Natural Resources Research

, Volume 26, Issue 4, pp 429–441

Analysis of Zoning Pattern of Geochemical Indicators for Targeting of Porphyry-Cu Mineralization: A Pixel-Based Mapping Approach

Original Paper


In this paper, a pixel-based mapping of geochemical anomalies is proposed to avoid estimation errors resulting from using interpolation methods in the modeling of anomalies. The pixel-based method is a discrete field modeling of geochemical landscapes for mapping lithogeochemical anomalies. In this method, the influence area of each composite rock sample is the whole area covered by a pixel where the materials of the sample were taken from. In addition to the pixel-based method, because delineation of mineral exploration target areas using geochemical data is a challenging task, the application of metal zoning concept is demonstrated for vectoring into porphyry mineralization systems. In this regard, different geochemical signatures of the deposit-type sought were mapped in a model. Application of the proposed pixel-based method and the metal zoning concept is a powerful tool for targeting areas with potential for porphyry copper deposits.


Exploration targets Lithogeochemical data Geochemical indicators Metal zoning patterns Porphyry Cu Pixel-based model 


Geochemical signatures are important for mineral prospecting. Mapping of geochemical anomalies based on element concentrations in stream sediment and lithogeochemical samples has been achieved by studying dispersion pattern of indicator elements of mineral deposits (e.g., Bonham-Carter 1994; Twarakavi et al. 2006; Nykänen et al. 2008; Carranza 2008; El-Makky and Sediek 2012; Yousefi et al. 2012, 2013, 2014; Abdolmaleki et al. 2014; Luz et al. 2014; Asadi et al. 2015; Parsa et al. 2016a, b, c).

Allocating the area of influence of geochemical samples is an important component of modeling geochemical anomalies (Govett 1983; Bonham-Carter and Goodfellow 1984, 1986; Bonham-Carter 1994; Carranza and Hale 1997; Moon 1999; Carranza 2004, 2010a; Spadoni et al. 2004; Spadoni 2006). In this regard, researchers have proposed methods for reasonable allocation of the area of influence of individual geochemical samples. However, the majority of previous studies were concerned with the area of influence of stream sediments samples (e.g., Bonham-Carter 1994; Carranza and Hale 1997; Moon 1999; Spadoni et al. 2004; Spadoni 2006; Cheng 2007; Carranza 2010a). Determining the area of influence of lithogeochemical samples has rarely been investigated (Govett 1983; Grunsky 1986; Harris et al. 2000). The relevant studies have focused on using interpolation methods; however, every interpolation method introduces estimation errors such as smoothing effect (e.g., Hu et al. 2013; Parsa et al. 2016a). In this paper, a pixel-based mapping of geochemical anomalies is proposed to avoid estimation errors resulting from interpolation. The pixel-based approach is a discrete field modeling of geochemical landscapes (Bonham-Carter and Goodfellow 1984, 1986; Bonham-Carter 1994; Carranza and Hale 1997; Moon 1999; Spadoni et al. 2004; Carranza 2004, 2010b; Yousefi et al. 2013) that can be used for mapping lithogeochemical anomalies. In this method, the area of influence of each composite rock sample is the whole area of a pixel where the materials of the sample were taken from. The element contents (or derived geochemical signatures) of composite samples are assigned to their corresponding pixels (i.e., pixel-based modeling), and thus, estimation errors resulting from smoothing effect of the any interpolation technique are avoided.

In mapping geochemical anomalies of different indicator elements of a certain deposit type, samples with anomalous concentrations of certain indicator elements are generally not located at the same positions, but are located differently around the deposit. Thus, for vectoring into reliable anomaly areas and obtaining a stronger signature of the deposit type, researchers have analyzed and integrated values of individual indicator elements (Bonham-Carter 1994; Carranza 2008; Nykänen et al. 2008; Yousefi et al. 2012, 2013; Abdolmaleki et al. 2014; Asadi et al. 2015).

For prospecting porphyry-Cu deposits at certain scales, many features of the deposits (e.g., geological, geochemical, and structural features) need to be taken into account (Sillitoe 2010; Yousefi and Carranza 2015a, b, c, 2016a, b). For deposit scale exploration, Sillitoe (2010) demonstrated that the concept of metal zoning can be employed to recognize the center of porphyry-Cu systems. Application of the metal zoning concept allows to delimit exploration targets of porphyry-Cu deposits more precisely because such deposits are related to and centered on an underlying porphyry pluton (e.g., Meinert et al. 2005; Sillitoe 2010). Therefore, this study aimed to demonstrate (1) generation of pixel-based models of geochemical anomalies and (2) zoning pattern analysis of geochemical indicator elements for delimiting exploration targets. A lithogeochemical data set for prospecting porphyry-Cu deposits in the Kerman province, southeast of Iran, was used in this study.

