Stream sediment geochemical data analysis for district-scale mineral exploration targeting: Measuring the performance of the spatial U-statistic and C-A fractal modeling

Ore Geology Reviews 113 (2019) 103115

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Stream sediment geochemical data analysis for district-scale mineral exploration targeting: Measuring the performance of the spatial U-statistic and C-A fractal modeling

T

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Saeid Ghasemzadeha, Abbas Maghsoudia, , Mahyar Yousefib, Mark J. Mihalaskyc a

Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran Faculty of Engineering, Malayer University, Malayer, Iran c United States Geological Survey, Spokane, Washington, United States b

A R T I C LE I N FO

A B S T R A C T

Keywords: Geochemical footprints Anomaly identification Ore-forming processes Exploration targets

Recognition of mineralization-related geochemical footprints and modeling their multi-element dispersion patterns are important aspects to consider when “vectoring” toward undiscovered ore deposits. The collection, analysis, and interpretation of stream sediment geochemical data together make an exploration method that has proven to be successful at the district scale mineral exploration targeting. Identifying the possible sources of stream sediment geochemical anomalies and mapping evidence (i.e., footprints) of the underlying ore-forming processes, however, are not trivial tasks. This is because stream sediment samples represent transported material reflecting the entire geology upstream from the sample locations. Furthermore, indicator element distribution patterns are commonly strongly affected by local factors such as regolith, topographic gradient, vegetation density, and/or climate. Therefore, there is a need for finding a better geochemical anomaly separation method regarding the nature of geochemical data obtained from the area sampled. The main objectives of this study were (1) evaluating and comparing the spatial U-statistic and concentration–area fractal modeling methods of anomaly identification, both amenable to spatial analysis, for recognizing geochemical footprints of porphyry copper mineralization, and (2) measuring their performance in the context of district-scale exploration targeting in an area located in southeast Iran. Subsequently, the methods were first evaluated on a dataset of element contents to decompose anomalous populations. Finally, the geochemical model that proved more efficient with respect to predicting the known mineral deposits was integrated with additional evidence maps to delineate exploration targets. Our evaluation of the resulting geochemical targeting model demonstrated that the targets derived from this method are robust and worthy of follow-up exploration.

1. Introduction Geochemical anomaly mapping is a way for vectoring toward undiscovered mineralized zones by identifying anomalous concentrations of pathfinder indicator elements in different sampling media (e.g., soils, rocks, and stream sediments; Xie et al., 2008; Grunsky et al., 2009; Andersson et al., 2014; Wang et al., 2015a,b; Parsa, et al 2018; Ghezelbash et al., 2019a). Similarly, the discrimination of anomalous element concentrations related to ore forming processes from background values is a fundamental task in geochemistry for mineral exploration (Carranza and Hale, 1997). Therefore, geochemists face the challenge of how best to highlight significant mineralization-related geochemical anomalies in various landscapes using geochemical datasets derived from different sample media and laboratory analytical

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techniques. Many approaches have been proposed aimed at defining significant thresholds for separation of anomalous from background values (e.g., Howarth and Sinding-Larsen, 1983; Bonham-Carter and Goodfellow, 1984; Cheng et al, 1994; Cheng, 1999; Zuo et al., 2009a; Parsa et al., 2016b; Xiao et al., 2018). These methods are categorized into frequency-based and spatial frequency-based general approaches (e.g., Chipréset al., 2009; Zuo et al., 2013a; Yang et al., 2015). The most effective delineation of geochemical anomalies is achieved when using most of the analyzed sample results, such as element concentrations, frequency distributions, correlations and variances, geometric properties (shape and extent), and magnitude of anomalous areas (Cheng, 1999, 2007). The effectiveness of the frequency-based methods (e.g., classical statistic approaches; Hawkes and Webb, 1963; Turkey, 1977; Rose and Hawkes, 1979; Reimann et al., 2002) is adversely influenced

Corresponding author. E-mail address: [email protected] (A. Maghsoudi).

https://doi.org/10.1016/j.oregeorev.2019.103115 Received 24 February 2019; Received in revised form 25 August 2019; Accepted 4 September 2019 Available online 06 September 2019 0169-1368/ © 2019 Elsevier B.V. All rights reserved.

