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Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling
GEXPLO-05146; No of Pages 9 Journal of Geochemical Exploration xxx (2013) xxx–xxx
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Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling Mahyar Yousefi a,⁎, Emmanuel John M. Carranza b, 1, Abolghasem Kamkar-Rouhani a a b
School of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, The Netherlands
a r t i c l e
i n f o
Article history: Received 1 August 2012 Accepted 30 January 2013 Available online xxxx Keywords: Weighting Drainage catchment basin Stream sediment anomaly mapping Mineral potential modeling
a b s t r a c t In stream sediment geochemical exploration, anomalies can be recognized by ‘undiluting’ concentrations of indicator elements in every stream sediment sample catchment basin (SCB) as a function of topographic, geomorphologic, and geologic factors. However, this SCB modeling, like contour mapping, of stream sediment geochemical anomalies depends on sampling locations and sampling density. These arbitrary aspects of stream sediment sampling can render SCB or contour mapping of stream sediment anomalies inefficient. In this paper, instead of evaluating the relative exploration importance of each sample per SCB, we evaluated the relative exploration importance of samples per natural drainage catchment basin (DCB). Accordingly, we developed a new fuzzy weighting scheme for each DCB based on the distribution of anomalous and background samples in each DCB. In this new approach of weighted drainage catchment basin (WDCB) mapping of stream sediment geochemical anomalies, individual DCBs are given fuzzy weights representing their relative importance for prospecting the deposit-type sought. Hence, a map of WDCB can be used directly as a geochemical evidence layer in fuzzy-based mineral prospectivity mapping. In this regard, we demonstrated that the prediction rate of prospectivity map obtained by using WDCB approach with respect to known mineral deposit occurrences is higher than that of prospectivity map obtained by using a SCB or contour map. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Geochemical exploration based on stream sediment data is, traditionally, an efficient method for identifying anomalous areas especially in the preliminary stages of prospecting for undiscovered outcropping or concealed mineral deposits. Analysis of significant anomalies in geochemical landscapes based on stream sediment geochemical data is important for creating and integrating layers of geochemical evidence in mineral potential mapping (MPM) for the deposit-type sought (Carranza, 2010b; Yousefi et al., 2012). In this regard, various researchers continue to address outstanding problems and/or develop better methods regarding the distinction between background and anomalies in geochemical landscapes (Bonham-Carter, 1994; Carranza, 2004a, 2008, 2010a,b, 2011; Carranza and Hale, 1997; Moon, 1999; Ohta et al., 2005; Spadoni, 2006; Spadoni et al., 2004; Yousefi et al., 2012). Geochemical landscapes have been modeled from point data of stream sediment chemical compositions by creating maps with point symbols, contours (Govett, 1983), sample catchment basins (or SCBs) (Bonham-
⁎ Corresponding author at: Shahrood University of Technology, P.O. Box 316, Shahrood, Iran. Tel.: +98 911 3385443; fax: +98 273 3395509. E-mail addresses: M.Yousefi[email protected] (M. Yousefi), [email protected] (E.J.M. Carranza). 1 Present address: School of Earth and Environmental Sciences, James Cook University, Queensland, Australia.
Carter, 1994; Bonham-Carter and Goodfellow, 1984, 1986; Carranza, 2010b; Carranza and Hale, 1997; Moon, 1999; Spadoni et al., 2004), stream orders (Carranza, 2004a), and extended sample catchment basins (Spadoni, 2006). Details of processes controlling the downstream variations of stream sediment compositions are poorly understood or unknown because of complex erosion processes, influence of pollutants, and influence of compositions and distributions of regolith and bedrock (Bogen et al., 1992; Macklin et al., 1994; Spadoni, 2006). Some of the most important known generic factors affecting downstream variations in stream sediment composition and downslope variations in intensity of erosion processes are climatic, topographic, geomorphologic, geologic and anthropogenic factors (Spadoni, 2006). In existing methods for analysis of stream sediment geochemical data for mineral exploration, only or mainly surface geochemical dispersion has been considered to relate quantitatively catchment-based topographic, geomorphologic and geologic factors with downstream variations in stream sediment compositions in order to identify anomalies related to mineral deposits (Hawkes, 1976; Moon, 1999; Spadoni, 2006). There is a lack of studies using mathematical functions that consider adequately surface and subsurface geochemical dispersions in downstream variations in stream sediment chemical compositions. Theoretically, concentrations of indicator elements in stream sediments decrease with increasing distance downstream from an anomaly source (e.g., mineral occurrences). However, in many cases, the
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Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
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gradient of downstream decay of stream sediment chemical compositions is not linear because of non-linear downstream variations of factors that could influence fluvial dispersion of sediments (e.g., Xie et al., 2010; Yilmaz, 2003). Therefore, complex anomaly patterns exist such that (a) weak anomalies are concealed within the strong variance of background, (b) background values exist in anomalous areas, (c) concealed or sub-outcropping deposits are not associated with geochemical anomalies, and (d) stream sediments immediately downstream of mineral deposits exhibit non-anomalous element contents (Cheng, 2007). Nevertheless, various studies have considered interpolation of geochemical variables (contour mapping) or the SCB (i.e., the area of influence of every stream sediment sample) in developing models of surface geochemical dispersion (Hawkes, 1976; Moon, 1999; Spadoni, 2006) and geochemical background (Bonham-Carter and Goodfellow, 1984, 1986; Bonham-Carter et al., 1987, 1988; Moon, 1999) in order to map anomalous areas (Bonham-Carter, 1994; Carranza, 2004a, 2008, 2010a,b; Carranza and Hale, 1997; Ódor et al., 1997; Rantitsch, 2004; Spadoni et al., 2004). In this regard, although anomalous patterns caused by mineralization processes can be highly complex as shown by their spatial and frequency properties (Zuo et al., 2009), anomalies can be recognized by ‘undiluting’ concentrations of indicator elements in stream sediment samples far downstream from an anomaly source as a function of topographic, geomorphologic, and geologic factors (Carranza, 2004a; Hawkes, 1976; Moon, 1999). However, according to Hawkes (1976) and Solovov (1987), analysis of geochemical background in stream sediments by using SCBs is applicable mainly to low order streams, where clastic dispersion predominates, but not to high order streams, where hydromorphic dispersion, particularly groundwater processes, predominates. Furthermore, both SCB modeling and any contour mapping method are dependent on sampling sites and density. In SCB modeling, the area of influence of each stream sediment sample is just its upstream area until the next sample upstream. In this regard, as the number of stream sediment samples becomes large and as drainage patterns become intricate, calculation of areas upstream of individual stream sediment samples for SCB modeling becomes unwieldy (Carranza, 2004a). Hence, SCB modeling or contour mapping may lead to rough estimates of geochemical background and, thus, errors for the detection of anomaly sources. In addition, the detection of diluted sources of anomalies is a major problem where drainage basins of extremely different sizes are sampled and where multiple samples are taken within a drainage basin (Moon, 1999). Considering the foregoing problems of either SCB modeling or contour mapping and, in view of complex relationships between element contents in stream sediments
and their plausible sources, the purpose of this paper is to develop an improved approach to facilitate analysis of complex anomaly patterns in order to vector into mineralized zones and, therefore, support MPM. Here, instead of using sampled stream segments to define SCBs, we used main rivers to outline their catchment basins. Catchment basins of main rivers (hereafter referred to as drainage catchment basins or DCBs) rather than SCBs have been used for modeling and studying contamination, erosion, hydrogeochemical dispersion, groundwater migration and sources of sediment (e.g., Biondić et al., 2006; Matějíček et al., 2003; McKergow et al., 2005; Prtoljan et al., 2012; Sidorchuk et al., 2003). Every main river corresponds to a DCB. The outlet of each main river, which is its junction with another main river, is the starting point for outlining its DCB. Hence, the whole area upstream of a main river outlet is its DCB. For performing this process, as Martz and Garbrecht (1993) and Carranza (2008) noted a digital elevation model (DEM) of the study area was used to delineate drainage networks and their corresponding outlets and catchments automatically. In this regard, GIS-based geochemical mapping can be carried out (Carranza, 2008). Furthermore, there is also an algorithm to delineate catchment basin of a set of points (sampling points or here, outlet points of main rivers) by using DEM (Jones, 2002). Hence, unlike in SCB modeling where the outlet of each stream segment is a stream sediment sample location, the DCB approach described here is not dependent on sample locations and sampling density. For attributing measured or derived geochemical variables to individual DCBs to evaluate their relative exploration importance, we developed a new weighting scheme for each DCB – a fuzzy DCB score (FCx) – based on the distribution of anomalous and background samples in each DCB. The improved technique described here is called weighted drainage catchment basin (WDCB), which is a kind of discrete field modeling of geochemical landscapes (Carranza, 2010b) and an efficient way to generate a weighted stream sediment geochemical evidential map for integrating among other geo-exploration data, to map mineral prospectivity. In this study, we used the GMPI (geochemical mineralization probability index — a derived multivariate geochemical signature) data set of Yousefi et al. (2012) to test and evaluate the proposed WDCB approach in mapping porphyry-Cu prospectivity in the Kerman province, southeast of Iran. 2. Methods and results In the WDCB approach, drainage networks are divided into DCBs, which represent the skeleton and infrastructure of a topographic and
Fig. 1. Classification and distribution of strongly anomalous, moderately anomalous and background GMPI values of stream sediment samples in each DCB.
Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
M. Yousefi et al. / Journal of Geochemical Exploration xxx (2013) xxx–xxx
geomorphologic model of a stream system (Matějíček et al., 2003) for investigating its related features (Sidorchuk et al., 2003), e.g., geochemical dispersion. Stream sediments contain information about geochemical dispersion in DCBs (Xie et al., 2010) because element compositions of stream sediments are derived from the weathering, erosion (Carranza, 2010b; Howarth and Thornton, 1983; Ohta et al., 2005) and dispersion of materials within a DCB. Spadoni (2006) has demonstrated that the area of spatial influence of a stream sediment sample extends downhill and uphill of a sample location and has argued that it is reasonable to suppose that geochemical values immediately downstream of each sample location do not differ too much from the values at a sample location itself. Hence, the area occupied by each DCB is morphologically the area of influence of all stream sediment samples within each DCB. Hawkes (1976) and Moon (1999) have considered the whole area of a DCB to propose idealized formulas for evaluating background and anomalous stream sediment samples. 2.1. Creation of WDCB map To create a WDCB map, we attributed a weight to the whole area of each DCB based on the notion that stream sediments associated with mineralized DCBs have higher concentrations of elements compared to stream sediments associated with non-mineralized DCBs. To assign a weight to each DCB, element contents or derived geochemical variables of all stream sediment samples within a DCB were used for defining its importance for prospecting mineral deposits. Hence, in mineralized DCBs the mean and/or median of element contents, with respect to all samples (both anomaly and background samples), is generally higher than in non-mineralized DCBs. To demonstrate this statement, we delineated DCBs in the study area (n= 55) (Fig. 1) and we used the dataset of GMPI values derived by Yousefi et al. (2012) for every stream sediment sample in the study area. The GMPI (Yousefi et al., 2012) represents a multivariate geochemical signature of the deposit-type sought that can be derived for a stream sediment geochemical data set after transforming the original data values to address the closure problem of composition data by using isometric logratio transformation (Buccianti, 2013; Carranza, 2011; Egozcue et al., 2003). Subsequently, we used a graphical tool of exploratory data analysis (EDA), Fig. 14 of Yousefi et al. (2012), and selected two threshold GMPI values (0.8 and 0.86) to define three classes of GMPI values (Fig. 1) — strong anomalies, moderate anomalies and background. Then, the median of GMPI values (MT) of all stream sediment samples in each DCB was calculated (Table 1) and plotted (Fig. 2). According to Fig. 2, the values of MT for DCBs with known Cu occurrences (e.g., DCBs numbered 6, 17, 12, 5, 3, 15, 25, 51, 37, 27, 40, 23, 19, 10 and 20) are generally higher than other DCBs. In Fig. 2, there are DCBs with no known Cu occurrences (e.g., those numbered 1, 8, 13, 28, 29, 21, 26, 46, 48, 50, and 54) for which the values of MT are comparable to those of DCBs with known Cu occurrences. Hence, the former DCBs can be considered as promising areas. Furthermore, we calculated and plotted the median of background GMPI values (Mb) for each individual DCB (Table 1, Fig. 3). For this analysis, we used for each DCB only background samples (i.e., those with GMPI values of b 0.8). The distributions of MT and Mb are similar (Figs. 2, 3), although the level of geochemical background in mineralized DCBs is higher than that in non-mineralized DCBs. The similarity of Figs. 2 and 3 illustrates that using DCBs instead of SCBs does not undermine the recognition of mineralized areas despite simplification of geochemical patterns due to aggregation of samples in DCBs. Therefore, further weighting of DCBs as described below is justified and could support MPM. The generation of a map of WDCB, which can be used in MPM, is discussed here with demonstrations of its applications to map geochemical anomalies for potential of porphyry-Cu deposits in the case study area. Because stream sediment samples in every DCB have different element contents, the weight assigned to each DCB is based on classes of
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Table 1 Calculated parameters of each DCBs: NT = number of samples; MT = median of GMPI values; ns = number of samples with strongly anomalous GMPI values; Ms = median of strongly anomalous GMPI values; nm = number of samples with moderately anomalous GMPI values; Mm = median of moderately anomalous GMPI values; nb = number of background GMPI values; Mb = median of background GMPI values; Cx = DCB score; and FCx = fuzzy DCB score. DCB ID
NT
MT
Ns
Ms
nm
Mm
nb
Mb
Cx
FCx
1 2 3 4a 5 6 7 8 9 10 11 12 13 14 15 16a 17 18a 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55a
39 22 33 0 41 106 36 17 89 63 30 139 31 44 36 0 48 0 35 56 36 1 33 37 17 43 14 22 24 3 108 1 68 19 50 5 48 19 56 35 18 17 4 28 5 26 1 8 69 8 18 1 85 10 0
0.63 0.46 0.65 0.54 0.61 0.66 0.39 0.50 0.45 0.49 0.38 0.69 0.62 0.48 0.55 0.42 0.61 0.40 0.42 0.57 0.54 0.47 0.56 0.41 0.54 0.48 0.52 0.50 0.55 0.49 0.43 0.83 0.44 0.40 0.46 0.46 0.53 0.42 0.44 0.53 0.37 0.37 0.40 0.37 0.42 0.64 0.49 0.72 0.38 0.72 0.66 0.89 0.43 0.49 0.66
2 0 1 0 4 22 0 0 0 2 0 42 1 1 1 0 6 0 1 9 1 0 2 0 2 5 1 1 0 0 2 0 0 0 2 0 6 0 4 5 0 0 0 0 0 7 0 3 1 3 7 1 1 0 0
0.92 0.00 0.93 0.00 0.88 0.88 0.00 0.00 0.00 0.94 0.00 0.90 0.87 0.89 0.87 0.00 0.90 0.00 0.88 0.98 0.87 0.00 0.89 0.00 0.94 0.87 0.93 0.86 0.00 0.00 0.93 0.00 0.00 0.00 0.89 0.00 0.92 0.00 0.89 0.95 0.00 0.00 0.00 0.00 0.00 0.96 0.00 0.95 0.86 0.97 0.88 0.89 0.95 0.00 0.00
4 0 3 0 5 9 0 0 2 1 0 9 3 3 4 0 5 0 0 0 1 0 4 1 1 4 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
0.85 0.00 0.84 0.00 0.83 0.84 0.00 0.00 0.84 0.83 0.00 0.84 0.84 0.83 0.82 0.00 0.86 0.00 0.00 0.00 0.85 0.00 0.82 0.84 0.82 0.84 0.00 0.84 0.00 0.00 0.00 0.83 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.80 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
33 22 29 0 32 75 36 17 87 60 30 88 27 40 31 0 37 0 34 47 34 1 27 36 14 34 13 19 24 3 106 0 68 19 48 5 42 19 52 30 18 17 4 28 5 18 1 5 68 5 11 0 84 10 0
0.58 0.46 0.62 0.52 0.54 0.58 0.39 0.50 0.44 0.47 0.38 0.58 0.59 0.45 0.51 0.41 0.53 0.40 0.41 0.49 0.52 0.47 0.48 0.39 0.47 0.38 0.49 0.45 0.55 0.49 0.42 0.00 0.44 0.40 0.44 0.46 0.47 0.42 0.40 0.46 0.37 0.37 0.40 0.37 0.42 0.51 0.49 0.58 0.37 0.58 0.51 0.00 0.42 0.49 0.21
80.73 45.62 78.54 63.17 87.82 109.71 39.28 50.33 46.46 56.78 38.27 129.48 75.72 57.92 69.16 42.87 92.14 40.57 47.35 88.60 61.08 47.24 75.37 42.90 81.39 76.34 65.07 65.41 54.62 49.43 46.37 166.28 43.98 39.56 52.66 46.19 76.09 41.65 56.31 79.66 37.29 36.89 40.17 37.33 42.00 119.41 48.87 142.48 40.63 145.08 133.99 265.54 44.95 48.80 155.25
0.91 0.51 0.90 0.76 0.94 0.98 0.41 0.59 0.52 0.68 0.39 0.99 0.88 0.70 0.83 0.47 0.95 0.43 0.54 0.94 0.74 0.54 0.88 0.47 0.91 0.88 0.79 0.79 0.65 0.57 0.52 0.99 0.48 0.41 0.62 0.52 0.88 0.45 0.67 0.90 0.38 0.37 0.42 0.38 0.45 0.99 0.56 0.99 0.43 0.99 0.99 0.99 0.50 0.56 0.99
a The parameters MT, Mb and Cx of DCBs numbered 4, 16, 18 and 55, which occupy very small areas and have not been sampled, were calculated based on average values in two upstream DCBs.