The Study Area

The study area, the same area studied by Yousefi (2017), is a very small part of the Urumieh–Dokhtar magmatic arc of Iran. The area, occupying ~5 km2, is covered by the 1:5000 scale geological map prepared by the National Iranian Copper Industries Company (NICICO). The lithostratigraphic map of the study area is shown in Figure 1. In the study area, the outcropping igneous rocks are granodiorites, quartz-monzonites, leucogranites, and micro-granodiorites with ages of Oligocene–Miocene. Volcanic rocks consist of andesite lavas and tuffs with the age of Eocene (Fig. 1), according to unpublished reports by the NICICO. The characteristics of the Urumieh–Dokhtar magmatic arc and the conceptual model of porphyry-Cu mineralization in this belt and in the study area are described in Yousefi and Carranza (2015a, b, c, 2016a, b) and Yousefi (2017).
Figure 1

Geological map of the study area

Methods and Results

Sampling Scheme

For lithogeochemical exploration, samples are commonly collected as a composite of some subsamples (e.g., Selinus 1983; Rugless and Teale 1987) in order to recognize their bulk geochemical signatures (Grunsky 1986). For this, regular sampling scheme with a certain grid pixel size (e.g., Selinus 1981, 1983; Costa and Araujo 1996) or irregular sampling scheme without a definite sampling grid or pixel size (e.g., Grunsky 1986) could be conducted. The study area in this paper was divided into 100 m × 100 m pixels (cells) as a regular sampling scheme (e.g., Selinus 1981, 1983; Costa and Araujo 1996) resulting in 540 pixels (Fig. 2). Selection of the pixel size is a subjective task, which is carried out respecting geological variability and complexity and lithological heterogeneity and anisotropy of a study area. Small grid or cell size could be used for areas with high geological complexity and variability, whereas large grid or cell size could be used for areas with low geological complexity and variability. However, the choice of cell size affects exploration cost. Meaning the smaller the cell size the higher the number of samples, and so the higher exploration cost and vice versa. Considering the factors mentioned above, the sampling grid or cell size is determined by expert judgment. In this study, considering also field limitations (i.e., presence of river and stream, lack of outcropping rocks, presence of anthropogenic factors like building, dam, and factory), 30 pixels were excluded, and so 510 composite samples were taken. Sampling in each pixel was carried out by taking 4–5 kg rock samples (40–50 subsamples, each weighing ~100 g) from every outcropping rock that is typical of the major rock type in the study area. The average surface of the influence area of each subsample measures 200–250 m2. The samples per pixel consisted of unweathered rock. Multi-element (Zn, Pb, Ag, As, Cu, and Mo) concentration data from the rock samples that were collected, prepared, and analyzed by the NICICO in 2014 were used in this study. Concentrations of elements were determined by inductively coupled plasma-mass spectrometry method. The detection limits were: 1 ppm for Cu, 0.5 ppm for Mo, 1 ppm for Pb, 1 ppm for Zn, 0.1 ppm for Ag, and 0.5 ppm for As. The method of Thompson and Howarth (1976) was applied for assessing the analytical precision using duplicated samples. Analytical precision for most elements was acceptable (i.e., better than 20%).
Figure 2

Sampling and eliminated cells (or pixels)

Pixel-Based Modeling of Geochemical Landscape

Geochemical landscapes have been modeled from point data by creating maps with point symbols, contours (Govett 1983), sample catchment basins (Bonham-Carter and Goodfellow 1984, 1986; Bonham-Carter et al. 1987; Bonham-Carter 1994; Carranza and Hale 1997; Moon 1999; Spadoni et al. 2004; Carranza 2010b; Abdolmaleki et al. 2014), stream orders (Carranza 2004), extended sample catchment basins (Spadoni 2006), and weighted drainage catchment basin (Yousefi et al. 2013). Among these techniques that have been used in the modeling and mapping stream sediment geochemical anomalies, only point symbols and contouring or interpolation can be used to model lithogeochemical anomalies (e.g., Govett 1983; Selinus 1983; Grunsky 1986; Harris et al. 2000; He et al. 2013).