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spectrometry (ICP-OES) method by the Geological Survey of Iran. For Au, the fire assay method was employed, and the final aliquot was analyzed using the Atomic Absorption Spectrometer (AAS) method. Analytical precision was estimated using the method proposed by Thompson and Howarth (1976), and the results showed that the precision was better than 10% for most elements. Subsequently, a multielement dataset of Cu, Mo, Au, Zn, Pb, Ag, As, and Sb was used for geochemical prospecting of deposits in the study area (Table 1). This suite of indicator elements has been found to be useful for identifying porphyry Cu deposit footprints (Yilmaz, 2003b; Halter et al., 2004; Singer et al., 2005; Sotnikov et al., 2007; Weixuan et al., 2007; Yang et al., 2009; Yousefi et al., 2012; Parsa et al., 2016a; Yousefi, 2017b). In this study, the geological map of Baft district, published initially in 1972 at the scale of 1:1,00,000 and Advanced Space-Borne Thermal Emission and Reflection Radiometer (ASTER) were used, respectively, for delineation of faults and identification of hydrothermal alteration zones.

due to spatial variability related to the complex geological settings within which ore forming processes and the corresponding dispersion patterns of indicator elements occur (Carranza and Hale, 1997; Cheng, 2007; Zuo et al., 2009b; Yousefi et al., 2012). Thus, considering the influence of the spatial distribution of indicator elements with respect to geology in data processing should yield more accurate results for identifying the location of undiscovered mineral deposits (Zhao et al., 2012; De Caritat and Grunsky, 2013; Makvandi et al., 2016; Ghezelbash et al., 2019b). Accordingly, frequency space-based methods, such as fractal/multifractal models (e.g., Cheng et al., 1994; Cheng et al., 2000; Li et al., 2003; Carranza, 2008; Afzal et al., 2011), singularity mapping techniques (e.g, Cheng, 2007; Bai et al., 2010; Wang et al., 2013; Zuo, 2014; Zuo et al., 2015; Wang and Zuo, 2018; Xiao et al., 2018), spatial U-statistic (Cheng et al., 1996; Cheng, 1999; Ghavami-Riabi et al., 2010; Ghannadpour and Hezarkhani, 2015; Parsa et al., 2016c), spatial filtering (Chork and Mazzucchelli,1989), and geographically-weighted principal components analysis (Wang et al., 2015a,b), have been developed and effectively applied to identify geochemical anomalies related to mineralizing processes. In this study we aim to 1) identify and model geochemical anomalies related to the processes of porphyry copper mineralization, 2) recognize their district-scale dispersion patterns over an area in southeast Iran, and 3) measure the performance of the geochemical models in exploration targeting. Two frequency space-based techniques, namely spatial U-statistic (Cheng et al., 1996; Cheng, 1999) and concentration–area (C-A) fractal modeling (Cheng et al., 1994), were applied and quantitatively compared to identify which approach showed the highest correlation with porphyry copper mineralization processes. The better geochemical evidence layer was then combined with two district-scale geological evidence layers to produce a mineral potential model for exploration targeting in the study area.

4. Methods and results 4.1. Pre-processing Understanding the statistical distribution characteristics of geochemical data (e.g., normal or log-normal distribution) facilitates their interpretation for identifying the location of undiscovered ore deposits (Reimann et al., 2002; Zuo et al., 2009b; Zhao et al., 2016). For example, a high value of skewness for a dataset of element concentrations suggests that its distribution pattern is strongly irregular, which likely is due to the diversity of geological processes that have occurred in the area (i.e., different mineralization stages affecting the element distribution and its concentration in trap sites; cf. Reimann et al., 2002; Zuo et al., 2009b). For the dataset used in this study (Table 1), quantile–quantile (Q–Q) plots of log-transformed concentration values (Fig. 2) show departures of observed values from expected values and indicate that the concentration values for most elements do not follow log-normal distributions, but probably controlled by fractal/multifractal pattern relationships (Cheng et al., 1994; Xiao et al., 2012; Zuo et al., 2015; Zhao et al., 2016). Consequently, the study area has been affected by a variety of ore-forming processes (e.g., different dispersion patterns of the indicator elements) that likely operated at different geological times and scales (Rantitsch, 2001; Reimann et al., 2002; Zuo et al., 2009b).

