- Email: [email protected]
Application of multifractal modeling technique in systematic geochemical stream sediment survey to identify copper anomalies: A case study from Ahar, Azarbaijan, Northwest Iran
Chemie der Erde 71 (2011) 397–402
Contents lists available at SciVerse ScienceDirect
Chemie der Erde journal homepage: www.elsevier.de/chemer
Application of multifractal modeling technique in systematic geochemical stream sediment survey to identify copper anomalies: A case study from Ahar, Azarbaijan, Northwest Iran Kaveh Pazand a,∗ , Ardeshir Hezarkhani b , Mohammad Ataei c , Yousef Ghanbari a a
Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Ponak Ave, Tehran, Iran Department of Mining, Metallurgy and Petroleum Engineering, Amirkabir University, Hafez Ave. No. 424, Tehran, Iran c Department of Mining, Geophysics and Petroleum Engineering, Shahrood University of Technology, 7th tir Sq., PO Box 36155-316, Shahrood, Iran b
a r t i c l e
i n f o
Article history: Received 26 October 2010 Accepted 16 August 2011 Keywords: Geochemical anomalies Background Thresholds Fractal
a b s t r a c t This research is based on the application of stream sediments to mineral exploration. Identifying the geochemical anomalies from background is a fundamental task in exploration geochemistry. This paper applied the element concentration–area (C–A) model, to separating the geochemical anomalies from background based on a fractal approach and for the compilation of geochemical mapping from stream sediment samples (n = 620) of the Ahar region (Iran), where some Cu mineralization occurs. Comparisons of the known copper occurrences against the anomalous area created using thresholds from C–A method illustrate these hits. All of known Cu mineralizations and moreover defines two extra Cu anomaly districts. Additional sampling (n = 186) around new Cu anomaly confirms this anomaly within the district. © 2011 Elsevier GmbH. All rights reserved.
1. Introduction Regional sampling of stream sediments has been widely used in geochemical prospecting aimed at mineral exploration. The chemical composition of stream-sediments provides information on the lithological composition of the drainage basin and on the presence of contaminants and mineral deposits (Rantitsch, 2000). Separation of anomalies from background values is crucial in exploration geochemistry. In the past recent years, geochemical anomalies have been identified by means of various methods and several methods have been proposed in the literature to account for it properly (Bakac et al., 1999; Cocker, 1999; Harris et al., 1999, 2000; Rantitsch, 2000; Chandrajith et al., 2001; Naseem et al., 2002; Changjiang et al., 2003; Albanese et al., 2007; Ji et al., 2007; Ranasinghe et al., 2008, 2009; Nude and Arshin, 2009; Grunsky et al., 2009; Sun et al., 2009; Zuo et al., 2009). Since Mandelbrot’s invention of the concept of fractals more than two decades ago (Mandelbrot, 1977; Mandelbrot et al., 1984), fractal and multifractal models have been applied to physical and chemical quantities with geometrical support. In the geological sciences, these approaches have been used to describe the irregularity of geological features and the spatial distribution patterns of geological objects (Zuo et al., 2009) and to characterize properties of
∗ Corresponding author. Tel.: +98 91 24055244; fax: +98 021 44603072. E-mail addresses: [email protected], [email protected] (K. Pazand). 0009-2819/$ – see front matter © 2011 Elsevier GmbH. All rights reserved. doi:10.1016/j.chemer.2011.08.003
mineralization and mineral deposits. Fractal and multifractal models have also been applied to separate anomalies from background values. Examples include the concentration–area model (C–A) (Cheng, 1995), the spectrum–area model (S–A) (Cheng, 2000), the multifractal singular value decomposition (MSVD) (Li and Cheng, 2004), the concentration–distance (C–D) model (Li et al., 2003), the mapping singularity technique (Cheng, 2007, 2008) and many other applications. These methods are gradually being adopted as an effective and efficient means to analyze spatial structures in metallic geochemical systems. In this paper, after a brief discussion of the “concentration–area” method, the application of multifractal modeling in a systematic geochemical stream sediment survey for identifying areas potentially favorable for Copper is described. For demonstration purposes, the Ahar district, northwest Iran, will be studied as an example. 2. The concentration–area method for geochemical anomaly separation This method serves to illustrate the relationship between the obtained results and the geological, geochemical and mineralogical information. Its most useful features are the easy implementation and the ability to compute quantitative anomalous thresholds. Cheng et al. (1994) proposed an element concentration–area (C–A) model, which may be used to define the geochemical background and anomalies. The model has the general form: A( ≤ )∞−a1 ;
A( ≤ )∞−a2
398
K. Pazand et al. / Chemie der Erde 71 (2011) 397–402
where A() denotes the area with concentration values greater than the contour value ; represents the threshold; and a1 and a2 are characteristic exponents. Using fractal theory, Cheng et al. (1994) derived similar power–law relationships and equations in extended form. The two approaches which were used to calculate A() by Cheng et al. (1994) were (1) the A() is the area enclosed by contour level q on a geochemical contour map resulting from interpolation of the original data using a weighted moving average method, and (2) A() are the values obtained by box-counting of original elemental concentration values. By box-counting, one superimposes grid with cells on the study region. The area A() for a given q is equal to the number of cells multiplied by cell area with concentration values greater than . Average concentration values are used for those boxes containing more than one sample. Area–concentration [A()] with element concentrations greater than usually show a power–law relation (Cheng et al., 1994). The breaks between straight-line segments on this plot and the corresponding values of have been used as cut-offs to separate geochemical values into different components, representing
different causal factors, such as lithological differences and geochemical processes. Factors such as mineralizing events, surface geochemical element concentrations, and surface weathering are of considerable importance (Lima et al., 2003). Multifractal theory may be interpreted as a theoretical framework that explains the power–law relations between areas enclosing concentrations below a given value and the actual concentrations. To demonstrate and prove that data distribution has a multifractal nature requires a rather extensive computation. This method has several limitation and accuracy problems, especially when the boundary effects on irregular geometrical data sets are involved (Agterberg et al., 1996; Li et al., 2003). The C–A method seems to be equally well applicable to all cases, which is probably due to the fact that geochemical distributions mostly satisfy the properties of a multifractal function. There is some idea that geochemical distributions are fractal in nature and behavior, at least empirically, according to Bolviken et al. (1992). Some approaches seem to support the idea that geochemical data distributions are multifractal, although this point is far from being proven (Afzal et al., 2010; Li et al., 2003). This idea
Fig. 1. Major structural zones of Iran (after Nabavi, 1976) and the location of the Ahar area in these zones and a modified and simplified geologic map of it (after Mahdavi and Amini Fazl, 1988).
