Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling

Afzal, Peyman; Alghalandis, Younes Fadakar; Khakzad, A; Moarefvand, Parviz; Omran, N Rashidnejad

doi:10.1016/j.gexplo.2011.03.005

Cited by 262 publications

(105 citation statements)

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“…Various geochemical and mineralization processes can be defined due to differences in fractal dimensions, based on analysis of relevant geochemical data. 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: 68%

“…Afzal et al [19] proposed use of the C-V fractal model to delineate different levels of mineralization in different ore deposit types. On the C-V log-log plot, it can be observed that where the slope of the curve changes, this represents an intensive change in the geochemical population, which is in turn affected by changes in geological and mineralization characteristics.…”

Section: C-v Fractal Modelmentioning

confidence: 99%

“…Since that time, several fractal models have been developed and applied to geochemical exploration; these models work by separating geochemical populations into component mineralized zone and phase populations. Examples of fractal models include the number-size (N-S) model proposed by Mandelbrot [14], the concentration-area (C-A) model of Cheng et al [16], the concentration-perimeter (C-P) model of Cheng et al [17], the concentration-distance (C-D) model proposed by Li et al [18], and the concentrationvolume (C-V) model of Afzal et al [19]. Fractal/multifractal modeling assists in identifying relationships between geological, geochemical, and mineralogical settings and linking them to spatial information obtained from mineral deposits [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

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: 68%

Section: C-v Fractal Modelmentioning

confidence: 99%

Section: Introductionmentioning

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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“…The RQD-V fractal model which is developed based on Concentration-Volume (C-V) fractal model by Afzal et al (2011) for separation of rock populations based on RQD as an important parameter for the rock mass characterisation, can be expressed as:…”

Section: Rqd-volume (Rqd-v) Fractal Modelmentioning

confidence: 99%

APPLICATION OF RQD-NUMBER AND RQD-VOLUME MULTIFRACTAL MODELLING TO DELINEATE ROCK MASS CHARACTERISATION IN KAHANG Cu-Mo PORPHYRY DEPOSIT, CENTRAL IRAN / ZASTOSOWANIE METOD MODELOWANIA NUMERYCZNEGO ORAZ MODELOWANIA FRAKTALNEGO DO ANALIZY JAKOŚCI SKAŁ W CELU OKREŚLENIA CHARAKTERYSTYKI GÓROTWORU W OBSZARZE ZŁOŻA Cu-Mo W KAHANG, ŚRODKOWY IRAN

Yasrebi¹,

Wetherelt²,

Foster³

et al. 2013

Archives of Mining Sciences

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Identification of rock mass properties in terms of Rock Quality Designation (RQD) plays a significant role in mine planning and design. This study aims to separate the rock mass characterisation based on RQD data analysed from 48 boreholes in Kahang Cu-Mo porphyry deposit situated in the central Iran utilising RQD-Volume (RQD-V) and RQD-Number (RQD-N) fractal models. The log-log plots for RQD-V and RQD-N models show four rock mass populations defined by RQD thresholds of 3.55, 25.12 and 89.12% and 10.47, 41.68 and 83.17% respectively which represent very poor, poor, good and excellent rocks based on Deere and Miller rock classification. The RQD-V and RQD-N models indicate that the excellent rocks are situated in the NW and central parts of this deposit however, the good rocks are located in the most parts of the deposit. The results of validation of the fractal models with the RQD block model show that the RQD-N fractal model of excellent rock quality is better than the RQD-V fractal model of the same rock quality. Correlation between results of the fractal and the geological models illustrates that the excellent rocks are associated with porphyric quartz diorite (PQD) units. The results reveal that there is a multifractal nature in rock characterisation with respect to RQD for the Kahang deposit. The proposed fractal model can be intended for the better understanding of the rock quality for purpose of determination of the final pit slope. . Wykresy logarytmiczne wykonane dla modeli RQD-V i RQD-N wykazują istnienie czterech populacji warstw górotworu, określonych na podstawie parametrów progowych: 3.55; 25.12; 89.12% oraz 10.47; 41.68 i 83.17%, odpowiadającym kolejno stopniom jakości: bardzo słaby, słaby, dobry i bardzo dobry, zgodnie z klasyfikacją skał Deere i Millera. Wyniki uzyskane przy zastosowaniu modeli RQD-V i RQD-N wskazują, że najlepsze skały zalegają w północno-zachodniej i centralnej części złoża, z kolei dobrej jakości skały znaleźć można w obrębie całego złoża. Walidacja modeli fraktalnych w oparciu o model blokowy (RQD block model) wskazuje, że model RQD-N dla bardzo dobrej jakości skał jest skuteczniejszy niż model RQD-V dla tej samej jakości skał. Wysoki stopień korelacji pomiędzy wynikami uzyskanymi w oparciu o modele fraktalne i geologiczne pokazuje, że najwyższej jakości skały związane są z obecnością porfirowego diorytu kwarcowego. Badanie wykazuje fraktalną naturę charakterystyki jakości skał w złożu Kahang. Zaproponowany model fraktalny wykorzystać można do lepszego poznania zagadnienia jakości skał w celu obliczenia nachylenia wyrobiska.Słowa kluczowe: określenie właściwości górotworu, modele fraktalne RQD-V i RQD-N, złoże porfiru Cu-MO w Kahang, porfirowy dioryt kwarcowy, środkowy Iran

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“…Fractal methods are intended for different branches of geophysical exploration, such as separation of geophysical anomalies from background, spatial distribution of earthquakes, geomagnetic polarity and signal analysis (Turcotte, 1997;Malamud and Turcotte, 1999;Dimri, 2000, Dimri, 2005Shen et al, 2009). Fractal methods also serve to depict relationships of geophysical, geological and geochemical settings with spatial information derived from analysis of mineral deposit occurrence data (Turcotte, 1997;Goncalves et al, 2001;Dimri, 2005;Carranza, 2009;Zia Zarifi et al, 2010;Afzal et al, 2011). Fractal dimensions in geological, geochemical and geophysical processes correspond to variations in physical attributes such as mineralogy, vein and veinlets density or orientation, fluid phases, alteration zones, structural feature and so on (Turcotte, 1997;Sim et al, 1999;Carranza, 2009;Afzal et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Application of concentration-volume fractal method in induced polarization and resistivity data interpretation for Cu-Mo porphyry deposits exploration, case study: Nowchun Cu-Mo deposit, SE Iran

Saein

Rasa

Omran

et al. 2012

Nonlin. Processes Geophys.

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Abstract. The aim of this study is the utilization of the concentration-volume (C-V ) fractal method based on geoelectrical data including induced polarization (IP) and resistivity (RS) in targeting areas hosting different sulfidic mineralization zones in Nowchun Cu-Mo porphyry deposit, SE Iran. The C-V fractal model employed in this research in order to separate high and moderate sulfidic zones from low sulfidic zone and barren wall rocks in the deposit is corresponding to chargeability and resistivity. Results obtained from the C-V method indicate that there is a positive correlation between subsurface mineralization and sulfide mineralized zones; additionally, use of the C-V method based on geophysical data is recognized as an accurate approach for delineation of various mineralization zones in the depth for optimization of mineral exploration operation, particularly in porphyry deposits.

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Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling

Cited by 262 publications

References 33 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

Application of concentration-volume fractal method in induced polarization and resistivity data interpretation for Cu-Mo porphyry deposits exploration, case study: Nowchun Cu-Mo deposit, SE Iran

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