2. Study area The study area, (Baft district; Fig. 1a) covering a surface of about 2500 km2, is a small part of Urumieh-Dokhtar magmatic arc in Iran. A variety of mineralization styles exist in the arc, formed during Alpine orogeny in response to episodes of subduction-related magmatism of the Urumieh-Dokhtar magmatic arc (Hezarkhani, 2006; Zarasvandi et al., 2015). The majority of hydrothermal deposits and occurrences are related to and associated with Cenozoic subvolcanic to plutonic complexes of the magmatic arc (Maghsoudi et al., 2005; Richards, 2015; Aghazadeh et al., 2015). Most of the known porphyry deposits and prospects of Iran are hosted by this belt (Shafiei et al., 2009; Ayati et al., 2013; Aghazadeh et al., 2015; Zarasvandi et al., 2015). The Kerman Cenozoic magmatic belt (Shafiei, 2010) hosts many porphyry copper deposits, such as Sarcheshmeh, Meiduk, Iju, Daraloo, Parkam, and high amount of sub-economic mineralization was developed in the southeastern segment of the Urumieh-Dokhtar magmatic arc. In north and northeast of the study area, there are widespread volcanic rocks mainly consisting of andesites, andesite-basalts, dacites, and minor amounts of basalts and rhyolites of Eocene age. These host rocks were affected by intrusive bodies through faults and structures (Fig. 1b). Presumably, these fractures could have provided the pathways for migration of mineral-bearing fluids (Sillitoe, 2010; Pirajno, 2010; Yousefi, 2017a).

4.2. Deriving a multi-element geochemical footprint The recognition of anomalous multi-element geochemical signatures is an effective practice in vectoring toward undiscovered ore deposits (e.g., Cheng et al., 1994; Wang et al., 2015a,b; Ghezelbash et al., 2019c). Principal component analysis (PCA) is an effective multivariate geochemical data analysis technique aimed at reducing the dimension and detecting hidden structures in multivariate data (e.g., Reimann et al., 2002; Cheng et al., 2006 Sun et al., 2016; Xiao et al., 2018). Simply applying multivariate analysis methods to the geochemical data such as PCA, however, does not consider the inherent compositional nature of geochemical data and the impact of outlier values. Geochemical data, reported as element concentrations that represent some proportion of a larger whole (e.g., weight percent, parts per million, etc.), are considered to be “closed” and susceptible to spurious correlations (Buccianti and Grunsky, 2014). The use of standard statistical methods on compositional data results in imprecise geochemical models (Filzmoser et al., 2018). To mitigate these problems, the data should be “opened” into real number space prior to multivariate analysis (Aitchison, 1986; Filzmoser et al., 2009b; Buccianti and Grunsky, 2014; Buccianti et al., 2015; Buccianti et al., 2018). Log-ratio transformation is one of the most commonly used

3. Sampling and dataset In stream sediment geochemical exploration, exploration scale, stream orders and shapes, and the amount of well- or poorly-developed stream networks are major factors determining sampling sites and density (e.g., Yilmaz, 2003a). Diversity in the lithology type and structural features affect the sampling scheme as well. In the present study, a total of 877 stream sediment samples were taken and analyzed for 49 elements using the inductively coupled plasma optical emission 2

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Fig. 1. (a) Simplified map of Iran, Urumieh-Dokhtar volcano-plutonic belt, (b) Geological map of Baft area.

transformations, namely additive log-ratio (alr) (Aitchison, 1986), centered log-ratio (clr) (Aitchison, 1986) and the isometric log-ratio (ilr) (Egozcue et al., 2003). The presence of outlier values is another issue affecting geochemical data analysis and interpretation (Reimann and Filzmoser, 2000; Filzmoser and Hron, 2008). Applying multivariate analysis methods that do not consider the negative influence of outliers, such as ordinary PCA, would lead to erroneous results (Filzmoser et al., 2009a,b), therefore robust-based statistical techniques should be applied (e.g., Zuo, 2014). Robust PCA (RPCA) is an extension algorithm (Filzmoser et al. 2009a) that provides a minimum covariance determinant estimator, thus modulating the negative influence of outliers (e.g., Rousseeuw and Driessen 1999). In this study, to extract a multi-element geochemical signature responding to porphyry Cu ore processes, the ilr transformation was first applied to the dataset of element concentrations (i.e. Cu, Mo, Au, Ag, As, Sb, Zn, and Pb) to modulate the closure effect (Filzmoser et al., 2009a,b). Then, RPCA was applied to the ilrtransformed data to explore the elemental associations and reduce the effect of outliers. Finally, to interpret the results obtained by RPCA, loadings and scores were transformed back to clr-space (Filzmoser

Table 1 Statistical parameters and detection limit of porphyry-Cu indicator elements.