K. Pazand et al. / Chemie der Erde 71 (2011) 397–402
399
Fig. 2. Sampling locations of stream sediments around Ahar region (for lithological units see caption of Fig. 1).
may provide and help the development of an alternative interpretation validation and useful methods to be applied to elemental geochemical distributions analysis. 3. Geological setting Continental collision between the Afro-Arabian continent and the Iranian microcontinent during closure of the Tethys ocean in the Late Cretaceous resulted in the development of a volcanic arc in NW Iran (Mohajjel and Fergusson, 2000; Babaie et al., 2001; Karimzadeh Somarin, 2005). In Iran, all known copper mineralization occurs in the Cenozoic Urumieh-Dokhtar orogenic belt (Fig. 1). This belt was formed by subduction of the Arabian plate beneath central Iran during the Alpine orogeny and hosts major porphyry and skarn deposits. (Hezarkhani and Williams-Jones, 1998; Karimzadeh Somarin and Moayyed, 2002). The Ahar sheet is located in E Tabriz, East Azarbaijan province, NW Iran in the northern part of the Urumieh-Dokhtar magmatic arc (Fig. 1) and covers an area of about 2500 km2 . The composition of volcanic rocks in this area varies from calcalkaline to alkaline during Eocene to Quaternary. Regionally, the oldest country rocks are Cretaceous sedimentary and subvolcanic rocks include conglomerate, marl, shale, andesite, tuff and pyroclastic rock, followed by Eocene latite and ignimbrite. The Oligocene–Miocene intrusive rocks include granodiorite, diorite, gabbro and alkali syenite (Mahdavi and Amini Fazl, 1988). The youngest rocks of the region are Quaternary Volcanic (Fig. 1).
4. Sampling and analysis From 2003 to 2004, 620 stream sediment samples of the −80 mesh (0.18 mm) fraction were collected from the study area with a density of one sample per 2.5–3 km2 for outcrop area and 7–10 km2 for desert area (Fig. 2). Samples were collected from the centre of the streams, avoiding, wherever possible, the collection of organic matter. Each sample represents composite material taken from five points over a stream stretch of 200–500 m. The sample collection protocols are described in detail by Salminen et al. (1998). Analyses were carried out by Geological Survey of Iran Laboratories. Atomic absorption spectrometry (AAS) was used for (Cu, Mo). Statistical results show that Cu and Mo mean values are 66.94 and 10.68 ppm, respectively presented in Table 1. Their distributions are as shown in Fig. 3 and are nearly normal.
Table 1 Statistical parameters of Cu and Mo in stream sediments.
Mean Median Std. deviation Variance Skewness Kurtosis Minimum Maximum
Cu (ppm)
Mo (ppm)
66.94 60.34 36.05 1299.45 4.53 34.25 22.50 420.00
10.68 2.65 135.46 18348.31 24.30 599.52 0.30 3350.00
400
K. Pazand et al. / Chemie der Erde 71 (2011) 397–402
Fig. 3. Cu and Mo histograms.
Variation between maximum and minimum for these data shows a wide range. Concentration–area relations were computed by assigning an area of influence to each sampled point and summing all elemental areas whose concentration lies below a given value. This procedure was repeated for different elemental concentrations. The evaluated grades in cells were sorted out based on decreasing grades and cumulative areas were calculated for grades. Finally, log–log plots were constructed for Cu and Mo (Fig. 4). Geochemical populations are delineated in these plots of Cu and Mo. On the basis of this procedure, there are 4 populations for Cu and Mo respectively as shown in Fig. 4. Anomalous Cu threshold is 44 ppm and its high intensity anomaly is 277 ppm. Also, it is clear that there are two stages of Cu enrichments based on log–log plot as depicted in Fig. 4. The first event for Cu C–A variations occurred at grades below 224 ppm. The second event shows up between grades 224 ppm and 156 ppm. The final event included major Cu mineralization which occurred and was interpreted in grades higher than 277 ppm. Mo threshold and high intensity anomalies are 7.4 ppm, and 262 ppm (Fig. 4). If elements with non-uniform behavior area plotted on log–log coordinates, the plot will have different slopes and various straight-line segments which area connects at an angle or with breaks on the plot. Breaks between the straight-line segments and the corresponding values of Cu and Mo have been used as cut-offs to reclassify distribution maps and are presented in Fig. 5. Based on these results, elemental grade distribution maps were drawn (Fig. 5). Clearly most Cu anomalies are located in six districts of the area, especially high intensity Cu anomalies are located in three parts (Fig. 5) of them (Fig. 5). Mo anomalies are situated in North and Western parts. High intensity Mo anomalies are located in the Western part of area. Based on these maps, potential co-presence of these elements is located in two parts of Mo map as shown in Fig. 5. 5. Comparison with known occurrences Comparisons of the known Copper occurrences against the anomalous area created using thresholds from C–A method (see
Fig. 4. Log–log plots (C–A method) for Cu and Mo. The vertical axis represents cumulative cell areas A(), with elemental concentration values greater than , and the horizontal axis is the actual value ().
