Geochemical anomaly separation by multifractal modeling in Kahang (Gor Gor) porphyry system, Central Iran

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

doi:10.1016/j.gexplo.2009.11.003

Cited by 173 publications

(69 citation statements)

References 16 publications

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“…All elemental log-log plots for this area show four distinct geochemical populations based on sudden changes in the rate of volume decrease, as depicted in Fig. 8 and Table 1; in the "enriched" population, the slope of the segments is close to 90 ∘ [16,67]. Using the log-log plots, the furthermost element populations are interpreted as "enriched" zinc-lead zones, having lead and zinc concentrations higher than 19.95% and 6.3%, respectively.…”

Section: C-v Fractal Modelingmentioning

confidence: 86%

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: C-v Fractal Modelingmentioning

confidence: 86%

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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“…Mineralisation within intrusives bodies and their surrounding host rocks consists of chalcocite, chalcopyrite, pyrite, malachite, magnetite, limonite jarosite, goethite and chalcantite in quartz stockworks and advanced argillic alteration. The eastern part of the deposit is covered by phyllic and quartz-sericite alteration (Afzal et al, 2010;Rashidnejad Omran et al, 2011).…”

Section: Geological Setting Of the Kahang Cu-mo Porphyry Depositmentioning

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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“…Stream sediment and lithogeochemical studies are essential for prospecting of different ore deposits (Hawkes & Webb, 1979). Several methods are used for geochemical data interpretation and modelling such as classical statistics (e.g., Tukey, 1977;Hawkes & Webb, 1979;Reimann et al, 2005), fractal and multifractal modelling (Cheng et al, 1994;Agterberg et al, 1996;Cheng, 1999;Li et al, 2003;Zuo et al, 2009;Afzal et al, 2010a;Afzal et al, 2010b) and singularity modeling (Cheng, 2007). Fractal theory has been established by Mandelbrot (1983) as an important non-Euclidean branch in geometry.…”

Section: Introductionmentioning

confidence: 99%

“…Fractal theory has been established by Mandelbrot (1983) as an important non-Euclidean branch in geometry. Several methods have been proposed and developed based on fractal geometry for application in geosciences since the 1980s (Agterberg et al, 1993;Sanderson et al, 1994;Cheng, 1999;Turcotte, 1997Turcotte, , 2002Goncalves et al, 2001;Monecke et al, 2005;Gumiel et al, 2010;Afzal et al, 2010b;Zuo, 2011;Sadeghi et al, 2012). The present study is based on the integration of remote sensing techniques and geochemical data analysis (stream sediment and litho geochemical samples) and as well as geological field verification studies to identify Au, Ag and As prospects in the Chartagh, north-west iran.…”

Section: Introductionmentioning

confidence: 99%

Introducing Au Potential Areas, Using Remote Sensing and Geochemical Data Processing Using Fractal Methods in Chartagh, Western Azerbijan – Iran

Mansouri¹,

Feizi²

2016

Archives of Mining Sciences

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The studied area -Chartagh -is located in the East of Azerbaijan gharbi Province, Iran. In this paper, geology map, ASTER satellite images were used and after processing these images with ENVI softwares, geochemical data analysis consisting of lithogeochemical samples, within geological field observations. On ASTER data; using a number of selected methods including band ratio, Minimum Noise Fraction (MNF) and Spectral Angle Maper (SAM) distinguished alternation zones. Geochemical anomalies were separated by number -size (N-S) fractal method. (N-S) fractal method was utilized for High intensive Au, As and Ag anomalies.

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Geochemical anomaly separation by multifractal modeling in Kahang (Gor Gor) porphyry system, Central Iran

Cited by 173 publications

References 16 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

Introducing Au Potential Areas, Using Remote Sensing and Geochemical Data Processing Using Fractal Methods in Chartagh, Western Azerbijan – Iran

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