Number of sample Detection limit Mean Median Max Min Range St.dev Kurtosis Skewness

Cu

Mo

Au

Ag

As

Sb

Pb

Zn

877

877

877

877

877

877

877

877

1

0.5

0.001

0.1

0.5

0.5

1

1

57.52 55 331 3.4 327.6 26.5 15.52 2.36

1.03 0.83 6.96 0.5 6.46 0.62 23.97 4.03

0.0034 0.001 0.251 0.001 0.25 0.011 249.15 13.39

0.13 0.06 29.4 0.1 29.3 1.04 697.7 25.45

19.36 13 743 1.2 741.8 29.62 409.62 17.31

1.7 0.81 119 0.5 118.5 4.93 377.77 17.21

46.62 12.6 10,000 3.4 9996.6 406.27 439.58 19.63

113.26 88 5640 45 5595 230.4 408.46 18.75

procedures to open geochemical data so that standard statistical analysis methods can be properly applied (Aitchison, 1986; Aitchison et al., 2000; Filzmoser et al., 2009a; Carranza, 2011; Buccianti et al., 2015; Buccianti et al., 2018). There are three types of log-ratio 3

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Fig. 2. Q-Q plot for Cu, Mo, Au, Ag, As, Sb, Zn, and Pb.

et al., 2009a,b; Zuo, 2014). This procedure was performed in R free software (Templ et al., 2011). The results indicate that, the first and second robust principal components (RPC1 and RPC2), respectively, explained 45.36% and 25.08% of the data variability (70.44% total), and thus, as concluded by Zuo et al. (2013a) and Wang et al. (2014), is sufficient to carryout analysis and interpretation. For further verification of the total variance obtained, a scree plot of eigenvalues of the principal components was used (Fig. 3a). In this plot, an inflection point appeared at principal component (number = 2), that the total variance explained by the first two components is acceptable (cf. Wang et al., 2017). For better visualization of the results, loadings and scores of RPC1 and RPC2 are presented as a compositional biplot (Fig. 3b) indicating that there is an association between the elements of Au, Cu, and Mo showing positive loadings in RPC1. The association demonstrates that, there is a close relationship between these elements

Fig. 3. (a) Scree plot of RPCA, (b) Compositional biplot of RPC1 vs. RPC2 from ilr-transformed data, (c) Spatial distribution of RPC1 scores.

during ore deposition processes in the study area. As well, it has been recognized that this suite of elements has genetic and spatial relationships with porphyry Cu deposits (Yilmaz, 2003b; Halter et al., 2004; Singer et al., 2005; Sotnikov et al., 2007; Weixuan et al., 2007; Yang et al., 2009; Yousefi et al., 2012; Parsa et al., 2016a; Yousefi, 2017b). Therefore, RPC1 can be applied as a significant multi-element signature for prospecting for porphyry copper deposits in the study area (Fig. 3c). 4

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processes (e.g., McCuaig et al., 2010; Joly et al., 2015), is an example of complex nonlinear systems (Cheng, 2007; Cheng and Agterberg, 2009; Xiang et al., 2019). From a modern nonlinear point of view, this natural phenomenon, characterized by element enrichment and/or depletion (e.g., Chen et al., 2019), cannot be adequately described without the use of nonlinear concepts (Agterberg, 2012). Fractal/multifractal models are examples of nonlinear concepts capable of modeling the complex mineralization-related features in mineral exploration practices (Turcotte, 1986; Agterberg, 1993; Cheng et al., 1994; Cheng et al., 2000; Li et al, 2003; Cheng, 2007; Afzal et al., 2010; Zuo et al., 2015; Zuo and wang 2016; Xiang et al., 2019). The C-A fractal modeling method was originally proposed by Cheng et al. (1994) and has been effectively used for identifying geochemical anomalies within complex geological settings (Cheng et al., 1994; Zuo et al., 2013a,b). The C-A model follows the power-law function, so is a scale-invariance method, which can be expressed as follows (Cheng et al., 1994):