Fig. 5) show that these hit all of four known occurrences districts and moreover define two additional Cu anomalies districts. There are no known deposits and occurrences for Mo in the area but Mo anomalies coincidence with the Cu anomalies. To proof and
Table 2 Statistical parameter of Cu in second stage sampling.
Cu (ppm)
N
Mean
Median
Std. deviation
Variance
Minimum
Maximum
186
106.96
90.50
96.39
9290.50
14
961
K. Pazand et al. / Chemie der Erde 71 (2011) 397–402
401
Fig. 5. Cu and Mo geochemical population distribution maps based on C–A method and the comparison between the distribution map of Cu and the known copper occurrences from Ahar sheet.
Fig. 6. Stream sediments sample locations of anomaly district 1 in step 2 and Cu distribution of them.
402
K. Pazand et al. / Chemie der Erde 71 (2011) 397–402
identity the exact anomaly identified from stream sediment in a second stage, 186 additional samples were taken around the anomalous sites of districts; results confirm an anomaly within the district (Fig. 6). Statistical results show that Cu mean value is 106.96 ppm and maximum value is 961 ppm, presented in Table 2. 6. Conclusions In this study, it has been suggested that by using stream sediment exploration technique, it is possible to identify regional metalliferous regimes, which are useful in defining areas for Cu exploration. Distribution pattern of Cu and Mo in sediments are useful to indicate areas where new mineral deposits may exist. Study on the Ahar sheet area shows the potential use of the C–A method for geochemical anomaly separation as a useful tool for geochemical and mineral exploration. The advantages of this method are essentially its simplicity, and easy computational implementation, as well as the possibility to compute a numerical value of concentrations, i.e., the anomalous threshold, which is the most useful criteria for cross examination of information with numerical data from different sources. In the C–A procedure, original element concentration data can be treated directly, and therefore it is unnecessary to process the data with pretreatment of any smoothing procedure, thus enhancing recognition of a geochemical anomaly from background. There is a very good correlation between the calculated anomalous threshold and anomaly values and known copper occurrences in the Ahar area. References Afzal, P., Khakzad, A., Moarefvand, P., Rashidnejad, N., Esfandiari, B., Alghalandis, Y.F., 2010. Geochemical anomaly separation by multifractal modeling in Kahang (Gor Gor) porphyry system, Central Iran. Journal of Geochemical Exploration 104, 34–46. Agterberg, F.P., Cheng, Q., Brown, A., Good, D., 1996. Multifractal modeling of fractures in the Lac du Bonnet batholith, Manitoba. Computers and Geosciences 22 (5), 497–507. Albanese, S., De Vivo, B., Lima, A., Cicchella, D., 2007. Geochemical background and baseline values of toxic elements in stream sediments of Campania region (Italy). Journal of Geochemical Exploration 93, 21–34. Babaie, H.A., Ghazi, A.M., Babaei, A., La Tour, T.E., Hassanipak, A.A., 2001. Geochemistry of arc volcanic rocks of the Zagros crush zone, Neyriz, Iran. Journal of Asian Earth Science 19, 61–76. Bakac, M., Kumru, M.N., Bassari, A., 1999. R-mode factor analysis applied to the exploration of radioactivity in the Gediz River. Journal of Radioanalytical and Nuclear Chemistry 242, 457–465. Bolviken, B., Stokke, P.R., Feder, J., Jossang, T., 1992. The fractal nature of geochemical landscapes. Journal of Geochemical Exploration 43, 91–109. Chandrajith, R., Dissanayake, C.B., Tobschall, H.J., 2001. Application of multi-element relationships in stream sediments to mineral exploration: a case study of Walawe Ganga Basin, Sri Lanka. Applied Geochemistry 16, 339–350. Changjiang, Li., Tuhua, M., Junfa, S., 2003. Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background. Journal of Geochemical Exploration. 77, 167–175. Cheng, Q., Agterberg, F.P., Ballantyne, S.B., 1994. The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration 51, 109–130. Cheng, Q., 2007. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geological Reviews 32, 314–324. Cheng, Q., 2008. Non-linear theory and power-law models for information integration and mineral resources quantitative assessments. Mathematical Geology 40, 503–532. Cheng, Q., 1995. The perimeter-area fractal model and its application to geology. Mathematical Geology 27, 69–82. Cheng, Q., 2000. GeoData Analysis System (GeoDAS) for mineral exploration: user’s guide and exercise manual. Material for the Training Workshop
