; Hezarkhani 2006a). The Sungun deposit contains 500 million tonnes of sulfide reserves grading 0.76% Cu and 0.01% Mo (Hezarkhani and Williams-Jones 1998). A number of small and sub-economic porphyry copper deposits (for example Sonajil) are all associated with mid- to late-Miocene diorite/granodiorite to quartz–monzonite stocks. Important Cu skarn deposits also are found in and around the Oligocene–Miocene volcanic rocks in this belt (Mollai et al. 2009; Hezarkhani 2006b; Mollai 2004, 2005, 2007). This area was already studied on 1:25,000 regional scales by stream sediment geochemical exploration phase and has shown significant geochemical anomalies of Cu (Kavoshgaran Co. 2007). Based on the results, the area was selected for lithogeochemical exploration survey. One of the questions raised is whether there is a relationship between copper mineralization and alteration in the region. This paper demonstrates the use of regional geochemical surveys for Cu exploration and local lithogeochemical for mapping alteration intensity and facies and to introduce diamond exploration methods, at the Sonajil Cu porphyry mineralization.

Fig. 1
figure 1

Geology map of Iran (modified from Stoklin 1977; Shahabpour 1994) and detailed geological map of the Sonajil area showing the distribution of different igneous suites (modified from the geological map of Sonajil by the National Iranian Copper Industries Company-Exploration Department)

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Geology setting

The Sonajil intrusive is located about 60 km southeast of Ahar, in Azarbaijan province in northwestern Iran (Fig. 1) and is intruded into by andesitic trachytic Eocene pyroclastics and agglomerate. To the center, the stock is hydrothermally altered that contains sub-economic copper mineralization (Hezarkhani 2008). The oldest rocks exposed in the study area are a 600-m sequence of Eocene with intercalations of andesite and andesitic breccias and a 1,500-m-thick sequence of Middle to early Late Tertiary, intermediate, calc-alkaline lavas, and tuffaceous rock, intruded by numerous calc-alkaline andesitic dykes (Fig. 1). The entire stock and volcanic cover rocks seem to be located within a caldera (Hezarkhani 2008). The intrusive rocks at Sonajil area mainly are (1) diorite/granodiorite, (2) quartz–monzonite/monzonite, and (3) andesitic and related dykes, in order of emplacement. The monzonite/quartz–monzonites are mainly porphyritic and exposed to the south of the stock, and intrude the monzonite/quartz–monzonite. The intrusive is generally unfoliated and porphyritic. Rocks in the study area were subjected to intense hydrothermal alteration, especially within and adjacent to the diorite/granodiorite intrusion. Even in the outermost part of the area, it is impossible to find completely fresh igneous rocks in outcrop. Several stages of weak hydrothermal alteration and associated poor mineralization have produced new minerals, created new textural relationships, and in many cases, obliterated the primary character of the rocks (Hezarkhani 2008).

Sampling and analysis

To design the sampling pattern, results of previous geochemical stream sediment and heavy mineral studies, a preliminary geological map, and field notes were used. To cover all of the area, grid cells 100 × 100 taking 1,248 lithogeochemical samples were used (Fig. 2). For each sample, at least 40 pieces of surface rock chip samples (100 g) were collected from 40 points randomly inside each cell, and minimum sample weight is 4 kg. Measurement of 44 elements (Ag, Al, As, Au, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Hg, K, La, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, Re, S, Sb, Sc, Sn, Sr, Te, Th, Ti, Tl, U, V, W, Y, Zn, and Zr) by inductively coupled plasma-mass spectrometry method using natural rock standards as reference samples for calibration and major oxides that include MgO, Fe2O3, Al2O3, Na2O, K2O, CaO, and SiO2 by X-ray fluorescence spectrometer method in the 1,248 rock samples was performed in AMDEL Australian laboratory. Appropriate standards and sample analytical duplicates were introduced in each sample batch at a rate of 5%. With the aim of providing the most real indices and indicators of individual constituents by use of statistical treatment of the analytical results, the minimum values are considered to be the amount represented by half of the values of the lower sensitivity limit of the corresponding chemical element (National Iranian Copper Industries 2008).

Fig. 2
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Lithogeochemical sampling pattern location cells in the study area

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Results

Geochemical characteristics of elements are summarized in Table 1, with the values for minimum, maximum, standard deviation (Std. Dev.), mean, range, kurtosis, and skewness. It should be noted that copper is highly enriched in the region, and its value varies from 7.10 to 21,200 ppm. Among polymetal elements such as Pb, Zn, Mo, Au, As, Ag, Ni, Co, and U, Cu concentration has the greatest range of variation with standard deviation equal to 956.49 (Table 1).

