Application of multifractal models to identify geochemical anomalies in Zarshuran Au deposit, NW Iran
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ORIGINAL PAPER
Application of multifractal models to identify geochemical anomalies in Zarshuran Au deposit, NW Iran Ahad Nazarpour & Nematolah Rashidnejad Omran & Ghodratolah Rostami Paydar
Received: 28 June 2013 / Accepted: 4 November 2013 # Saudi Society for Geosciences 2013
Abstract The identification and separation of soil geochemical anomalies using the concentration-number (C-N) and concentration-area (C-A) methods was conducted at the Zarshuran Carlin-type Au deposit, NW Iran. Log-log elemental plots fitted with straight lines show C-N and C-A relationships of Au, As, Sb and Cu. The thresholds obtained from the two methods are similar. Element concentrations can be divided into three segments that correlate with a particular rock type including mafic rocks, serpentine schist (within the Iman Khan Anticline), Ghaldagh limestone and Zarshuran shale units. Various structural features and corresponding alteration show that geologic structures play an important role in the discrimination of geochemical anomalies and element distribution in soils. Keywords Fractal . Concentration-area method . Number -size method . Zarshuran . Iran
Introduction One of the most fundamental tasks in soil geochemistry data processing is determining thresholds to separate geochemical anomalies from background values to help delineate potential mineralization (Jian et al. 2010; Hassanpour and Afzal 2011; Cheng et al. 1997; Zuo 2011a, b; Cheng 2012; Carranza 2008). A. Nazarpour (*) Department of Geology, North Tehran Branch, Islamic Azad University, Tehran, Iran e-mail: [email protected] N. R. Omran Department of Geology, Tarbiat Modares University, Tehran, Iran G. R. Paydar Department of Geology, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
Traditionally, processing methods such as mean, percentage, standard deviation, box plot and fence have been used (e.g., Gałuszka 2007; Hawkes and Webb 1962; Schwertman and Silva 2007; Schwertman et al. 2004; Afzal et al. 2010). Other techniques, including probability plots and the analysis of variance, have also been widely used to determine threshold values separating background populations from anomalies (e.g., Cheng and Agterberg 1996; Govett et al. 1975; Miesch 1981; Sinclair 1974, 1976, 1991; Stanley 1988; Stanley and Sinclair 1989; Carranza 2008; Zuo 2011a, b; Cheng 2012). The traditional methods generally do not consider the spatial properties of the data, such as the geometry, shape, extent of the anomalous areas (e.g., Rafiee 2005) and may fail to recognize anomalies in regions with high background values close to anomalous values, or miss weak anomalies in regions with known mineral deposits (Darabi-Golestan et al. 2012; Hassanpour and Afzal 2011; Jian et al. 2010; Mohammadi et al. 2012). This limitation can be addressed using statistical techniques that also take into consideration the spatial variability of geochemical values, such as geostatistical and fractal processing methods. In this paper we use the fractal approach. Fractal theory is one of the non-linear mathe
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