on GeoDAS held at York University, Toronto, Canada, vol. 1, 3, p. 204. http://www.gisworld.org/geodas. Cocker, M.D., 1999. Geochemical mapping in Georgia, USA: a tool for environmental studies, geologic mapping and mineral exploration. Journal of Geochemical Exploration 67, 345–360. Grunsky, E.C, Drew, L.J., Sutphin, D.M., 2009. Process recognition in multielement soil and stream-sediment geochemical data. Applied Geochemistry 24, 1602–1616. Harris, J.R., Grunsky, E.C., Wilkinson, L., 2000. Effective use and interpretation of lithogeochemical data in regional exploration programs. Ore Geological Reviews 16, 107–143. Harris, J.R., Wilkinson, L., Grunsky, G., Heather, K., Ayer, J., 1999. Techniques for analysis and visualization of lithogeochemical data with applications to the Swayze greenstone belt, Ontario. Journal of Geochemical Exploration 67, 301–334. Hezarkhani, A., Williams-Jones, A.E., 1998. Controls of alteration and mineralization in the Sungun porphyry copper deposit. Iran: evidence from fluid inclusions and stable isotopes. Economic Geology 93, 651–670. Ji, H., Zeng, D., Shi, Y., Wu, Y., Wu, X., 2007. Semi-hierarchical correspondence cluster analysis and regional geochemical pattern recognition. Journal of Geochemical Exploration 93, 109–119. Karimzadeh Somarin, A., 2005. Petrology and geochemistry of Early Tertiary volcanism of the Mendejin area. Iran, and implications for magma genesis and tectonomagmatic setting. Geodinamica Acta 18, 343–362. Karimzadeh Somarin, A., Moayyed, M., 2002. Granite- and gabbrodiorite-associated skarn deposits of NW Iran. Ore Geological Reviews 20, 127–138. Li, Q., Cheng, Q., 2004. Fractal singular-value (eigen-value) decomposition method for geophysical and geochemical anomaly reconstruction. Earth Science – China Univ. Geosci. 29, 109–118 (in Chinese with English abstract). Li, C., Ma, T., Shi, J., 2003. Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background. Journal of Geochemical Exploration 77, 167–175. Lima, A., De Vivo, B., Cicchella, D., Cortini, M., Albanese, S., 2003. Multifractal IDW interpolation and fractal filtering method in environmental studies: an application on regional stream sediments of (Italy), Campania region. Applied Geochemistry 18, 1853–1865. Mahdavi, M.A., Amini Fazl, A., 1988. Geological Map of Iran 1:100,000 Series. Geological Survey of Iran, Ahar. Mandelbrot, B.B., 1977. Fractals: Form, Chance, and Dimension. Freeman, San Francisco, 365. Mandelbrot, B.B., Passoja, D.E., Paullay, A.J., 1984. Fractal character of fracture surfaces of metals. Nature 308 (5961), 721–722. Mohajjel, M., Fergusson, C.L., 2000. Dextral transpression in Late Cretaceous continental collision, Sanandaj-Sirjan zone, western Iran. Journal of Structural Geology 22, 1125–1139. Nabavi, M., 1976. An Introduction to the Geology of Iran. Geological Survey of Iran Publication, 109 pp, (in Persian). Naseem, S., Sheikh, S.A., Qadeeruddin, M., Shirin, K., 2002. Geochemical stream sediment survey in Winder Valley, Balochistan, Pakistan. Journal of Geochemical Exploration 76, 1–12. Nude, P.M., Arshin, E., 2009. Overbank sediments as appropriate geochemical sample media in regional stream sediment surveys for gold exploration in the savannah regions of northern Ghana. Journal of Geochemical Exploration 103, 50–56. Ranasinghe, P.N., Fernando, G.W.A.R., Dissanayake, C.B., Rupasinghe, M.S., 2008. Stream sediment geochemistry of the Upper Mahaweli River Basin of Sri Lanka – geological and environmental significance. Journal of Geochemical Exploration, 28. Ranasinghe, P.N., Fernando, G.W.A.R., Dissanayake, C.B., Rupasinghe, M.S., Witter, D.L., 2009. Statistical evaluation of stream sediment geochemistry in interpreting the river catchment of high-grade metamorphic terrains. Journal of Geochemical Exploration 103, 97–114. Rantitsch, G., 2000. Application of fuzzy clusters to quantify lithological background concentrations in stream-sediment geochemistry. Journal of Geochemical Exploration 71, 73–82. Salminen, R., Tarvainen, T., Demetriades, A., Duris, M., Fordyce, F.M., Gregorauskiene, V., Kahelin, H., Kivisilla, J., Klaver, G., Klein, H., Larson, J.O., Lis, J., Locutura, J., Marsina, K., Mjartanova, H., Mouvet, C., O’Connor, P., Odor, L., Ottonello, G., Paukola, T., Plant, J.A., Reimann, C., Schermann, O., Siewers, U., Steenfelt, A., Van Der Sluys, J., De Vivo, B., Williams, L., 1998. FOREGS Geochemical Mapping Field Manual. Guide 47. Geological Survey of Finland, Espoo, 36 pp. Sun, X., Deng, J., Gong, Q., Wang, Q., Yang, L., Zhao, Z., 2009. Kohonen neural network and factor analysis based approach to geochemical data pattern recognition. Journal of Geochemical Exploration 103, 6–16. Zuo, R., Cheng, Q., Agterberg, F.P., Xia, Q., 2009. Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China. Journal of Geochemical Exploration 101, 225–235.