samples. To calculate a score (Cx) for each DCB the following generic equation is used: n n C x ¼ L M L L 100 þ ðL−1Þ M L−1 L−1 100 NT NT nL−2 100 þ… þ ðL−2Þ ML−2 NT nL−Lþ1 100 þ M L−Lþ1 NT
ð1Þ
Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
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M. Yousefi et al. / Journal of Geochemical Exploration xxx (2013) xxx–xxx
Fig. 2. Distribution of median GMPI values (MT) of stream sediment samples per DCB.
where L is the number of geochemical classes according to a geochemical data set in a study area, ML is the median of element contents (or the median of derived values of multivariate signature) of stream sediment samples in the most anomalous class (L), nL is the number of stream sediment samples in the most anomalous class per DCB, NT is the total number of stream sediment samples per DCB, L − 1 and L − 2 represent the succeeding geochemical classes with decreasing level of anomaly, and L − L + 1 represents the most background geochemical class. Thus, Cx is related to the levels of element contents in all stream sediment samples in every DCB. Hence, a map of Cx is similar to an interpolated map although it is a discrete field model because values of Cx are attributed to DCBs. In Eq. (1), the number of stream sediment samples per geochemical class influences the relative importance of a DCB for mineral exploration. For example, if a DCB has a higher number of anomalous stream sediment samples compared to another DCB, the Cx of the former is generally higher than that of the latter. For the present study, based on Fig. 1, we used the GMPI data set of Yousefi et al. (2012), consisting of values of derived multivariate geochemical signature of porphyry-Cu deposit that are classified into three classes for mapping mineral prospectivity — strongly anomalous (s),
moderately anomalous (m) and background (b). To calculate Cx per DCB the following equation is used: n n n C x ¼ 3 Ms s 100 þ 2 Mm m 100 þ Mb b 100 : NT NT NT
ð2Þ
The calculated values of Cx per DCB (Table 1) according to Eq. (2) do not lie within the [0,1] range. Thus, values of Cx are non-probabilistic and are not appropriate to represent the probability of presence or absence of deposit-type sought in each DCB. To assign probabilistic values to every DCB (e.g., fuzzy weights) for application in MPM, we transformed the calculated values of Cx by applying the following logistic function: FC x ¼
1 1 þ e−sðC x −iÞ
ð3Þ
where FCx is a fuzzy score, and i and s are inflection point and slope, respectively, of the logistic function. The parameters i and s determine the shape of the logistic function and, hence, the output values. These parameters are chosen based on a subjective assessment. For the present study, the values 0.065 and 45 were used for s and i, respectively, in
Fig. 3. Distribution of median background GMPI values (Mb) of stream sediment samples per DCB.
Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
M. Yousefi et al. / Journal of Geochemical Exploration xxx (2013) xxx–xxx
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Fig. 4. Distribution of FCx values of DCBs.
Eq. (3) to obtain FCx per DCB (Table 1). The distribution of FCx is shown in Fig. 4. According to Fig. 4, DCBs with known mineral occurrences have high values of FCx but some DCBs (e.g., those numbered 46, 26, 13, 1, 28, 50 and 48) without known mineral occurrences also have high values of FCx. These latter DCBs are therefore potentially valuable for further exploration. This result is based only on the comparison of FCx values in DCBs, because like SCB modeling or contour mapping, areas with higher values of indicator geochemical variables have higher priority for further exploration. Further verification (acceptance or rejection) of potentially valuable areas in a geochemical map is carried out using other geo-exploration evidential layers especially in MPM (Carranza, 2008), here by using a map of proximity to intrusive contacts. Hence, the map in Fig. 4 is the WDCB map, which can be integrated with other geo-evidence layers for MPM. 2.2. Performance of WDCB approach in MPM The study area is a small part of the Urumieh-Dokhtar Volcanic belt of Iran (Yousefi et al., 2012). This belt has a great potential for
porphyry-Cu deposits as far as its geology suggests (e.g., Atapour and Aftabi, 2007; Boomeri et al., 2009; Hezarkhani, 2006a,b). Several published works have discussed and verified the genetic and spatial relationships of intrusive rocks with porphyry-Cu deposits in the Urumieh-Dokhtar volcanic belt (e.g., Atapour and Aftabi, 2007; Boomeri et al., 2009; Hezarkhani, 2006a,b, 2009). The porphyry-Cu deposits in this belt are related to intrusive porphyries and igneous plutons (e.g., Hezarkhani, 2009; Lundmark et al., 2005; Peytcheva et al., 2009) because the margins of intrusions are strongly fractured and, thus, enabled hydrothermal fluids to exchange heat and mass with the intruded rocks (Guillou-Frottier and Burov, 2003). Hence, areas close to contacts of intrusive bodies have stronger likelihood of porphyry-Cu mineralization than areas farther to the contacts (Campos et al., 2002; Peytcheva et al., 2009; Xiaoming et al., 2007). To demonstrate the superior ability of the WDCB mapping approach over SCB modeling or contour mapping for facilitating analysis of complex anomaly patterns in order to vector into mineralized zones to support MPM, we used buffers around intrusive bodies in the study area as indicators of the mineral deposit-type sought. In this study, porphyry-Cu deposits represent the mineral deposit-type sought. For
Fig. 5. Map of fuzzy scores of proximity to intrusive contacts (white curvilinear features).
Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
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Fig. 6. Map of interpolated GMPI values, derived using inverse distance interpolation method, representing multi-element signature of porphyry-Cu mineralization (Yousefi et al., 2012). Because the stream sediment sampling density in the study area is high, the spatial distributions of GMPI values portrayed by SCB modeling (not shown here) and by interpolation (as shown here) are approximately the same.
