摘要：Introduction Separation of geochemical anomalies from the background has always been a major concern of exploration geochemistry. The search for methods that can make this analysis quantitative and objective aims not only at the reduction of but also at providing an automatic routine in exploration, assisting the interpretation and production of geochemical maps (Nazarpour et al., 2015). The Malayer-Isfahan metallogenic belt with the north-west-south-east trend is the largest and most important Pb-Zn belt of MVT type in Iran (Rajabi et al., 2012). In this study, separation of Pb and Zn geochemical anomalies was performed using the methods named further in the study area. Materials and methods 1. Classical statistics Various statistical methods have been used to process geochemical data in order to determine threshold values. Statistical quantities, such as the mean, standard deviation (SDEV) and percentiles, have been used to define thresholds for separating anomalies form the background. For example, geochemical anomalies have been defined as values greater than a threshold value defined as the 75th or 85th percentile, and Mean+SDEV or Mean+nSDEV (Nazarpour et al., 2015). The boxplot and median+2MAD techniques of the EDA approach have been widely used to separate geochemical anomalies from the background. In exploratory data analysis (EDA) of geochemical exploration data, the median+2MAD value was originally used to identify extreme values and serve as a threshold for further inspection of large data sets (Carranza, 2009). The MAD (is the median of absolute deviations of individual dataset values (Xi) from the median of all dataset values (Tukey, 1977). 2. Multifractal models Fractal and multifractal models have also been applied to separate anomalies from background values. These methods are gradually being adopted as effective and efficient means to analyze spatial structures in metallic geochemical systems (Cheng et al., 1994). The concentration-number (C-N), concentration-area (C-A) multifractal methods have been used for delineation and description of relations among mineralogical, geochemical and geological features based on surface and subsurface data (Nazarpour et al., 2015). Fractal/multi-fractal models consist of frequency distribution and spatial self-similar or self-affine characteristics of geochemical variables. These fractal/multifractal models have been demonstrated to be effective tools for decomposing geological complexes and mixed geochemical populations and to recognize weak geochemical anomalies hidden within strong geochemical background (Cheng et al., 1994). 3. Singularity Index (SI) The Singularity technique is another important process for fractal/multifractal modeling of geochemical data (Zuo et al., 2015). This technique is defined as the characterization of the anomalous behavior of singular physical processes that often result in anomalous amounts of energy release or material accumulation within a narrow spatial–temporal interval. The Singularity can be estimated from observed element concentration within small neighborhoods based on the following equation (Cheng, 2007): (1) The Singularity Index is a powerful tool to identify weak anomalies, but it is influenced by the selection of the window size. (Zuo et al., 2015). Results and Discussion In this study, a total of 19946 stream sediment geochemical samples were analyzed using the ICP-MS and XRF methods. In the maps derived from the Singularity Index (SI) the higher accuracy of this method compared to other applied methods was employed. Therefore, the hidden and weak anomalies are better represented, and a better overlap with limestone as the major host rock of Pb and Zn deposits (MVT type) in the study area were observed. A comparison among all of the applied methods indicates that the concentration of Pb and Zn increased toward the and south east and northwest parts, respectively. In these regions there is a high potential for the occurrence of promising mining areas. Moreover, the obtained Pb and Zn anomalies have a good correlation with the exposure of limestone in the study area. References Carranza, E.J.M., 2009. Mapping of anomalies in continuous and discrete fields of stream sediment geochemical landscapes. Geochemistry: Exploration, Environment, Analysis 10: 171–187. Cheng, Q., Agterberg. F.P. and Ballantyne. S.B., 1994. The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration, 51(2): 109–130. Cheng, Q., 2007. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geology Reviews, 32: 314–324. Nazarpour, A., Sadeghi, and B. and Sadeghi, M., 2015. Application of fractal models to characterization and evaluation of vertical distribution of geochemical data in Zarshuran gold deposit, NW Iran. Journal of Geochemical Exploration, 148: 60–70. Rajabi, A., Rastad, E. and Canet, C., 2012. Metallogeny of Cretaceous carbonate-hosted Zn–Pb deposits of Iran: geotectonic setting and data integration for future mineral exploration. International Geology Review, 54(14): 1649–1672. Tukey, J.W., 1977. Exploratory Data Analysis. Addison-Wesley, Reading, 688 pp. Zuo, R., Wang, J., Chen, G. and Yang, M., 2015. Identification of weak anomalies: A multifractal perspective. Journal of Geochemical Exploration, 148: 12–24.