Contents lists available at SciVerse ScienceDirect
Chemie der Erde journal homepage: www.elsevier.de/chemer
Application of multifractal modeling technique in systematic geochemical stream sediment survey to identify copper anomalies: A case study from Ahar, Azarbaijan, Northwest Iran Kaveh Pazand a,∗ , Ardeshir Hezarkhani b , Mohammad Ataei c , Yousef Ghanbari a a
Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Ponak Ave, Tehran, Iran Department of Mining, Metallurgy and Petroleum Engineering, Amirkabir University, Hafez Ave. No. 424, Tehran, Iran c Department of Mining, Geophysics and Petroleum Engineering, Shahrood University of Technology, 7th tir Sq., PO Box 36155-316, Shahrood, Iran b
a r t i c l e
i n f o
Article history: Received 26 October 2010 Accepted 16 August 2011 Keywords: Geochemical anomalies Background Thresholds Fractal
a b s t r a c t This research is based on the application of stream sediments to mineral exploration. Identifying the geochemical anomalies from background is a fundamental task in exploration geochemistry. This paper applied the element concentration–area (C–A) model, to separating the geochemical anomalies from background based on a fractal approach and for the compilation of geochemical mapping from stream sediment samples (n = 620) of the Ahar region (Iran), where some Cu mineralization occurs. Comparisons of the known copper occurrences against the anomalous area created using thresholds from C–A method illustrate these hits. All of known Cu mineralizations and moreover defines two extra Cu anomaly districts. Additional sampling (n = 186) around new Cu anomaly confirms this anomaly within the district. © 2011 Elsevier GmbH. All rights reserved.
1. Introduction Regional sampling of stream sediments has been widely used in geochemical prospecting aimed at mineral exploration. The chemical composition of stream-sediments provides information on the lithological composition of the drainage basin and on the presence of contaminants and mineral deposits (Rantitsch, 2000). Separation of anomalies from background values is crucial in exploration geochemistry. In the past recent years, geochemical anomalies have been identified by means of various methods and several methods have been proposed in the literature to account for it properly (Bakac et al., 1999; Cocker, 1999; Harris et al., 1999, 2000; Rantitsch, 2000; Chandrajith et al., 2001; Naseem et al., 2002; Changjiang et al., 2003; Albanese et al., 2007; Ji et al., 2007; Ranasinghe et al., 2008, 2009; Nude and Arshin, 2009; Grunsky et al., 2009; Sun et al., 2009; Zuo et al., 2009). Since Mandelbrot’s invention of the concept of fractals more than two decades ago (Mandelbrot, 1977; Mandelbrot et al., 1984), fractal and multifractal models have been applied to physical and chemical quantities with geometrical support. In the geological sciences, these approaches have been used to describe the irregularity of geological features and the spatial distribution patterns of geological objects (Zuo et al., 2009) and to characterize properties of
∗ Corresponding author. Tel.: +98 91 24055244; fax: +98 021 44603072. E-mail addresses: [email protected], [email protected] (K. Pazand). 0009-2819/$ – see front matter © 2011 Elsevier GmbH. All rights reserved. doi:10.1016/j.chemer.2011.08.003
mineralization and mineral deposits. Fractal and multifractal models have also been applied to separate anomalies from background values. Examples include the concentration–area model (C–A) (Cheng, 1995), the spectrum–area model (S–A) (Cheng, 2000), the multifractal singular value decomposition (MSVD) (Li and Cheng, 2004), the concentration–distance (C–D) model (Li et al., 2003), the mapping singularity technique (Cheng, 2007, 2008) and many other applications. These methods are gradually being adopted as an effective and efficient means to analyze spatial structures in metallic geochemical systems. In this paper, after a brief discussion of the “concentration–area” method, the application of multifractal modeling in a systematic geochemical stream sediment survey for identifying areas potentially favorable for Copper is described. For demonstration purposes, the Ahar district, northwest Iran, will be studied as an example. 2. The concentration–area method for geochemical anomaly separation This method serves to illustrate the relationship between the obtained results and the geological, geochemical and mineralogical information. Its most useful features are the easy implementation and the ability to compute quantitative anomalous thresholds. Cheng et al. (1994) proposed an element concentration–area (C–A) model, which may be used to define the geochemical background and anomalies. The model has the general form: A( ≤ )∞−a1 ;
A( ≤ )∞−a2
398
K. Pazand et al. / Chemie der Erde 71 (2011) 397–402
where A() denotes the area with concentration values greater than the contour value ; represents the threshold; and a1 and a2 are characteristic exponents. Using fractal theory, Cheng et al. (1994) derived similar power–law relationships and equations in extended form. The two approaches which were used to calculate A() by Cheng et al. (1994) were (1) the A() is the area enclosed by contour level q on a geochemical contour map resulting from interpolation of the original data using a weighted moving average method, and (2) A() are the values obtained by box-counting of original elemental concentration values. By box-counting, one superimposes grid with cells on the study region. The area A() for a given q is equal to the number of cells multiplied by cell area with concentration values greater than . Average concentration values are used for those boxes containing more than one sample. Area–concentration [A()] with element concentrations greater than usually show a power–law relation (Cheng et al., 1994). The breaks between straight-line segments on this plot and the corresponding values of have been used as cut-offs to separate geochemical values into different components, representing
different causal factors, such as lithological differences and geochemical processes. Factors such as mineralizing events, surface geochemical element concentrations, and surface weathering are of considerable importance (Lima et al., 2003). Multifractal theory may be interpreted as a theoretical framework that explains the power–law relations between areas enclosing concentrations below a given value and the actual concentrations. To demonstrate and prove that data distribution has a multifractal nature requires a rather extensive computation. This method has several limitation and accuracy problems, especially when the boundary effects on irregular geometrical data sets are involved (Agterberg et al., 1996; Li et al., 2003). The C–A method seems to be equally well applicable to all cases, which is probably due to the fact that geochemical distributions mostly satisfy the properties of a multifractal function. There is some idea that geochemical distributions are fractal in nature and behavior, at least empirically, according to Bolviken et al. (1992). Some approaches seem to support the idea that geochemical data distributions are multifractal, although this point is far from being proven (Afzal et al., 2010; Li et al., 2003). This idea
Fig. 1. Major structural zones of Iran (after Nabavi, 1976) and the location of the Ahar area in these zones and a modified and simplified geologic map of it (after Mahdavi and Amini Fazl, 1988).