MPM of this deposit-type, a map of distance from contacts of intrusive bodies with other lithologies was generated first. Then, because proximity to intrusive contacts represents favorability for porphyry-Cu mineralization (Carranza, 2002, 2004b; Carranza and Hale, 2002a,b; Carranza et al., 2008; Xiaoming et al., 2007), we used the inverse of distance to outlines of intrusive bodies to represent favorability for porphyry-Cu occurrence. After that, to assign fuzzy proximity weights to distances from intrusive contacts, we used a logistic function like Eq. (3) to transform the values in the map of inverse of distances into the [0, 1] range (Fig. 5). Because the map in Fig. 5 is a weighted fuzzy evidence layer, it can be integrated with a weighted fuzzy geochemical evidential map. In addition, because a map of GMPI (Yousefi et al., 2012) or a WDCB map (Fig. 4) is a weighted fuzzy geochemical evidential layer, either of them can be integrated with the map of fuzzy proximity scores of intrusive contacts (Fig. 5) for MPM through fuzzy logic modeling (Carranza and Hale, 2001; Porwal et al., 2003, 2004). To demonstrate that the WDCB approach is superior over contour mapping or SCB modeling, we used the fuzzy “AND” operator to integrate the map of fuzzy scores of proximity to intrusive contacts (Fig. 5) with two kinds of weighted geochemical evidential maps, an interpolated map of
GMPI values (Fig. 6) and the map of WDCB (Fig. 4). Thus, we generate two fuzzy prospectivity maps (Figs. 7, 8) for comparing and evaluating the WDCB method with a contour mapping method. We used DCBs numbered 12, 15, 37, and 40 with known mineral occurrences for comparing and evaluating the results. In this regard, in DCB number 12 there are anomalous samples immediately downstream of known mineral occurrences. Hence, the resulting prospectivity map (Fig. 7) shows high prospectivity values around the mineral occurrences in DCB number 12. However, as shown in Fig. 1, there are no anomalies immediately downstream of the known mineral occurrences in DCBs numbered 15, 37, and 40. In these DCBs, anomalous samples are located far downstream of the known mineral occurrences. Fig. 6 shows that areas immediately around the known mineral occurrences in DCBs numbered 15, 37, and 40 are not anomalous. Thus, if the map in Fig. 6 is integrated with the map of fuzzy scores of proximity to intrusive contacts (Fig. 5), the resulting prospectivity map (Fig. 7) does not show high prospectivity values around the mineral occurrences in DCBs numbered 15, 37 and 40. Hence, some important areas are missed for further prospecting. For example, the eight mineral occurrences in DCBs numbered 15, 37, and 40 are missed if the contour mapping (or SCB modeling) is used to map geochemical anomalies. In contrast, if the map of
Fig. 7. Map of fuzzy scores of mineral prospectivity generated by combining map of fuzzy scores of proximity to intrusive contacts (Fig. 5) and map of interpolated GMPI values (Fig. 6) using the fuzzy “AND” operator. White curvilinear features represent intrusive contacts.
Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
M. Yousefi et al. / Journal of Geochemical Exploration xxx (2013) xxx–xxx
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Fig. 8. Map of fuzzy scores of mineral prospectivity generated by combining map of fuzzy scores of proximity to intrusive contacts (Fig. 5) and map of FCx (WDCB; Fig. 4) using the fuzzy “AND” operator. White curvilinear features represent intrusive contacts.
WDCB (Fig. 4) is integrated with the map of fuzzy scores of proximity to intrusive contacts (Fig. 5), the resulting prospectivity map (Fig. 8) shows higher prospectivity values at and around the known mineral occurrences in DCBs numbered 15, 37, and 40 as well as in DCB number 12. The foregoing comparison of Figs. 7 and 8 demonstrates that, in situations with complex anomaly patterns (as described in the Introduction), a map of WDCB is more effective than a contour or interpolated map (or a SCB model) for mapping of mineral prospectivity. For further illustration of this statement, the values of mineral prospectivity at the locations of all 32 known mineral occurrences in the two resulting fuzzy prospectivity maps, Figs. 7 and 8, are shown in Fig. 9 for comparison. This figure illustrates that most of the mineral occurrences in the study area are associated with high prospectivity values if the WDCB map instead of the interpolated GMPI map is used for mineral prospectivity. Furthermore, the percentages of known mineral occurrences delineated depending on threshold fuzzy prospectivity scores (Fig. 10) show that the prediction rate of the prospectivity map generated by using the WDCB map (Fig. 8) is higher than that of the prospectivity map generated by using the interpolated GMPI map (Fig. 7).
3. Discussion Stream sediment geochemical anomalies are usually spatially noncoincident with other indicative geological features (e.g., igneous intrusions) because the former represent transported materials whereas the latter are in situ. Accordingly, integration of geochemical evidential maps derived from stream sediment geochemical data with other geo-exploration evidence layers may result in prospectivity maps with poor prediction rate. Hence, in explored regions with complex stream sediment anomaly patterns (e.g., DCBs numbered 15, 37, and 40 in Fig. 6) some mineralized areas are likely to be missed (e.g. Carranza, 2011; Pazand et al., 2011; Ranasinghe et al., 2008; Yousefi et al., 2012; Zuo, 2011; Zuo et al., 2009). A reason for this, in addition to the complex stream sediment anomaly patterns, is lack of efficacy of geochemical anomaly mapping methods to address the transported nature of stream sediment geochemical anomalies. That means, because of differences in physical and chemical characteristics (e.g., mobility) of geochemical elements, stream sediment samples with anomalous concentrations of different indicator elements can be located differently downstream from an anomaly source (Xie et al., 2010; Yilmaz, 2003; Yousefi et al.,
Fig. 9. Comparison of fuzzy scores of mineral prospectivity, obtained by using the WDCB map (Fig. 4) and the interpolated GMPI map (Fig. 6), at known mineral occurrences.
Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
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Fig. 10. Percentages of known mineral occurrences delineated depending on threshold values of fuzzy scores of mineral prospectivity obtained by using the WDCB map (Fig. 4) and the interpolated GMPI map (Fig. 6).
anomalies in preliminary exploration stages with relatively low sampling density.
2012). Therefore, consideration of DCBs but not SCBs and attribution of FCx to DCBs comprise a useful approach to address complex stream sediment anomaly patterns. This is because the WDCB approach considers the whole area of each DCB, as an infrastructure of a topographic and geomorphologic model (Matějíček et al., 2003), but it does not undermine the recognition of mineralized areas. A map of WDCB can be generated for uni-element concentration data (e.g., Carranza, 2004a, 2011) or for derived multi-element associations (e.g., Chandrajith et al., 2001; Grunsky et al., 2009; Halfpenny and Mazzucchelli, 1999; Yousefi et al., 2012). In the latter situation, factor scores (e.g., Helvoort et al., 2005; Kumru and Bakac, 2003; Reimann et al., 2002; Sun et al., 2009) or values of GMPI (Yousefi et al., 2012) can be used as input values to calculate FCx.
However, because sampling density affects SCB modeling, this paper has demonstrated in an area with certain sampling sites and density that the WDCB approach is better than contour and SCB mapping methods to map geochemical anomalies. This is because the former delineates complex anomaly patterns independent of sampling sites while the two latter methods are dependent on sampling sites and density, and, consequently, are inefficient for delineating complex anomaly patterns. This statement is illustrated especially in mineral potential mapping where WDCB map is integrated with other geoexploration evidential layers.
4. Concluding remarks
Acknowledgments
The weighted drainage catchment basin (WDCB) modeling of stream sediment geochemical landscapes is an approach whereby the relative importance of all stream sediment samples and geochemical anomaly classes (here strongly anomalous, moderately anomalous and background) in every drainage catchment basin (DCB) are considered in assigning a weight to each of them for prospecting of mineral deposits. The following advantages of the WDCB modeling approach to geochemical anomaly mapping compared to the sample catchment basin (SCB) modeling approach and a contour mapping method have been demonstrated in this paper:
The authors thank Madan Pars Asia (MPA) consulting engineering company, especially Dr. Krimi, and Mr. Sahebzamani for supplying the necessary data to do this research work. The authors express special thanks to Mr. Esfehanipoor, head of exploration department of National Iranian Copper Industries Company (NICICO), for some support. We thank Dr. Antonella Buccianti for her suggestions for improving this paper. We also appreciate the comments of the anonymous reviewers.