Dispersion of indicator elements in geochemical landscape is affected by many factors such as geological factors (e.g., lithology types and structural features). In addition, because anomalous patterns caused by mineralization processes can be highly complex as shown by their spatial and frequency properties (Zuo et al. 2009a; He et al. 2013), the details of such processes, which control variations of rock compositions in an area, are poorly known using surficial geochemical data such as from stream sediments. Furthermore, the gradient of chemical compositions in rock samples is not linear because of nonlinear variations of factors that could influence dispersion of geochemical elements (Yilmaz 2003; Xie et al. 2010). Considering the complexity of geochemical anomaly patterns that is affected by complexity of geological factors even in areas with simple geology (Van Loon 2002), and because of the limitation in the number of samples taken in an area, it is important to effectively define the area of influence of each sample for mapping geochemical anomalies.

Interpolation for contouring of lithogeochemical data is essential because it is not possible to take samples continuously and so samples are taken at regular or irregular intervals. Thus, contour maps carry the estimation errors resulting from interpolation. In this paper, considering the pixel-based sampling scheme, the element contents of a composite sample or the value of a derived geochemical signature is assigned to the whole of the pixel. Figure 3 shows pixel-based models of Cu, Mo, Zn, Pb, Ag, and As in rocks of the study area. These elements were used because, as Sillitoe (2010) mentioned, they can reveal metal zoning patterns around porphyry-Cu systems.
Figure 3

Pixel-based distribution maps of indicator elements (in ppm) of porphyry-Cu deposits

For comparison of the proposed approach with maps of interpolated geochemical values, Cu content of the samples was gridded into raster maps using ordinary kriging. There are three important parameters for ordinary kriging, namely variogram type, search radius, and cell size, which affect the output model of geochemical anomalies (Zuo 2012). In this paper, spherical semivariogram and a search radius with 12 points, the defaulted number suggested by ESRI (2004) and Philip and Watson (1982), were applied in all of the interpolated maps. Because the purpose of this section is to investigate the effect of cell size in the output models of geochemical anomalies, the same variogram type and search radius were applied for different pixel sizes. For this, four different pixel sizes, namely 20 m × 20 m, 50 m × 50 m (smaller than the pixel-based model), 100 m × 100 m (equal to the size of the pixel-based model), and 200 m × 200 m (larger than the pixel-based model), were applied to interpolate the Cu data for comparison purpose (Fig. 4). The effect of using different pixel sizes with the same other parameters of interpolation method was investigated by Zuo (2012) as well.
Figure 4

Interpolated Cu contents (ppm) obtained by kriging with a grid size of (a) 20 m × 20 m, (b) 50 m × 50, (c) 100 m × 100, and (d) 200 m × 200 m

The lowest and highest element contents for Cu, which were obtained by chemical analysis of the samples, were 2 and 12,375 ppm, respectively (Fig. 3a). As can be seen in Figure 4, the minimum element contents in Figure 4a, b, and d are −947.16, −617.12, and −398.80, respectively, based on interpolation with pixel sizes of 20 m × 20 m, 50 m × 50 m, and 200 m × 200 m; however, negative element contents are meaningless. Thus, distribution patterns of geochemical variables are adversely influenced by the smoothing effect of interpolation (e.g., Hu et al. 2013; Parsa et al. 2016a) and the ensuing estimation errors. The maximum Cu values in Figure 4b and d are lower than the highest element content obtained by chemical analysis of the samples. This underestimation could affect decision making for further exploration programs. On the other hand, the minimum element content in Figure 4c, generated with a pixel size of 100 m × 100 m (the same pixel size used in the pixel-based model in Fig. 3a), is 2 ppm, which is equal to the value obtained by chemical analysis. Furthermore, inspection of the models in Figure 4 shows that geochemical populations and anomalies in Figure 4c become more evident than those in Figure 4a, b, and d.

The above comparisons demonstrate that, among the pixel sizes used, the pixel size of 100 m × 100 m yielded the most reliable model of lithogeochemical landscape of the study area. This is the size of the grid cells from which the materials of each lithogeochemical sample were taken from.