K. Pazand et al. / Chemie der Erde 71 (2011) 397–402
399
Fig. 2. Sampling locations of stream sediments around Ahar region (for lithological units see caption of Fig. 1).
may provide and help the development of an alternative interpretation validation and useful methods to be applied to elemental geochemical distributions analysis. 3. Geological setting Continental collision between the Afro-Arabian continent and the Iranian microcontinent during closure of the Tethys ocean in the Late Cretaceous resulted in the development of a volcanic arc in NW Iran (Mohajjel and Fergusson, 2000; Babaie et al., 2001; Karimzadeh Somarin, 2005). In Iran, all known copper mineralization occurs in the Cenozoic Urumieh-Dokhtar orogenic belt (Fig. 1). This belt was formed by subduction of the Arabian plate beneath central Iran during the Alpine orogeny and hosts major porphyry and skarn deposits. (Hezarkhani and Williams-Jones, 1998; Karimzadeh Somarin and Moayyed, 2002). The Ahar sheet is located in E Tabriz, East Azarbaijan province, NW Iran in the northern part of the Urumieh-Dokhtar magmatic arc (Fig. 1) and covers an area of about 2500 km2 . The composition of volcanic rocks in this area varies from calcalkaline to alkaline during Eocene to Quaternary. Regionally, the oldest country rocks are Cretaceous sedimentary and subvolcanic rocks include conglomerate, marl, shale, andesite, tuff and pyroclastic rock, followed by Eocene latite and ignimbrite. The Oligocene–Miocene intrusive rocks include granodiorite, diorite, gabbro and alkali syenite (Mahdavi and Amini Fazl, 1988). The youngest rocks of the region are Quaternary Volcanic (Fig. 1).
4. Sampling and analysis From 2003 to 2004, 620 stream sediment samples of the −80 mesh (0.18 mm) fraction were collected from the study area with a density of one sample per 2.5–3 km2 for outcrop area and 7–10 km2 for desert area (Fig. 2). Samples were collected from the centre of the streams, avoiding, wherever possible, the collection of organic matter. Each sample represents composite material taken from five points over a stream stretch of 200–500 m. The sample collection protocols are described in detail by Salminen et al. (1998). Analyses were carried out by Geological Survey of Iran Laboratories. Atomic absorption spectrometry (AAS) was used for (Cu, Mo). Statistical results show that Cu and Mo mean values are 66.94 and 10.68 ppm, respectively presented in Table 1. Their distributions are as shown in Fig. 3 and are nearly normal.
Table 1 Statistical parameters of Cu and Mo in stream sediments.
Mean Median Std. deviation Variance Skewness Kurtosis Minimum Maximum
Cu (ppm)
Mo (ppm)
66.94 60.34 36.05 1299.45 4.53 34.25 22.50 420.00
10.68 2.65 135.46 18348.31 24.30 599.52 0.30 3350.00
400
K. Pazand et al. / Chemie der Erde 71 (2011) 397–402
Fig. 3. Cu and Mo histograms.
Variation between maximum and minimum for these data shows a wide range. Concentration–area relations were computed by assigning an area of influence to each sampled point and summing all elemental areas whose concentration lies below a given value. This procedure was repeated for different elemental concentrations. The evaluated grades in cells were sorted out based on decreasing grades and cumulative areas were calculated for grades. Finally, log–log plots were constructed for Cu and Mo (Fig. 4). Geochemical populations are delineated in these plots of Cu and Mo. On the basis of this procedure, there are 4 populations for Cu and Mo respectively as shown in Fig. 4. Anomalous Cu threshold is 44 ppm and its high intensity anomaly is 277 ppm. Also, it is clear that there are two stages of Cu enrichments based on log–log plot as depicted in Fig. 4. The first event for Cu C–A variations occurred at grades below 224 ppm. The second event shows up between grades 224 ppm and 156 ppm. The final event included major Cu mineralization which occurred and was interpreted in grades higher than 277 ppm. Mo threshold and high intensity anomalies are 7.4 ppm, and 262 ppm (Fig. 4). If elements with non-uniform behavior area plotted on log–log coordinates, the plot will have different slopes and various straight-line segments which area connects at an angle or with breaks on the plot. Breaks between the straight-line segments and the corresponding values of Cu and Mo have been used as cut-offs to reclassify distribution maps and are presented in Fig. 5. Based on these results, elemental grade distribution maps were drawn (Fig. 5). Clearly most Cu anomalies are located in six districts of the area, especially high intensity Cu anomalies are located in three parts (Fig. 5) of them (Fig. 5). Mo anomalies are situated in North and Western parts. High intensity Mo anomalies are located in the Western part of area. Based on these maps, potential co-presence of these elements is located in two parts of Mo map as shown in Fig. 5. 5. Comparison with known occurrences Comparisons of the known Copper occurrences against the anomalous area created using thresholds from C–A method (see
Fig. 4. Log–log plots (C–A method) for Cu and Mo. The vertical axis represents cumulative cell areas A(), with elemental concentration values greater than , and the horizontal axis is the actual value ().