The values of RPC1 are unbounded, thus as demonstrated in the studies by Yousefi et al., (2012, 2013) and Yousefi and Carranza (2015a), to have access to more discriminable geochemical populations, the values of RPC1 were transformed into logistic space to obtain a geochemical mineralization prospectivity index (GMPI) (Yousefi et al., 2012) of porphyry Cu deposits. 4.3. Determining the spatial dependency of element contents For window-based methods of determining spatial dependency, such as the spatial U-statistic, it is important to select the proper window size (Zhang et al., 2016). Determination of a suitable maximum search radius for the calculation of U-values is of significance as well. The search radius can be estimated using different techniques, such as nearest neighbor, corrected Akaike information criterion, or cross validation (Tian et al., 2018). Given that the spatial U-statistic is a frequency space-based method, the appropriate search radius for calculation of U-values is related to the spatial dependency of the element contents. Geostatistics, as a subset of spatial statistics (Goovaerts, 1997; Pyrcz and Deutsch, 2014), can be used to determine the spatial variation and dependency of the element contents. In this regard, the variogram (Matheron, 1963) can be used to describe the spatial dependency of data (Goovaerts, 1997; Pyrcz and Deutsch, 2014). Based on the values of a given variable Z at two locations of xi and xi + h and distances between those (h), which is called the “lag”, the variogram can be expressed as follows (Webster and Oliver, 2007):

γ (h) =

1 2N(h)

A(⩾c )∝c −a

where A ≥ c represents the area with values greater than or equal to c, α denotes the fractal dimension. Slopes of straight lines fitted on the log–log plot of A ≥ c versus c can be used to estimate fractal dimension. In this study, the C–A method was applied to the logisticallytransformed RPC1 scores to decompose the geochemical populations (Fig. 5a). Then, based on the least squares method, four straight lines were fitted on the log–log plot and five geochemical populations were obtained (Fig. 5b). The populations were classified as ‘low background’, ‘high background’, ‘low anomaly’, ‘high anomaly’, and ‘extreme

N(h)

∑

[z(xi) − z(xi+h)]2

i=1

(2)

(1)

where γ(h) and N(h) are the variogram and number of the pairs spaced within the lag, respectively. In this study, the variogram was computed for the dataset of derived RPC1. Then, an exponential model was fitted on the variogram plot (Fig. 4). Subsequently, the parameters of the variogram, including sill, range, and nugget, were extracted to obtain the maximum search radius (i.e., 5984 m). 4.4. Prioritization and classification of geochemical anomalies 4.4.1. Concentration-Area (C-A) fractal model Mineralization, as the end product of geological and geochemical

Fig. 5. (a) Log-Log plot of logistically transformed RPC1 score and the accumulated frequency values (b) Geochemical population map obtained from C-A model.

Fig. 4. Variogram of multi-element geochemical signatures. 5

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anomaly’. 4.4.2. Spatial U-statistics U-spatial statistics is a moving average technique with a variable window size (Cheng et al., 1996; Cheng, 1999). In this method, in addition to the element contents, spatial location of samples and intersample geometry are two important factors contributing to the modeling procedure (Cheng et al., 1996). Calculation of the U-values for geochemical samples is a multi-stage computational practice. For a given sampling site (e.g., ith sample in a study area), the following procedure should be implemented (Cheng et al., 1996; Cheng, 1999): a) A search window should be defined by considering two important factors that include the shape of the window and the variable search radius, so that isotropy or anisotropy variation of the geochemical variables in different directions are modeled by the factors (Cheng et al., 2000); b) Calculation of the mean ( μ ) and standard deviation (σ ) of all data in the window with r1 radius; c) Calculation of the weighted average ( X¯ i ) for the samples using the surrounding samples in the neighborhood radius of r1, considering closer samples have more weight; d) Calculation of the U-value for the sample in the neighborhood radius of r1 as shown below (Cheng, 1999):

Ui (r ) =

X¯ i − μ σ

(3)

e) Selection of a new r (0 ⩽ r ⩽ rmax ) value and repeating the procedural steps mentioned above; f) Extraction of the maximum value from the U-values calculated using the function below (Cheng, 1999):

|Ui ∗ = max |Ui (r )||,

0 ⩽ r ⩽ rmax

Fig. 7. P-A plot for (a) C-A model and (b) spatial U-statistic.