Modeling of Multiplicative Geochemical Halos

The presence of different types of evidence would be, compared to a single evidence, more indicative of the presence of a mineral deposit occurrence (Carranza 2008). So, if the distribution patterns of different indicator elements are analyzed simultaneously, a robust geochemical signature for vectoring into mineralized zones is generated (e.g., Bonham-Carter 1994; Nykänen et al. 2008; Carranza 2008; Carranza and Sadeghi 2010; Yousefi et al. 2012, 2013; Abdolmaleki et al. 2014; Asadi et al. 2015; Yousefi 2017). In this regard, multiplicative lithogeochemical anomalies have been used to study primary halos of porphyry-Cu deposits (Grigorian 1992; Ziaii et al. 2009). Mapping multiplicative lithogeochemical anomalies, or ratios of multiplicative anomalies, was proposed to investigate vertical variations in primary halos of mineralization (Solovov 1987, 1990; Grigorian 1992; Ziaii et al. 2011) by analysis of lithogeochemical data (Kitaev 1991).

In this paper, lateral variations of multiplicative geochemical anomalies were examined to reveal zoning patterns of indicator elements for delimiting exploration targets for porphyry-Cu deposit. Subsequently, multiplicative geochemical models of Cu*Mo, Pb*Zn, and Ag*As were first generated. To define thresholds for separation of anomalous and background values of Cu*Mo, Pb*Zn, and Ag*As, a graphical analysis tool was applied as follows. Because of the presence of mineral deposits in the study area, several outliers can be expected in the geochemical data (Ohta et al. 2005) as well as in the Cu*Mo, Pb*Zn, and Ag*As values. Therefore, the boxplot (Fig. 5) was used to determine thresholds for recognizing and separating outliers (e.g., Reimann et al. 2005; Carranza 2008). The boxplots (Fig. 5a, c, and e) show several outliers greater than 30,000, 3000, and 5, respectively, for Cu*Mo, Pb*Zn, and Ag*As. Hence, the threshold values obtained from the boxplots can be used to separate and map anomalous and background values of multiplicative geochemical halos (Fig. 6). For further evaluation of the thresholds obtained by the boxplots (i.e., Fig. 5a, c, and e), the concentration–area (C–A) fractal method (Cheng et al. 1994) was applied to separate background and anomalies in the multiplicative geochemical models (Fig. 5b, d, and f). Comparison of the boxplots with their corresponding C–A plots shows that the threshold values obtained by the former method are equal to the threshold values obtained by the latter method. Thus, the thresholds obtained are reliable. Figure 6 shows binary maps of the separated geochemical populations in the three multiplicative geochemical models.
Figure 5

Boxplots and C–A fractal models of multiplicative geochemical values

Figure 6

Anomaly map of multiplicative geochemical halos

Mapping of Metal Zoning and Exploration Targets

The main purposes of geochemical studies for mineral exploration are (cf. Cheng 2007; Carranza 2008; Zuo and Cheng 2008; Grunsky et al. 2009; Zuo et al. 2009a, b; Carranza 2010b): (1) recognition and delineation of anomaly patterns of indicator elements of the deposit-type sought and (2) delineation of target areas for vectoring into mineralized zones. In this regard, Govett (1983) showed that lateral distribution of anomalous patterns of indicator elements could be used for vectoring into ore zones. Examples of well-developed metal zoning around porphyry-Cu deposits were documented by Jerome (1966), Govett (1983), Lang and Eastoe (1988), Titley (1993), Babcock et al. (1995), and Sillitoe (2010).

In this paper, for better understanding of zoning patterns, as proposed by Sillitoe (2010), the outer limits of multiplicative geochemical anomalies have been delineated (Fig. 7). Mapping anomalies of multiplicative geochemical halos revealed a zoning pattern of Cu–Mo (innermost halo) to Pb–Zn (intermediate halo) to Ag–As (outermost halo). Thus, the Cu–Mo anomalies in the innermost part of zoning pattern in Figure 7 represent a reliable target for further exploration. For evaluation of the target area obtained by analysis of zoning pattern, stockwork zones, as important geological features associated with porphyry-Cu deposits (Sillitoe 2010), and locations of Cu occurrences were used. Stockworks coincide with the internal parts of porphyry-Cu systems (Sillitoe 2010). As shown in Figure 7, the stockwork zones coincide with and extend outward from the target area. Furthermore, most of the Cu occurrences are in the innermost zone (Cu–Mo anomaly area). Various other indicators of porphyry-Cu mineralization (e.g., significant alteration types, silicified veins, porphyritic textures, and economic minerals) have been observed in the field in the target area (refer to Yousefi 2017).
Figure 7