Fig. 5) show that these hit all of four known occurrences districts and moreover define two additional Cu anomalies districts. There are no known deposits and occurrences for Mo in the area but Mo anomalies coincidence with the Cu anomalies. To proof and
Table 2 Statistical parameter of Cu in second stage sampling.
Cu (ppm)
N
Mean
Median
Std. deviation
Variance
Minimum
Maximum
186
106.96
90.50
96.39
9290.50
14
961
K. Pazand et al. / Chemie der Erde 71 (2011) 397–402
401
Fig. 5. Cu and Mo geochemical population distribution maps based on C–A method and the comparison between the distribution map of Cu and the known copper occurrences from Ahar sheet.
Fig. 6. Stream sediments sample locations of anomaly district 1 in step 2 and Cu distribution of them.
402
K. Pazand et al. / Chemie der Erde 71 (2011) 397–402
identity the exact anomaly identified from stream sediment in a second stage, 186 additional samples were taken around the anomalous sites of districts; results confirm an anomaly within the district (Fig. 6). Statistical results show that Cu mean value is 106.96 ppm and maximum value is 961 ppm, presented in Table 2. 6. Conclusions In this study, it has been suggested that by using stream sediment exploration technique, it is possible to identify regional metalliferous regimes, which are useful in defining areas for Cu exploration. Distribution pattern of Cu and Mo in sediments are useful to indicate areas where new mineral deposits may exist. Study on the Ahar sheet area shows the potential use of the C–A method for geochemical anomaly separation as a useful tool for geochemical and mineral exploration. The advantages of this method are essentially its simplicity, and easy computational implementation, as well as the possibility to compute a numerical value of concentrations, i.e., the anomalous threshold, which is the most useful criteria for cross examination of information with numerical data from different sources. In the C–A procedure, original element concentration data can be treated directly, and therefore it is unnecessary to process the data with pretreatment of any smoothing procedure, thus enhancing recognition of a geochemical anomaly from background. There is a very good correlation between the calculated anomalous threshold and anomaly values and known copper occurrences in the Ahar area. References Afzal, P., Khakzad, A., Moarefvand, P., Rashidnejad, N., Esfandiari, B., Alghalandis, Y.F., 2010. Geochemical anomaly separation by multifractal modeling in Kahang (Gor Gor) porphyry system, Central Iran. Journal of Geochemical Exploration 104, 34–46. Agterberg, F.P., Cheng, Q., Brown, A., Good, D., 1996. Multifractal modeling of fractures in the Lac du Bonnet batholith, Manitoba. Computers and Geosciences 22 (5), 497–507. Albanese, S., De Vivo, B., Lima, A., Cicchella, D., 2007. Geochemical background and baseline values of toxic elements in stream sediments of Campania region (Italy). Journal of Geochemical Exploration 93, 21–34. Babaie, H.A., Ghazi, A.M., Babaei, A., La Tour, T.E., Hassanipak, A.A., 2001. Geochemistry of arc volcanic rocks of the Zagros crush zone, Neyriz, Iran. Journal of Asian Earth Science 19, 61–76. Bakac, M., Kumru, M.N., Bassari, A., 1999. R-mode factor analysis applied to the exploration of radioactivity in the Gediz River. Journal of Radioanalytical and Nuclear Chemistry 242, 457–465. Bolviken, B., Stokke, P.R., Feder, J., Jossang, T., 1992. The fractal nature of geochemical landscapes. Journal of Geochemical Exploration 43, 91–109. Chandrajith, R., Dissanayake, C.B., Tobschall, H.J., 2001. Application of multi-element relationships in stream sediments to mineral exploration: a case study of Walawe Ganga Basin, Sri Lanka. Applied Geochemistry 16, 339–350. Changjiang, Li., Tuhua, M., Junfa, S., 2003. Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background. Journal of Geochemical Exploration. 77, 167–175. Cheng, Q., Agterberg, F.P., Ballantyne, S.B., 1994. The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration 51, 109–130. Cheng, Q., 2007. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geological Reviews 32, 314–324. Cheng, Q., 2008. Non-linear theory and power-law models for information integration and mineral resources quantitative assessments. Mathematical Geology 40, 503–532. Cheng, Q., 1995. The perimeter-area fractal model and its application to geology. Mathematical Geology 27, 69–82. Cheng, Q., 2000. GeoData Analysis System (GeoDAS) for mineral exploration: user’s guide and exercise manual. Material for the Training Workshop
