Correlation between geology and concentration-volume fractal models: significance for Cu and Mo mineralized zones separation in the Kahang porphyry deposit (Central Iran)

Abstract

This study identifies the major mineralized zones including supergene enrichment and hypogene enrichment in the Kahang Cu-Mo porphyry deposit which is located in Central Iran based on subsurface data and utilization of the concentration-volume (C-V) fractal model. Additionally, a correlation between results achieved from a C-V fractal model and geological models consisting of zonation, mineralography and alteration have been conducted in order to have an accurate recognition and modification of the main mineralized zones. Log-log plots indicate five geochemical populations for Cu and Mo in the deposit which means that mineralization commences with 0.075 % and 13 ppm for Cu and Mo (as the first thresholds) respectively. The main mineralization began for Cu 0.42 % and Mo 100 ppm and also enriched mineralization containing Cu 1.8 % and Mo 645 ppm which is located in the central part of the deposit. According to the C-V model, the main Cu-Mo mineralized zones occur in the hypogene zone, especially in the central, NW and NE parts of the Kahang deposit. The supergene enrichment zone derived via the C-V model is smaller than that in the geological model and is located in the central and eastern parts of the deposit. Results analysed by the C-V fractal model certify that the interpreted zones based on the fractal model are accurate. To certify this, a logratio matrix has been employed to validate the C-V fractal model for the Cu and Mo main mineralized zones.

Multi-Gaussian kriging: a practice to enhance delineation of mineralized zones by Concentration–Volume fractal model in Dardevey iron ore deposit, SE Iran

“…The C-V fractal model proposed by Afzal et al (2011) is used to delineate the different mineralized zones in order to characterize the (a) distribution of major, minor or paragenesis elemental concentrations in relation to different types of deposits such as Cu porphyry, Pb-Zn with carbonate host rocks, orogenic and epithermal gold, coal seams and iron ores (e.g., Afzal et al, 2013Afzal et al, , 2014Delavar et al, 2012;Sadeghi et al, 2012;Yasrebi et al, 2013). This model is expressed in the following form: where, V(ρ b υ) and V(ρ ≥ υ) indicate volumes (V) with concentration values (ρ) that are, respectively, smaller and greater than contour values (υ), which define those volumes, and a1 and a2 are exponents.…”

Prospecting of Ni mineralization using fractal models based on lithogeochemical data in Patang area, Eastern Iran

“…The recognition of different geochemical anomalies is essential for delineation and description of ore mineralization (Hawkes and Webb 1979;Carranza 2008;Afzal et al 2013;Hashemi and Afzal 2013;Hosseini et al 2014;Yasrebi et al 2013Yasrebi et al , 2014. Prospecting of ore mineralization using lithogeochemical data has significant influence for identification of anomalies to design and consequently suggest a grid regarding detailed explorations.…”

Delineation of Cu prospects utilizing multifractal modeling and stepwise factor analysis in Noubaran 1:100,000 sheet, Center of Iran

“…Determination of threshold values for separating geochemical anomalies from background is important for ore deposit prospecting (Agterberg 1995;Gałuszk 2007;Hawkes and Webb 1962;Sadeghi et al 2012;Shamseddin Meigoony et al 2013;Yasrebi et al 2013aYasrebi et al , 2014. Fractal/multifractal modeling was established by Mandelbrot (1983) as an important nonEuclidian method and appropriate tool for separation of geochemical populations without data pre-processing.…”