(4)

4.5. Evaluating the performance of the geochemical models g) Calculation of the maximum U-value of all samples and mapping the distribution of the values in the study area.

Prediction-area (P-A) plots (Yousefi and Carranza, 2015b) were used to evaluate the performance of the C-A and spatial U-statistic geochemical models (Fig. 7a and b). The prediction rate (Pr) and occupied area (Oa), represented by the intersection points of the plots, were then extracted to calculate the normalized density, Nd (Mihalasky and Bonham-Carter, 2001), a ratio of Pr to Oa (Yousefi and Carranza, 2015b) for each of the models. Based on the calculated parameters, the value of Nd for C-A fractal and spatial U-statistic models were determined to be 3.76 and 3.04, respectively, indicating that both of the geochemical models perform well in terms of predicting mineral deposit locations because Nd is > 1 for both of them. Comparison of the two frequency space-based anomaly identification methods, however, showed that the geochemical anomalies delineated by C-A fractal method is better than that of spatial U-statistic because of its greater Nd (i.e., 3.76 > 3.04).

In this study, U-values were calculated for the dataset of RPC1 with a search radius equal to 5984 m, as obtained by variogram analysis (Fig. 4). The U-values obtained were interpolated by the inverse distance weighting method. Then, the values equal to U, U + SD, U + 2SD, U + 3SD were used as thresholds to identify and discriminate different populations (Fig. 6).

4.6. Modeling mineral exploration targets Mineral exploration targeting involves the integration of multiple layers of geoscientific data (indicators or evidence for mineralization; Bonham-Carter and Goodfellow, 1984; Carranza, 2008; Yousefi and Carranza; 2015a,b,c; Yousefi, 2017a; Parsa et al., 2018) to identify potential exploration targets. The evidence layers include geologic and metallogenic features, or proxies for such features, that represent processes essential to ore formation (McCuaig et al., 2010; Joly et al., 2015; Wyman, et al. 2016; Almasi et al., 2017; Yousefi et al., 2019). As demonstrated above, for targeting of porphyry Cu mineralization at district scales, multi-element geochemical anomaly patterns provide valuable information for vectoring toward mineralized zones (e.g.,

Fig. 6. Geochemical population map obtained from Spatial U-statistic method. 6

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Fig. 9. (a) Geometric average prospectivity model generated by integration of quantified spatial proxies, which are the responses to the operation of oreforming processes, (b) Student's t-values calculated for measuring of spatial relationship between geometric average prospectivity value and the Cu occurrences.

This type of areally-extensive hydrothermal alteration, known as a lithocap, is affected by district-scale faults (Garwin, 2002; Sillitoe, 2010; Cooke et al., 2014). Porphyry Cu deposits are genetically and spatially controlled by faults and fractures that influence their emplacement, geometry, metal enrichment, and post-depositional weathering factors. These structural features also provide conduits for ore-forming fluids as well (Sillitoe, 2010; Pirajno, 2010), and thus, district scale mapping of hydrothermally altered rocks and structural features provides valuable information on critical ore-forming processes (Sillitoe, 2010). Using evidence layers consisting of a multi-element geochemical footprint (i.e., GMPI of porphyry Cu mineralization; Fig. 8a), fault density (Fig. 8b), and proximity to argillic alteration (Fig. 8c), a mineral potential model was generated with a logistic-based continuous weighting approach (Yousefi and Carranza; 2015a,b,c; Yousefi and Nykänen, 2016) to avoid exploration bias and uncertainties resulting from quantification procedures. After generation of the quantified evidence layers of various integration approaches (Bonham-Carter, 1994; Carranza, 2008; Yousefi and Nykänen, 2017), a geometric average function (Yousefi and Carranza, 2015c) was applied as a multi-criteria decision-making method to delineate porphyry Cu targets (Fig. 9a).

Fig. 8. Quantified evidence layer of (a) Derived multi-element geochemical footprint of porphyry copper (GMPI of porphyry-Cu), (b) Fault density, and (c) Proximity to Argillic alteration.

