Delineation of gold mineralized zones using concentration–volume fractal model in Qolqoleh gold deposit, NW Iran

Afzal, Peyman; Ahari, Hooman Dadashzadeh; Omran, N Rashidnejad; Aliyari, Farhang

doi:10.1016/j.oregeorev.2013.05.005

Cited by 58 publications

(15 citation statements)

References 31 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%

“…Based on this data, the main rock types by depth were modeled for the study region using stratigraphic and lithoblending algorithms in the RockWorks 15 software package. This is a proper algorithm which 'bleeds' mapped lithology types outward from individual boreholes to form a solid model; however, this method results in lithology units extending from each borehole, which can end abruptly when the zones from neighboring boreholes do not match [41]. The 'Triangulation' algorithm was used for 3D modeling of mineralized strata in the Zardu area, which is an estimation environment.…”

Section: Geological Modelingmentioning

confidence: 99%

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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%

Section: Geological Modelingmentioning

confidence: 99%

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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“…Agterberg et al, 1996;Afzal et al, 2010Afzal et al, , 2011Afzal et al, , 2012Afzal et al, , 2013Cheng et al, 1994;Daneshvar et al, 2012;Sim et al, 1999;Turcotte, 1986). Cheng et al (1994) proposed a concentration-area (C-A) fractal model based on the relationship of elemental distributions and occupied areas.…”

Section: 1-2 IVmentioning

confidence: 99%

Site effect classification based on microtremor data analysis using a concentration–area fractal model

Adib

Afzal

Heydarzadeh³

2015

Nonlin. Processes Geophys.

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Abstract. The aim of this study is to classify the site effect using concentration-area (C-A) fractal model in Meybod city, central Iran, based on microtremor data analysis. Loglog plots of the frequency, amplification and vulnerability index (k-g) indicate a multifractal nature for the parameters in the area. The results obtained from the C-A fractal modelling reveal that proper soil types are located around the central city. The results derived via the fractal modelling were utilized to improve the Igarashi (1970, 1971) classification results in the Meybod city. The resulting categories are: (1) hard soil and weak rock with frequency of 6.2 to 8 Hz, (2) stiff soil with frequency of about 4.9 to 6.2 Hz, (3) moderately soft soil with the frequency of 2.4 to 4.9 Hz, and (4) soft soil with the frequency lower than 2.4 Hz.

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Delineation of gold mineralized zones using concentration–volume fractal model in Qolqoleh gold deposit, NW Iran

Cited by 58 publications

References 31 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

Site effect classification based on microtremor data analysis using a concentration–area fractal model

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