Zoning pattern of geochemical indicators


Contouring of lithogeochemical data, which is dependent on interpolation, may lead to inaccurate mapping of anomaly areas due to smoothing effect of interpolation. The proposed pixel-based method to map lithogeochemical anomalies avoids such bias of interpolation. In this paper, the whole area of a pixel in which several subsamples were gathered is considered the area of influence of a composite rock sample. Composite sampling has been used in stream sediment, lithogeochemical, and soil sampling to map geochemical anomalies (e.g., Selinus 1983; Rugless and Teale 1987; Nude and Arhin 2009; Cicchella et al. 2013). Thus, element contents (or a derived multi-element signature) of composite samples are assigned to corresponding pixels to map geochemical anomalies. This is an example of discrete field modeling of geochemical landscapes (e.g., Carranza 2010a, b; Yousefi et al. 2013). Discrete field modeling has been used mainly in the analysis of stream sediment geochemical data whereby the catchment basin of every sample has been used as the area of influence (Bonham-Carter and Goodfellow 1984; Bonham-Carter 1994; Carranza and Hale 1997; Spadoni et al. 2004; Carranza 2010b; Yousefi et al. 2013).

As demonstrated in this paper, zoning pattern analysis and mapping anomalous values of all indicator elements can be used for vectoring into mineralized zones. The best relative efficiency of mapping geochemical anomalies is achieved when all of the geochemical signatures are considered to evaluate the overall reliability of target areas. In the study area, there is a Cu–Mo anomaly that overlaps with the stockworks zone, surrounded by a Zn–Pb anomaly, which is in turn surrounded by an Ag–As anomaly, over a lateral distance of about 3 km from the Cu–Mo anomaly area. Kilometer-scale halos of indicator elements of porphyry-Cu deposits have been reported by Sillitoe (2010). Thus, the target areas generated by zoning pattern analysis in Figure 7 can be used to guide follow up exploration. However, the zoning pattern analysis demonstrated here for vectoring into porphyry-Cu deposits cannot be used for every type of mineral deposits except those that show a zoning pattern of indicator elements.

In existing methods of modeling geochemical anomalies, selecting the right pixel size is a challenging task (Hengl 2006; Carranza 2009; Zuo 2012). In lithogeochemical anomaly mapping, the area where the material of each composite rock samples was taken from is generally neglected implicitly. However, in the proposed pixel-based modeling of geochemical anomalies, a pixel size is selected before sampling based on consideration of geological features (i.e., lithological units, structures, alterations, and etc.) in the study area.

Similar to the sample catchment basins modeling method, neglecting the un-sampled pixels, even though proper, is a disadvantage of the proposed pixel-based modeling approach of geochemical anomalies. However, if there are few un-sampled pixels, one may allocate to an un-sample pixel the average element contents of its neighboring sampled pixels. Alternatively, as shown in this study, interpolation using a grid size equal to the sampling pixel size could be applied to map anomalies.

The proposed pixel-based method of discrete field modeling of geochemical anomalies avoids estimation errors due interpolation, but it is not bias-free. The bias may come from the choice of pixel size, which is defined before sampling. Thus, farther works are needed to investigate how pixel size could be estimated objectively in regular-grid lithogeochemical sampling programs. This should be investigated with regard to the geological variability and complexity and lithological heterogeneity and anisotropy of a study area.

Concluding Remarks

  1. 1.

    Pixel-based mapping of geochemical anomalies is a discrete field modeling approach, whereby the entire pixel is considered the area of influence of each composite rock sample where the material of the sample was taken from. This method overcomes the estimation error resulting from smoothing effect of interpolation for mapping of geochemical anomalies.

  2. 2.

    Mapping of different lithogeochemical signatures of porphyry-Cu deposits in a single model may reveal the central parts of a porphyry-Cu system, and thus, could be used to vector into mineralized zones and to delimit exploration targets.

  3. 3.

    Metal zoning is a powerful exploration concept once mineral deposits in porphyry systems are prospected. As demonstrated in this paper, mapping lateral zoning patterns of Cu–Mo, Zn–Pb, and Ag–As is worthwhile for targeting of porphyry-Cu mineralization.



Special thanks to Mr. Babaie, head of exploration department of National Iranian Copper Industries Company (NICICO), for some supports. The author thanks Parsolang consulting engineering company, especially Mr. Sahebzamani for supplying necessary material to do this research work. The author thanks Kanazin and Zarnab consulting engineering companies because the field operations were carried out by senior geologists at these companies. The author thanks John Carranza and three anonymous reviewers for their helpful comments.

Copyright information

© International Association for Mathematical Geosciences 2017

Authors and Affiliations

  1. 1.Faculty of EngineeringMalayer UniversityMalayerIran

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