Table 1 Summary of statistical parameters of element concentrations of samples from the Sonajil area
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The concentration ranges for Cu and the other trace elements in the field are presented in Fig. 3. According to drawing diagrams in Fig. 3, values out of each category have been defined. Values outside the category of some elements are indicative of a particular phenomenon. For example, values outside of K denoted phyllic alteration and Al guidance for advanced argillic. For elements such as As, Au, Cu, Mo, Pb, Zn, and Sb, they indicate metal mineralization and local enrichment of these elements. This was about elements such as Ca, Na, Ti, and Zr which are due to differences in lithology in rock units region.

Fig. 3
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Histograms of the elements in the Sonajil area

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The copper, gold, arsenic, molybdenum, antimony, and zirconium distributions all show asymmetrical, positively skewed distribution patterns. Other elements in Fig. 3 showed slightly normal or symmetrical. There is no good correlation between copper and other metals. Most of the observed positive correlation between elements in the area was obtained by Mg–Co and Th–U with correlation coefficients equal to 0.8 and 0.82, respectively (Fig. 4). Between Cu and rock-forming elements such as K, Na, and Al, the constant correlation is seen, due to the lack of diversity in the rock of the study area.

Fig. 4
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Correlation between paired elements in the study area

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Geochemical distribution

Distribution maps for several elements were prepared (Fig. 5) based on the kriging method (Panahi et al. 2004). The objective of the kriging is to provide a probability-based estimate of trace and heavy element distribution and their spatial distribution. High amounts of arsenic have a wide distribution in the north, northeast, and southwest parts of the region. Au is only in two points in the northeast, and limited areas in the southwest are seen as an anomaly. The highest amounts of Sb are located in the southeast region. The heterogeneous distributions of Pb and Zn are variable in all areas. Mo dispersion is greatest in the northeast, and in the southwestern area, it shows an anomaly, with fewer dispersions. Copper has isolated anomalies, and in scattered locations in the southwest, central, and northeast areas, it has an anomaly (Fig. 5).

Fig. 5
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Distribution maps for Au, As, Sb, S, Zn, Pb, Mo, and Cu element in study area

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Alteration

The rocks of the area have hydrothermal alteration (Hezarkhani 2008). Consequently, to find relationships between mineralization and alteration in the area, the two types of alteration maps were produced. The first series was based on the alteration indices (sericitization, chloritization, Spitz–Darling, and Hashimoto alteration index) and alterations obtained from aster data based on satellite images (phyllic, argillic, and iron oxide). Sericitization index (K2O/(Na2O + CaO)) (Kishida and Kerrich 1987) in two areas in the northeast and southwest has the largest range of their values (Fig. 6). The index distribution map of Spitz–Darling (Al2O3/Na2O ratio) (Spitz and Darling 1978) shows an enrichment of Al2O3 rather Na2O unit that refers to empty sodic in rocks of the region. High-grade area of this variable in the center, east, and southwest of the region is visible as small discrete areas. The Hashimoto index (Harris et al. 2000) distribution map that shows input of K and Mg and exit of Ca and Na confirms possible locations specified by mineralization ((MgO + K2O)/(MgO + K2O + CaO + Na2O)). This index is widely spread in the east and west regions and indicates that there is further potential of these areas for mineralization (Fig. 6). The index distribution map of chloritization (Yoshihiro 2009) has a high-grade area in the NE, SW, and central part of area. Remote sensing data (Aster data) were used for extraction of argillic, phyllic, and iron oxide alteration (Azizi et al. 2010) (Fig. 7).

Fig. 6
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Distribution map of sericitization, chloritization, Spitz–Darling, and Hashimoto alterations index

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Fig. 7
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Distribution map of phyllic, argillic, and iron oxide alteration in area

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Conclusion

The relationship between alteration and mineralization has been documented by many researchers (Hezarkhani and Williams-Jones 1998; Angeles et al. 2008). By comparison of the anomalies of copper and other elements in the region with alteration indices and alteration halo, it becomes clear that despite the anomaly of copper and other elements such as gold, molybdenum, lead, arsenic, and antimony of not being quite consistent, almost all of them are within the alteration zones. This relationship in Sb, Zn, and Pb is less than the other elements. Also, mineralization associated with iron oxide alteration in this region is low. The results show that with high reliability, indicators and halo alteration can be used in copper and other metal explorations. This research has been done on a local area, and because of the large number of samples, it takes too much time and cost. The results show that there is a high relationship between copper mineralization and alteration in the area. According to the existence potential for copper mineralization in the Azerbaijani region, the results can be used in future exploration work in the region especially for areas in the regional exploration and polymetal mineralization.