Delineation of mineralization zones using concentration–volume fractal method in Pb–Zn carbonate hosted deposits

Delavar, S. T. ghi; Afzal, Peyman; Borg, Gregor; Rasa, Iraj; Lotfi, Mehrdad; Omran, N Rashidnejad

doi:10.1016/j.gexplo.2012.05.003

Cited by 20 publications

(4 citation statements)

References 25 publications

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“…Log-log plots in fractal/multifractal modeling, being consistent with the widely accepted view that fractality always accompanies phase-changes [36], are accurate tools for delineating geological populations or geochemical zones, with breakpoints in those log-log plots representing threshold values [19,21]. The application of fractal models to the detection of mineralized zones is based on relationships between ore grades and occupied volumes or tonnages [19,27,[37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 73%

Identification of mineralized zones in the Zardu area, Kushk SEDEX deposit (Central Iran), based on geological and multifractal modeling

et al. 2016

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Abstract:The aim of this paper is to delineate the different lead-zinc mineralized zones in the Zardu area of the Kushk zinc-lead stratabound SEDEX deposit, Central Iran, through concentration-volume (C-V) modeling of geological and lithogeochemical drillcore data. The geological model demonstrated that the massive sulfide and pyrite+dolomite ore types as main rock types hosting mineralization. The C-V fractal modeling used lead, zinc and iron geochemical data to outline four types of mineralized zones, which were then compared to the mineralized rock types identified in the geological model. 'Enriched' mineralized zones contain lead and zinc values higher than 6.93% and 19.95%, respectively, with iron values lower than 12.02%. Areas where lead and zinc values were higher than 1.58% and 5.88%, respectively, and iron grades lower than 22% are labelled "high-grade" mineralized zones, and these zones are linked to massive sulfide and pyrite+dolomite lithologies of the geological model. Weakly mineralized zones, labelled 'low-grade' in the C-V model have 0-0.63% lead, 0-3.16% zinc and > 30.19% iron, and are correlated to those lithological units labeled as gangue in the geological model, including shales and dolomites, pyritized dolomites. Finally, a log-ratio matrix was employed to validate the results obtained and check correlations between the geological and fractal modeling. Using this method, a high overall accuracy (OA) was confirmed for the correlation between the enriched and highgrade mineralized zones and two lithological units -the massive sulfide and pyrite+dolomite ore types.

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Section: Introductionmentioning

confidence: 73%

Identification of mineralized zones in the Zardu area, Kushk SEDEX deposit (Central Iran), based on geological and multifractal modeling

et al. 2016

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“…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.…”

Section: C-v Fractal Modelmentioning

confidence: 99%

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

Afzal

Madani

Shahbeik

et al. 2015

Journal of Geochemical Exploration

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“…Concentration-volume fractal models has been widely used in porphyry deposit (e.g. Afzal et al, 2011;Yasrebi et al, 2013;Soltani et al, 2014;Sun and Liu, 2014) and lesser another type of deposit such as gold deposit (Afzal et al, 2013;Lin et al, 2014), Zn-Pb MVT deposit (Delavar et al, 2012), iron deposit (Sadeghi et al, 2012;Afzal et al, 2015;Rahmati et al, 2015). Grade distribution of block models can be generated by geostatistical methods such as the Ordinary Kriging, Multi-Gaussian Kriging and Sequential Gaussian simulation.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-Volume fractal model for delineating copper mineralization controllers using geostatistical simulation in Nohkouhi volcanogenic massive sulfide deposit, Central Iran

Hajsadeghi¹,

Asghari

Mirmohammadi

et al. 2018

Bulletin of the Mineral Research and Exploration

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The aim of this study was to delineate copper mineralization controllers in Nohkouhi volcanogenic massive sulfide (VMS) deposit by using geostatistical and fractal simulation. In this study, concentration-volume (C-V) fractal model has been used to indicate various copper populations related to different host rocks and copper minerals. Accordingly, uncertainty-volume (U-V) fractal model was applied to probability values achieved through sequential indicator simulation (SIS). Copper ores of Nohkouhi deposit including chalcopyrite and malachite were simulated in 30 realizations. The U-V fractal model obtained by using a probability map was divided into four probability zones (high, moderate, low, and very low) for copper minerals. Furthermore, copper grades were simulated for 10 times by sequential Gaussian simulation (SGS). Combination of C-V and U-V fractal modeling resulted in a hybrid method which could be properly employed to determinate various mineralization zones based on the relationship between quantitative (e.g. copper grade) and qualitative (e.g. copper minerals) variables. Moreover, integrating the results of C-V and U-V fractal modeling with the most frequent occurrence of rock type modeling helps identify copper mineralization controllers in a VMS deposit.

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Delineation of mineralization zones using concentration–volume fractal method in Pb–Zn carbonate hosted deposits

Cited by 20 publications

References 25 publications

Identification of mineralized zones in the Zardu area, Kushk SEDEX deposit (Central Iran), based on geological and multifractal modeling

Identification of mineralized zones in the Zardu area, Kushk SEDEX deposit (Central Iran), based on geological and multifractal modeling

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

Uncertainty-Volume fractal model for delineating copper mineralization controllers using geostatistical simulation in Nohkouhi volcanogenic massive sulfide deposit, Central Iran

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