on GeoDAS held at York University, Toronto, Canada, vol. 1, 3, p. 204. http://www.gisworld.org/geodas. Cocker, M.D., 1999. Geochemical mapping in Georgia, USA: a tool for environmental studies, geologic mapping and mineral exploration. Journal of Geochemical Exploration 67, 345–360. Grunsky, E.C, Drew, L.J., Sutphin, D.M., 2009. Process recognition in multielement soil and stream-sediment geochemical data. Applied Geochemistry 24, 1602–1616. Harris, J.R., Grunsky, E.C., Wilkinson, L., 2000. Effective use and interpretation of lithogeochemical data in regional exploration programs. Ore Geological Reviews 16, 107–143. Harris, J.R., Wilkinson, L., Grunsky, G., Heather, K., Ayer, J., 1999. Techniques for analysis and visualization of lithogeochemical data with applications to the Swayze greenstone belt, Ontario. Journal of Geochemical Exploration 67, 301–334. Hezarkhani, A., Williams-Jones, A.E., 1998. Controls of alteration and mineralization in the Sungun porphyry copper deposit. Iran: evidence from fluid inclusions and stable isotopes. Economic Geology 93, 651–670. Ji, H., Zeng, D., Shi, Y., Wu, Y., Wu, X., 2007. Semi-hierarchical correspondence cluster analysis and regional geochemical pattern recognition. Journal of Geochemical Exploration 93, 109–119. Karimzadeh Somarin, A., 2005. Petrology and geochemistry of Early Tertiary volcanism of the Mendejin area. Iran, and implications for magma genesis and tectonomagmatic setting. Geodinamica Acta 18, 343–362. Karimzadeh Somarin, A., Moayyed, M., 2002. Granite- and gabbrodiorite-associated skarn deposits of NW Iran. Ore Geological Reviews 20, 127–138. Li, Q., Cheng, Q., 2004. Fractal singular-value (eigen-value) decomposition method for geophysical and geochemical anomaly reconstruction. Earth Science – China Univ. Geosci. 29, 109–118 (in Chinese with English abstract). Li, C., Ma, T., Shi, J., 2003. Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background. Journal of Geochemical Exploration 77, 167–175. Lima, A., De Vivo, B., Cicchella, D., Cortini, M., Albanese, S., 2003. Multifractal IDW interpolation and fractal filtering method in environmental studies: an application on regional stream sediments of (Italy), Campania region. Applied Geochemistry 18, 1853–1865. Mahdavi, M.A., Amini Fazl, A., 1988. Geological Map of Iran 1:100,000 Series. Geological Survey of Iran, Ahar. Mandelbrot, B.B., 1977. Fractals: Form, Chance, and Dimension. Freeman, San Francisco, 365. Mandelbrot, B.B., Passoja, D.E., Paullay, A.J., 1984. Fractal character of fracture surfaces of metals. Nature 308 (5961), 721–722. Mohajjel, M., Fergusson, C.L., 2000. Dextral transpression in Late Cretaceous continental collision, Sanandaj-Sirjan zone, western Iran. Journal of Structural Geology 22, 1125–1139. Nabavi, M., 1976. An Introduction to the Geology of Iran. Geological Survey of Iran Publication, 109 pp, (in Persian). Naseem, S., Sheikh, S.A., Qadeeruddin, M., Shirin, K., 2002. Geochemical stream sediment survey in Winder Valley, Balochistan, Pakistan. Journal of Geochemical Exploration 76, 1–12. Nude, P.M., Arshin, E., 2009. Overbank sediments as appropriate geochemical sample media in regional stream sediment surveys for gold exploration in the savannah regions of northern Ghana. Journal of Geochemical Exploration 103, 50–56. Ranasinghe, P.N., Fernando, G.W.A.R., Dissanayake, C.B., Rupasinghe, M.S., 2008. Stream sediment geochemistry of the Upper Mahaweli River Basin of Sri Lanka – geological and environmental significance. Journal of Geochemical Exploration, 28. Ranasinghe, P.N., Fernando, G.W.A.R., Dissanayake, C.B., Rupasinghe, M.S., Witter, D.L., 2009. Statistical evaluation of stream sediment geochemistry in interpreting the river catchment of high-grade metamorphic terrains. Journal of Geochemical Exploration 103, 97–114. Rantitsch, G., 2000. Application of fuzzy clusters to quantify lithological background concentrations in stream-sediment geochemistry. Journal of Geochemical Exploration 71, 73–82. Salminen, R., Tarvainen, T., Demetriades, A., Duris, M., Fordyce, F.M., Gregorauskiene, V., Kahelin, H., Kivisilla, J., Klaver, G., Klein, H., Larson, J.O., Lis, J., Locutura, J., Marsina, K., Mjartanova, H., Mouvet, C., O’Connor, P., Odor, L., Ottonello, G., Paukola, T., Plant, J.A., Reimann, C., Schermann, O., Siewers, U., Steenfelt, A., Van Der Sluys, J., De Vivo, B., Williams, L., 1998. FOREGS Geochemical Mapping Field Manual. Guide 47. Geological Survey of Finland, Espoo, 36 pp. Sun, X., Deng, J., Gong, Q., Wang, Q., Yang, L., Zhao, Z., 2009. Kohonen neural network and factor analysis based approach to geochemical data pattern recognition. Journal of Geochemical Exploration 103, 6–16. Zuo, R., Cheng, Q., Agterberg, F.P., Xia, Q., 2009. Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China. Journal of Geochemical Exploration 101, 225–235.