Cheng, 2007; Carranza, 2008; Grunsky et al., 2009). These types of ore deposits are associated with various hydrothermal alteration types (Mars and Rowan, 2006; Sinclair, 2007; Cooke et al., 2014), some of which, such as advanced argillic alteration, extend a few to tens of kilometers around mineralization trap sites (e.g., Byrne et al., 2019). 7

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(e.g., Ohta et al., 2005; Zuo et al., 2009a,b; Reimann et al., 2012; Yousefi et al, 2013; Parsa et al., 2016a; Wang and Zuo, 2018; Yousefi et al., 2019). Recognizing anomalous concentrations of pathfinder elements, modeling their dispersion patterns, and delineating geochemical footprints of ore forming processes (Cheng, 1999, 2007; Zuo and Wang, 2016), are essential for precise identification of exploration targets (Yilmaz, 2003b; Xie et al., 2008; Grunsky et al., 2009; Andersson et al., 2014; Wang et al., 2015a,b; Yousefi, 2017a, b; Parsa, et al., 2018). The application of classical (non-spatial) statistical methods to the analysis of stream sediment geochemistry does not take into consideration affects related to spatial variability associated with the complex geological settings within which ore forming processes and the corresponding dispersion patterns of indicator elements occur. By considering the spatial distribution of indicator elements in relation to geological setting, and representing geochemical anomalies as mappable criteria in context of ore-forming processes, a better and more reliable mineral potential model of exploration targets is produced (e.g., Zuo et al., 2009b). In this study, an analysis of the characteristics of stream sediment geochemistry data for the region indicated that concentration values of deposit pathfinder elements involve multiple chemical element populations and complex spatial dispersion patterns. The application and quantitative comparison of the two frequency space-based methods, spatial U-statistic and C-A fractal models, demonstrated that the C-A fractal approach yields a better model and can more effectively decompose geochemical anomalies. As a result, when integrated with other weighted evidence layers, such as fault density and proximity to argillic alteration, yielded a more precise targeting model that displayed significant spatial correlations with various geological features favourable for exploration in the study area (i.e., silicified veins, porphyry dikes, and intrusive rocks). Thus, more reliable targets were identified for follow-up exploration.

4.7. Prioritization of exploration targets The student's t-value is a parameter used for quantitatively measuring the amount of spatial correlation between point features (e.g., known mineral deposits) and polygonal features (e.g., classes of exploration targeting models) (Bonham-Carter, 1994). The student's tvalue is calculated by the weights of evidence method (Bonham-Carter, 1994), and has been widely used for the determination of statistically significant thresholds to classify continuous spatial evidence values (Sun et al., 2010; Xiao et al., 2014). In this study, for prioritization of the exploration targets derived from the mineral potential model, the student’s t-value approach were applied (Fig. 9b). There is an empirical consensus that student’s t-values > 1.96 are statistically significant and their corresponding prospectivity values (i.e., here the prospectivity values > 0.8) represents a strong spatial correlation with mineral deposits (Agterberg et al., 1990; Bonham-Carter, 1994; Bonham-Carter et al., 1989). As shown in Fig. 9b, the maximum t-value (i.e., 4.76) also indicates that a geometric average prospectivity value equal to 0.90 can be used as a threshold to identify the targets with strong spatial correlation with deposit locations. Consequently, thresholds of 0.8 and 0.9 were applied to generate a ternary-class mineral potential model for exploration targeting consisting of low, moderate, and high prospectivity (Fig. 10). The delineated targets were spatially correlated with not only the location of known Cu-occurrences but also intrusive rocks, silicified veins, and porphyry dikes in the study area (Fig. 10), which are all indicators of mineralization related to porphyry Cu deposits. Hence, the areas with high prospectivity are favourable for further prospecting of porphyry Cu type mineralization in the district. 5. Discussion and conclusion It is well known that geochemical analysis of stream sediment samples is an effective approach to identifying potential exploration targets for undiscovered mineral deposits at the district scale (e.g., Yousefi et al., 2012; Zuo et al., 2013a,b; Zuo, 2014; Zhao et al., 2016). Mineral deposit pathfinder elements in stream sediments represent both mineralizing (deposit forming) and post-mineralizing processes in which the elements are released and dispersed during exhumation, weathering, and erosion of ore bodies and adjacent mineralized rock

Acknowledgments Authors wish to thank Geological Survey of Iran for supplying necessary data to do this research work. We would like to sincerely thank Dr. Oliver Kreuzer and Dr. Vesa Nykänen for their expert handling of this contribution. In addition, we would express our gratitude to two

Fig. 10. Map of classified exploration targets. 8

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anonymous reviewers whose comments helped us to improve this paper.

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