1. Anomalies mapped in WDCB discrete field models of stream sediment geochemical landscapes exhibit stronger positive spatial associations with other indicative geological features (here intrusive contacts) of the mineral deposit-type sought. 2. In the WDCB approach, DCBs are given fuzzy weights representing their relative importance for prospecting the deposit-type sought. Hence, a map of WDCB can directly be used in fuzzy logic MPM as a weighted geochemical evidence layer. 3. The prediction rate of prospectivity map obtained based on WDCB approach with respect to known mineral deposit occurrences is higher than that in the resulting prospectivity map generated based on SCB model or contour map. 4. Using the WDCB approach avoids the disadvantage of mapping anomalies through SCB modeling or contouring in terms of strong dependence on sample locations and sampling density. Consequently, the WDCB approach is much more useful than SCB modeling or contouring for analysis and mapping stream sediment geochemical
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Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
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Journal of Geochemical Exploration journal homepage: www.elsevier.com/locate/jgeoexp
Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling Mahyar Yousefi a,⁎, Emmanuel John M. Carranza b, 1, Abolghasem Kamkar-Rouhani a a b
School of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, The Netherlands
a r t i c l e
i n f o
Article history: Received 1 August 2012 Accepted 30 January 2013 Available online xxxx Keywords: Weighting Drainage catchment basin Stream sediment anomaly mapping Mineral potential modeling
a b s t r a c t In stream sediment geochemical exploration, anomalies can be recognized by ‘undiluting’ concentrations of indicator elements in every stream sediment sample catchment basin (SCB) as a function of topographic, geomorphologic, and geologic factors. However, this SCB modeling, like contour mapping, of stream sediment geochemical anomalies depends on sampling locations and sampling density. These arbitrary aspects of stream sediment sampling can render SCB or contour mapping of stream sediment anomalies inefficient. In this paper, instead of evaluating the relative exploration importance of each sample per SCB, we evaluated the relative exploration importance of samples per natural drainage catchment basin (DCB). Accordingly, we developed a new fuzzy weighting scheme for each DCB based on the distribution of anomalous and background samples in each DCB. In this new approach of weighted drainage catchment basin (WDCB) mapping of stream sediment geochemical anomalies, individual DCBs are given fuzzy weights representing their relative importance for prospecting the deposit-type sought. Hence, a map of WDCB can be used directly as a geochemical evidence layer in fuzzy-based mineral prospectivity mapping. In this regard, we demonstrated that the prediction rate of prospectivity map obtained by using WDCB approach with respect to known mineral deposit occurrences is higher than that of prospectivity map obtained by using a SCB or contour map. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Geochemical exploration based on stream sediment data is, traditionally, an efficient method for identifying anomalous areas especially in the preliminary stages of prospecting for undiscovered outcropping or concealed mineral deposits. Analysis of significant anomalies in geochemical landscapes based on stream sediment geochemical data is important for creating and integrating layers of geochemical evidence in mineral potential mapping (MPM) for the deposit-type sought (Carranza, 2010b; Yousefi et al., 2012). In this regard, various researchers continue to address outstanding problems and/or develop better methods regarding the distinction between background and anomalies in geochemical landscapes (Bonham-Carter, 1994; Carranza, 2004a, 2008, 2010a,b, 2011; Carranza and Hale, 1997; Moon, 1999; Ohta et al., 2005; Spadoni, 2006; Spadoni et al., 2004; Yousefi et al., 2012). Geochemical landscapes have been modeled from point data of stream sediment chemical compositions by creating maps with point symbols, contours (Govett, 1983), sample catchment basins (or SCBs) (Bonham-
⁎ Corresponding author at: Shahrood University of Technology, P.O. Box 316, Shahrood, Iran. Tel.: +98 911 3385443; fax: +98 273 3395509. E-mail addresses: M.Yousefi[email protected] (M. Yousefi), [email protected] (E.J.M. Carranza). 1 Present address: School of Earth and Environmental Sciences, James Cook University, Queensland, Australia.
Carter, 1994; Bonham-Carter and Goodfellow, 1984, 1986; Carranza, 2010b; Carranza and Hale, 1997; Moon, 1999; Spadoni et al., 2004), stream orders (Carranza, 2004a), and extended sample catchment basins (Spadoni, 2006). Details of processes controlling the downstream variations of stream sediment compositions are poorly understood or unknown because of complex erosion processes, influence of pollutants, and influence of compositions and distributions of regolith and bedrock (Bogen et al., 1992; Macklin et al., 1994; Spadoni, 2006). Some of the most important known generic factors affecting downstream variations in stream sediment composition and downslope variations in intensity of erosion processes are climatic, topographic, geomorphologic, geologic and anthropogenic factors (Spadoni, 2006). In existing methods for analysis of stream sediment geochemical data for mineral exploration, only or mainly surface geochemical dispersion has been considered to relate quantitatively catchment-based topographic, geomorphologic and geologic factors with downstream variations in stream sediment compositions in order to identify anomalies related to mineral deposits (Hawkes, 1976; Moon, 1999; Spadoni, 2006). There is a lack of studies using mathematical functions that consider adequately surface and subsurface geochemical dispersions in downstream variations in stream sediment chemical compositions. Theoretically, concentrations of indicator elements in stream sediments decrease with increasing distance downstream from an anomaly source (e.g., mineral occurrences). However, in many cases, the
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Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
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gradient of downstream decay of stream sediment chemical compositions is not linear because of non-linear downstream variations of factors that could influence fluvial dispersion of sediments (e.g., Xie et al., 2010; Yilmaz, 2003). Therefore, complex anomaly patterns exist such that (a) weak anomalies are concealed within the strong variance of background, (b) background values exist in anomalous areas, (c) concealed or sub-outcropping deposits are not associated with geochemical anomalies, and (d) stream sediments immediately downstream of mineral deposits exhibit non-anomalous element contents (Cheng, 2007). Nevertheless, various studies have considered interpolation of geochemical variables (contour mapping) or the SCB (i.e., the area of influence of every stream sediment sample) in developing models of surface geochemical dispersion (Hawkes, 1976; Moon, 1999; Spadoni, 2006) and geochemical background (Bonham-Carter and Goodfellow, 1984, 1986; Bonham-Carter et al., 1987, 1988; Moon, 1999) in order to map anomalous areas (Bonham-Carter, 1994; Carranza, 2004a, 2008, 2010a,b; Carranza and Hale, 1997; Ódor et al., 1997; Rantitsch, 2004; Spadoni et al., 2004). In this regard, although anomalous patterns caused by mineralization processes can be highly complex as shown by their spatial and frequency properties (Zuo et al., 2009), anomalies can be recognized by ‘undiluting’ concentrations of indicator elements in stream sediment samples far downstream from an anomaly source as a function of topographic, geomorphologic, and geologic factors (Carranza, 2004a; Hawkes, 1976; Moon, 1999). However, according to Hawkes (1976) and Solovov (1987), analysis of geochemical background in stream sediments by using SCBs is applicable mainly to low order streams, where clastic dispersion predominates, but not to high order streams, where hydromorphic dispersion, particularly groundwater processes, predominates. Furthermore, both SCB modeling and any contour mapping method are dependent on sampling sites and density. In SCB modeling, the area of influence of each stream sediment sample is just its upstream area until the next sample upstream. In this regard, as the number of stream sediment samples becomes large and as drainage patterns become intricate, calculation of areas upstream of individual stream sediment samples for SCB modeling becomes unwieldy (Carranza, 2004a). Hence, SCB modeling or contour mapping may lead to rough estimates of geochemical background and, thus, errors for the detection of anomaly sources. In addition, the detection of diluted sources of anomalies is a major problem where drainage basins of extremely different sizes are sampled and where multiple samples are taken within a drainage basin (Moon, 1999). Considering the foregoing problems of either SCB modeling or contour mapping and, in view of complex relationships between element contents in stream sediments
and their plausible sources, the purpose of this paper is to develop an improved approach to facilitate analysis of complex anomaly patterns in order to vector into mineralized zones and, therefore, support MPM. Here, instead of using sampled stream segments to define SCBs, we used main rivers to outline their catchment basins. Catchment basins of main rivers (hereafter referred to as drainage catchment basins or DCBs) rather than SCBs have been used for modeling and studying contamination, erosion, hydrogeochemical dispersion, groundwater migration and sources of sediment (e.g., Biondić et al., 2006; Matějíček et al., 2003; McKergow et al., 2005; Prtoljan et al., 2012; Sidorchuk et al., 2003). Every main river corresponds to a DCB. The outlet of each main river, which is its junction with another main river, is the starting point for outlining its DCB. Hence, the whole area upstream of a main river outlet is its DCB. For performing this process, as Martz and Garbrecht (1993) and Carranza (2008) noted a digital elevation model (DEM) of the study area was used to delineate drainage networks and their corresponding outlets and catchments automatically. In this regard, GIS-based geochemical mapping can be carried out (Carranza, 2008). Furthermore, there is also an algorithm to delineate catchment basin of a set of points (sampling points or here, outlet points of main rivers) by using DEM (Jones, 2002). Hence, unlike in SCB modeling where the outlet of each stream segment is a stream sediment sample location, the DCB approach described here is not dependent on sample locations and sampling density. For attributing measured or derived geochemical variables to individual DCBs to evaluate their relative exploration importance, we developed a new weighting scheme for each DCB – a fuzzy DCB score (FCx) – based on the distribution of anomalous and background samples in each DCB. The improved technique described here is called weighted drainage catchment basin (WDCB), which is a kind of discrete field modeling of geochemical landscapes (Carranza, 2010b) and an efficient way to generate a weighted stream sediment geochemical evidential map for integrating among other geo-exploration data, to map mineral prospectivity. In this study, we used the GMPI (geochemical mineralization probability index — a derived multivariate geochemical signature) data set of Yousefi et al. (2012) to test and evaluate the proposed WDCB approach in mapping porphyry-Cu prospectivity in the Kerman province, southeast of Iran. 2. Methods and results In the WDCB approach, drainage networks are divided into DCBs, which represent the skeleton and infrastructure of a topographic and
Fig. 1. Classification and distribution of strongly anomalous, moderately anomalous and background GMPI values of stream sediment samples in each DCB.
Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
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geomorphologic model of a stream system (Matějíček et al., 2003) for investigating its related features (Sidorchuk et al., 2003), e.g., geochemical dispersion. Stream sediments contain information about geochemical dispersion in DCBs (Xie et al., 2010) because element compositions of stream sediments are derived from the weathering, erosion (Carranza, 2010b; Howarth and Thornton, 1983; Ohta et al., 2005) and dispersion of materials within a DCB. Spadoni (2006) has demonstrated that the area of spatial influence of a stream sediment sample extends downhill and uphill of a sample location and has argued that it is reasonable to suppose that geochemical values immediately downstream of each sample location do not differ too much from the values at a sample location itself. Hence, the area occupied by each DCB is morphologically the area of influence of all stream sediment samples within each DCB. Hawkes (1976) and Moon (1999) have considered the whole area of a DCB to propose idealized formulas for evaluating background and anomalous stream sediment samples. 2.1. Creation of WDCB map To create a WDCB map, we attributed a weight to the whole area of each DCB based on the notion that stream sediments associated with mineralized DCBs have higher concentrations of elements compared to stream sediments associated with non-mineralized DCBs. To assign a weight to each DCB, element contents or derived geochemical variables of all stream sediment samples within a DCB were used for defining its importance for prospecting mineral deposits. Hence, in mineralized DCBs the mean and/or median of element contents, with respect to all samples (both anomaly and background samples), is generally higher than in non-mineralized DCBs. To demonstrate this statement, we delineated DCBs in the study area (n= 55) (Fig. 1) and we used the dataset of GMPI values derived by Yousefi et al. (2012) for every stream sediment sample in the study area. The GMPI (Yousefi et al., 2012) represents a multivariate geochemical signature of the deposit-type sought that can be derived for a stream sediment geochemical data set after transforming the original data values to address the closure problem of composition data by using isometric logratio transformation (Buccianti, 2013; Carranza, 2011; Egozcue et al., 2003). Subsequently, we used a graphical tool of exploratory data analysis (EDA), Fig. 14 of Yousefi et al. (2012), and selected two threshold GMPI values (0.8 and 0.86) to define three classes of GMPI values (Fig. 1) — strong anomalies, moderate anomalies and background. Then, the median of GMPI values (MT) of all stream sediment samples in each DCB was calculated (Table 1) and plotted (Fig. 2). According to Fig. 2, the values of MT for DCBs with known Cu occurrences (e.g., DCBs numbered 6, 17, 12, 5, 3, 15, 25, 51, 37, 27, 40, 23, 19, 10 and 20) are generally higher than other DCBs. In Fig. 2, there are DCBs with no known Cu occurrences (e.g., those numbered 1, 8, 13, 28, 29, 21, 26, 46, 48, 50, and 54) for which the values of MT are comparable to those of DCBs with known Cu occurrences. Hence, the former DCBs can be considered as promising areas. Furthermore, we calculated and plotted the median of background GMPI values (Mb) for each individual DCB (Table 1, Fig. 3). For this analysis, we used for each DCB only background samples (i.e., those with GMPI values of b 0.8). The distributions of MT and Mb are similar (Figs. 2, 3), although the level of geochemical background in mineralized DCBs is higher than that in non-mineralized DCBs. The similarity of Figs. 2 and 3 illustrates that using DCBs instead of SCBs does not undermine the recognition of mineralized areas despite simplification of geochemical patterns due to aggregation of samples in DCBs. Therefore, further weighting of DCBs as described below is justified and could support MPM. The generation of a map of WDCB, which can be used in MPM, is discussed here with demonstrations of its applications to map geochemical anomalies for potential of porphyry-Cu deposits in the case study area. Because stream sediment samples in every DCB have different element contents, the weight assigned to each DCB is based on classes of
3
Table 1 Calculated parameters of each DCBs: NT = number of samples; MT = median of GMPI values; ns = number of samples with strongly anomalous GMPI values; Ms = median of strongly anomalous GMPI values; nm = number of samples with moderately anomalous GMPI values; Mm = median of moderately anomalous GMPI values; nb = number of background GMPI values; Mb = median of background GMPI values; Cx = DCB score; and FCx = fuzzy DCB score. DCB ID
NT
MT
Ns
Ms
nm
Mm
nb
Mb
Cx
FCx
1 2 3 4a 5 6 7 8 9 10 11 12 13 14 15 16a 17 18a 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55a
39 22 33 0 41 106 36 17 89 63 30 139 31 44 36 0 48 0 35 56 36 1 33 37 17 43 14 22 24 3 108 1 68 19 50 5 48 19 56 35 18 17 4 28 5 26 1 8 69 8 18 1 85 10 0
0.63 0.46 0.65 0.54 0.61 0.66 0.39 0.50 0.45 0.49 0.38 0.69 0.62 0.48 0.55 0.42 0.61 0.40 0.42 0.57 0.54 0.47 0.56 0.41 0.54 0.48 0.52 0.50 0.55 0.49 0.43 0.83 0.44 0.40 0.46 0.46 0.53 0.42 0.44 0.53 0.37 0.37 0.40 0.37 0.42 0.64 0.49 0.72 0.38 0.72 0.66 0.89 0.43 0.49 0.66
2 0 1 0 4 22 0 0 0 2 0 42 1 1 1 0 6 0 1 9 1 0 2 0 2 5 1 1 0 0 2 0 0 0 2 0 6 0 4 5 0 0 0 0 0 7 0 3 1 3 7 1 1 0 0
0.92 0.00 0.93 0.00 0.88 0.88 0.00 0.00 0.00 0.94 0.00 0.90 0.87 0.89 0.87 0.00 0.90 0.00 0.88 0.98 0.87 0.00 0.89 0.00 0.94 0.87 0.93 0.86 0.00 0.00 0.93 0.00 0.00 0.00 0.89 0.00 0.92 0.00 0.89 0.95 0.00 0.00 0.00 0.00 0.00 0.96 0.00 0.95 0.86 0.97 0.88 0.89 0.95 0.00 0.00
4 0 3 0 5 9 0 0 2 1 0 9 3 3 4 0 5 0 0 0 1 0 4 1 1 4 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
0.85 0.00 0.84 0.00 0.83 0.84 0.00 0.00 0.84 0.83 0.00 0.84 0.84 0.83 0.82 0.00 0.86 0.00 0.00 0.00 0.85 0.00 0.82 0.84 0.82 0.84 0.00 0.84 0.00 0.00 0.00 0.83 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.80 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
33 22 29 0 32 75 36 17 87 60 30 88 27 40 31 0 37 0 34 47 34 1 27 36 14 34 13 19 24 3 106 0 68 19 48 5 42 19 52 30 18 17 4 28 5 18 1 5 68 5 11 0 84 10 0
0.58 0.46 0.62 0.52 0.54 0.58 0.39 0.50 0.44 0.47 0.38 0.58 0.59 0.45 0.51 0.41 0.53 0.40 0.41 0.49 0.52 0.47 0.48 0.39 0.47 0.38 0.49 0.45 0.55 0.49 0.42 0.00 0.44 0.40 0.44 0.46 0.47 0.42 0.40 0.46 0.37 0.37 0.40 0.37 0.42 0.51 0.49 0.58 0.37 0.58 0.51 0.00 0.42 0.49 0.21
80.73 45.62 78.54 63.17 87.82 109.71 39.28 50.33 46.46 56.78 38.27 129.48 75.72 57.92 69.16 42.87 92.14 40.57 47.35 88.60 61.08 47.24 75.37 42.90 81.39 76.34 65.07 65.41 54.62 49.43 46.37 166.28 43.98 39.56 52.66 46.19 76.09 41.65 56.31 79.66 37.29 36.89 40.17 37.33 42.00 119.41 48.87 142.48 40.63 145.08 133.99 265.54 44.95 48.80 155.25
0.91 0.51 0.90 0.76 0.94 0.98 0.41 0.59 0.52 0.68 0.39 0.99 0.88 0.70 0.83 0.47 0.95 0.43 0.54 0.94 0.74 0.54 0.88 0.47 0.91 0.88 0.79 0.79 0.65 0.57 0.52 0.99 0.48 0.41 0.62 0.52 0.88 0.45 0.67 0.90 0.38 0.37 0.42 0.38 0.45 0.99 0.56 0.99 0.43 0.99 0.99 0.99 0.50 0.56 0.99
a The parameters MT, Mb and Cx of DCBs numbered 4, 16, 18 and 55, which occupy very small areas and have not been sampled, were calculated based on average values in two upstream DCBs.
samples. To calculate a score (Cx) for each DCB the following generic equation is used: n n C x ¼ L M L L 100 þ ðL−1Þ M L−1 L−1 100 NT NT nL−2 100 þ… þ ðL−2Þ ML−2 NT nL−Lþ1 100 þ M L−Lþ1 NT
ð1Þ
Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
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M. Yousefi et al. / Journal of Geochemical Exploration xxx (2013) xxx–xxx
Fig. 2. Distribution of median GMPI values (MT) of stream sediment samples per DCB.
where L is the number of geochemical classes according to a geochemical data set in a study area, ML is the median of element contents (or the median of derived values of multivariate signature) of stream sediment samples in the most anomalous class (L), nL is the number of stream sediment samples in the most anomalous class per DCB, NT is the total number of stream sediment samples per DCB, L − 1 and L − 2 represent the succeeding geochemical classes with decreasing level of anomaly, and L − L + 1 represents the most background geochemical class. Thus, Cx is related to the levels of element contents in all stream sediment samples in every DCB. Hence, a map of Cx is similar to an interpolated map although it is a discrete field model because values of Cx are attributed to DCBs. In Eq. (1), the number of stream sediment samples per geochemical class influences the relative importance of a DCB for mineral exploration. For example, if a DCB has a higher number of anomalous stream sediment samples compared to another DCB, the Cx of the former is generally higher than that of the latter. For the present study, based on Fig. 1, we used the GMPI data set of Yousefi et al. (2012), consisting of values of derived multivariate geochemical signature of porphyry-Cu deposit that are classified into three classes for mapping mineral prospectivity — strongly anomalous (s),
moderately anomalous (m) and background (b). To calculate Cx per DCB the following equation is used: n n n C x ¼ 3 Ms s 100 þ 2 Mm m 100 þ Mb b 100 : NT NT NT
ð2Þ
The calculated values of Cx per DCB (Table 1) according to Eq. (2) do not lie within the [0,1] range. Thus, values of Cx are non-probabilistic and are not appropriate to represent the probability of presence or absence of deposit-type sought in each DCB. To assign probabilistic values to every DCB (e.g., fuzzy weights) for application in MPM, we transformed the calculated values of Cx by applying the following logistic function: FC x ¼
1 1 þ e−sðC x −iÞ
ð3Þ
where FCx is a fuzzy score, and i and s are inflection point and slope, respectively, of the logistic function. The parameters i and s determine the shape of the logistic function and, hence, the output values. These parameters are chosen based on a subjective assessment. For the present study, the values 0.065 and 45 were used for s and i, respectively, in
Fig. 3. Distribution of median background GMPI values (Mb) of stream sediment samples per DCB.
Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
M. Yousefi et al. / Journal of Geochemical Exploration xxx (2013) xxx–xxx
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Fig. 4. Distribution of FCx values of DCBs.
Eq. (3) to obtain FCx per DCB (Table 1). The distribution of FCx is shown in Fig. 4. According to Fig. 4, DCBs with known mineral occurrences have high values of FCx but some DCBs (e.g., those numbered 46, 26, 13, 1, 28, 50 and 48) without known mineral occurrences also have high values of FCx. These latter DCBs are therefore potentially valuable for further exploration. This result is based only on the comparison of FCx values in DCBs, because like SCB modeling or contour mapping, areas with higher values of indicator geochemical variables have higher priority for further exploration. Further verification (acceptance or rejection) of potentially valuable areas in a geochemical map is carried out using other geo-exploration evidential layers especially in MPM (Carranza, 2008), here by using a map of proximity to intrusive contacts. Hence, the map in Fig. 4 is the WDCB map, which can be integrated with other geo-evidence layers for MPM. 2.2. Performance of WDCB approach in MPM The study area is a small part of the Urumieh-Dokhtar Volcanic belt of Iran (Yousefi et al., 2012). This belt has a great potential for
porphyry-Cu deposits as far as its geology suggests (e.g., Atapour and Aftabi, 2007; Boomeri et al., 2009; Hezarkhani, 2006a,b). Several published works have discussed and verified the genetic and spatial relationships of intrusive rocks with porphyry-Cu deposits in the Urumieh-Dokhtar volcanic belt (e.g., Atapour and Aftabi, 2007; Boomeri et al., 2009; Hezarkhani, 2006a,b, 2009). The porphyry-Cu deposits in this belt are related to intrusive porphyries and igneous plutons (e.g., Hezarkhani, 2009; Lundmark et al., 2005; Peytcheva et al., 2009) because the margins of intrusions are strongly fractured and, thus, enabled hydrothermal fluids to exchange heat and mass with the intruded rocks (Guillou-Frottier and Burov, 2003). Hence, areas close to contacts of intrusive bodies have stronger likelihood of porphyry-Cu mineralization than areas farther to the contacts (Campos et al., 2002; Peytcheva et al., 2009; Xiaoming et al., 2007). To demonstrate the superior ability of the WDCB mapping approach over SCB modeling or contour mapping for facilitating analysis of complex anomaly patterns in order to vector into mineralized zones to support MPM, we used buffers around intrusive bodies in the study area as indicators of the mineral deposit-type sought. In this study, porphyry-Cu deposits represent the mineral deposit-type sought. For
Fig. 5. Map of fuzzy scores of proximity to intrusive contacts (white curvilinear features).
Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
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Fig. 6. Map of interpolated GMPI values, derived using inverse distance interpolation method, representing multi-element signature of porphyry-Cu mineralization (Yousefi et al., 2012). Because the stream sediment sampling density in the study area is high, the spatial distributions of GMPI values portrayed by SCB modeling (not shown here) and by interpolation (as shown here) are approximately the same.
MPM of this deposit-type, a map of distance from contacts of intrusive bodies with other lithologies was generated first. Then, because proximity to intrusive contacts represents favorability for porphyry-Cu mineralization (Carranza, 2002, 2004b; Carranza and Hale, 2002a,b; Carranza et al., 2008; Xiaoming et al., 2007), we used the inverse of distance to outlines of intrusive bodies to represent favorability for porphyry-Cu occurrence. After that, to assign fuzzy proximity weights to distances from intrusive contacts, we used a logistic function like Eq. (3) to transform the values in the map of inverse of distances into the [0, 1] range (Fig. 5). Because the map in Fig. 5 is a weighted fuzzy evidence layer, it can be integrated with a weighted fuzzy geochemical evidential map. In addition, because a map of GMPI (Yousefi et al., 2012) or a WDCB map (Fig. 4) is a weighted fuzzy geochemical evidential layer, either of them can be integrated with the map of fuzzy proximity scores of intrusive contacts (Fig. 5) for MPM through fuzzy logic modeling (Carranza and Hale, 2001; Porwal et al., 2003, 2004). To demonstrate that the WDCB approach is superior over contour mapping or SCB modeling, we used the fuzzy “AND” operator to integrate the map of fuzzy scores of proximity to intrusive contacts (Fig. 5) with two kinds of weighted geochemical evidential maps, an interpolated map of
GMPI values (Fig. 6) and the map of WDCB (Fig. 4). Thus, we generate two fuzzy prospectivity maps (Figs. 7, 8) for comparing and evaluating the WDCB method with a contour mapping method. We used DCBs numbered 12, 15, 37, and 40 with known mineral occurrences for comparing and evaluating the results. In this regard, in DCB number 12 there are anomalous samples immediately downstream of known mineral occurrences. Hence, the resulting prospectivity map (Fig. 7) shows high prospectivity values around the mineral occurrences in DCB number 12. However, as shown in Fig. 1, there are no anomalies immediately downstream of the known mineral occurrences in DCBs numbered 15, 37, and 40. In these DCBs, anomalous samples are located far downstream of the known mineral occurrences. Fig. 6 shows that areas immediately around the known mineral occurrences in DCBs numbered 15, 37, and 40 are not anomalous. Thus, if the map in Fig. 6 is integrated with the map of fuzzy scores of proximity to intrusive contacts (Fig. 5), the resulting prospectivity map (Fig. 7) does not show high prospectivity values around the mineral occurrences in DCBs numbered 15, 37 and 40. Hence, some important areas are missed for further prospecting. For example, the eight mineral occurrences in DCBs numbered 15, 37, and 40 are missed if the contour mapping (or SCB modeling) is used to map geochemical anomalies. In contrast, if the map of
Fig. 7. Map of fuzzy scores of mineral prospectivity generated by combining map of fuzzy scores of proximity to intrusive contacts (Fig. 5) and map of interpolated GMPI values (Fig. 6) using the fuzzy “AND” operator. White curvilinear features represent intrusive contacts.
Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
M. Yousefi et al. / Journal of Geochemical Exploration xxx (2013) xxx–xxx
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Fig. 8. Map of fuzzy scores of mineral prospectivity generated by combining map of fuzzy scores of proximity to intrusive contacts (Fig. 5) and map of FCx (WDCB; Fig. 4) using the fuzzy “AND” operator. White curvilinear features represent intrusive contacts.
WDCB (Fig. 4) is integrated with the map of fuzzy scores of proximity to intrusive contacts (Fig. 5), the resulting prospectivity map (Fig. 8) shows higher prospectivity values at and around the known mineral occurrences in DCBs numbered 15, 37, and 40 as well as in DCB number 12. The foregoing comparison of Figs. 7 and 8 demonstrates that, in situations with complex anomaly patterns (as described in the Introduction), a map of WDCB is more effective than a contour or interpolated map (or a SCB model) for mapping of mineral prospectivity. For further illustration of this statement, the values of mineral prospectivity at the locations of all 32 known mineral occurrences in the two resulting fuzzy prospectivity maps, Figs. 7 and 8, are shown in Fig. 9 for comparison. This figure illustrates that most of the mineral occurrences in the study area are associated with high prospectivity values if the WDCB map instead of the interpolated GMPI map is used for mineral prospectivity. Furthermore, the percentages of known mineral occurrences delineated depending on threshold fuzzy prospectivity scores (Fig. 10) show that the prediction rate of the prospectivity map generated by using the WDCB map (Fig. 8) is higher than that of the prospectivity map generated by using the interpolated GMPI map (Fig. 7).
3. Discussion Stream sediment geochemical anomalies are usually spatially noncoincident with other indicative geological features (e.g., igneous intrusions) because the former represent transported materials whereas the latter are in situ. Accordingly, integration of geochemical evidential maps derived from stream sediment geochemical data with other geo-exploration evidence layers may result in prospectivity maps with poor prediction rate. Hence, in explored regions with complex stream sediment anomaly patterns (e.g., DCBs numbered 15, 37, and 40 in Fig. 6) some mineralized areas are likely to be missed (e.g. Carranza, 2011; Pazand et al., 2011; Ranasinghe et al., 2008; Yousefi et al., 2012; Zuo, 2011; Zuo et al., 2009). A reason for this, in addition to the complex stream sediment anomaly patterns, is lack of efficacy of geochemical anomaly mapping methods to address the transported nature of stream sediment geochemical anomalies. That means, because of differences in physical and chemical characteristics (e.g., mobility) of geochemical elements, stream sediment samples with anomalous concentrations of different indicator elements can be located differently downstream from an anomaly source (Xie et al., 2010; Yilmaz, 2003; Yousefi et al.,
Fig. 9. Comparison of fuzzy scores of mineral prospectivity, obtained by using the WDCB map (Fig. 4) and the interpolated GMPI map (Fig. 6), at known mineral occurrences.
Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013
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M. Yousefi et al. / Journal of Geochemical Exploration xxx (2013) xxx–xxx
Fig. 10. Percentages of known mineral occurrences delineated depending on threshold values of fuzzy scores of mineral prospectivity obtained by using the WDCB map (Fig. 4) and the interpolated GMPI map (Fig. 6).
anomalies in preliminary exploration stages with relatively low sampling density.
2012). Therefore, consideration of DCBs but not SCBs and attribution of FCx to DCBs comprise a useful approach to address complex stream sediment anomaly patterns. This is because the WDCB approach considers the whole area of each DCB, as an infrastructure of a topographic and geomorphologic model (Matějíček et al., 2003), but it does not undermine the recognition of mineralized areas. A map of WDCB can be generated for uni-element concentration data (e.g., Carranza, 2004a, 2011) or for derived multi-element associations (e.g., Chandrajith et al., 2001; Grunsky et al., 2009; Halfpenny and Mazzucchelli, 1999; Yousefi et al., 2012). In the latter situation, factor scores (e.g., Helvoort et al., 2005; Kumru and Bakac, 2003; Reimann et al., 2002; Sun et al., 2009) or values of GMPI (Yousefi et al., 2012) can be used as input values to calculate FCx.
However, because sampling density affects SCB modeling, this paper has demonstrated in an area with certain sampling sites and density that the WDCB approach is better than contour and SCB mapping methods to map geochemical anomalies. This is because the former delineates complex anomaly patterns independent of sampling sites while the two latter methods are dependent on sampling sites and density, and, consequently, are inefficient for delineating complex anomaly patterns. This statement is illustrated especially in mineral potential mapping where WDCB map is integrated with other geoexploration evidential layers.
4. Concluding remarks
Acknowledgments
The weighted drainage catchment basin (WDCB) modeling of stream sediment geochemical landscapes is an approach whereby the relative importance of all stream sediment samples and geochemical anomaly classes (here strongly anomalous, moderately anomalous and background) in every drainage catchment basin (DCB) are considered in assigning a weight to each of them for prospecting of mineral deposits. The following advantages of the WDCB modeling approach to geochemical anomaly mapping compared to the sample catchment basin (SCB) modeling approach and a contour mapping method have been demonstrated in this paper:
The authors thank Madan Pars Asia (MPA) consulting engineering company, especially Dr. Krimi, and Mr. Sahebzamani for supplying the necessary data to do this research work. The authors express special thanks to Mr. Esfehanipoor, head of exploration department of National Iranian Copper Industries Company (NICICO), for some support. We thank Dr. Antonella Buccianti for her suggestions for improving this paper. We also appreciate the comments of the anonymous reviewers.
1. Anomalies mapped in WDCB discrete field models of stream sediment geochemical landscapes exhibit stronger positive spatial associations with other indicative geological features (here intrusive contacts) of the mineral deposit-type sought. 2. In the WDCB approach, DCBs are given fuzzy weights representing their relative importance for prospecting the deposit-type sought. Hence, a map of WDCB can directly be used in fuzzy logic MPM as a weighted geochemical evidence layer. 3. The prediction rate of prospectivity map obtained based on WDCB approach with respect to known mineral deposit occurrences is higher than that in the resulting prospectivity map generated based on SCB model or contour map. 4. Using the WDCB approach avoids the disadvantage of mapping anomalies through SCB modeling or contouring in terms of strong dependence on sample locations and sampling density. Consequently, the WDCB approach is much more useful than SCB modeling or contouring for analysis and mapping stream sediment geochemical
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Please cite this article as: Yousefi, M., et al., Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling, Journal of Geochemical Exploration (2013), http://dx.doi.org/10.1016/j.gexplo.2013.01.013