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Manuscript ID : IJES-1801-1165 (R1) Visit : 149 Page: 56 - 67

10.30495/ijes.2019.544596

Article Type: Original Research

Resources classification using fractal modelling in Eastern Kahang Cu-Mo porphyry deposit, Central Iran

Subject Areas : Mineralogy

Amir Bijan Yasrebi 1 , Ardashir Hezarkhani 2

1 - Computational Geomechanics Group, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Streatham Campus, Exeter, UK
2 - Department of Mining and Metallurgical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

Received: 2018-02-04 Accepted : 2018-10-04 Published : 2019-01-01

Keywords: Resources classification, Concentration-Volume (C-V) fractal model, Estimated variance, Eastern Kahang Cu-Mo porphyry deposit,

Abstract :

Resources/reserves classification is crucial for block model creation utilised in mine planning and feasibility study. Selection of estimation methods is an essential part of mineral exploration and mining activities. In other word, resources classification is an issue for mining companies, investors, financial institutions and authorities, but it remains subject to some confusion. The aim of this paper is to determine a resources classification for a Cu block model generated by an Ordinary Kriging (OK) and a Concentration-Volume (C-V) fractal modelling based on estimated variance in Eastern Kahang Cu-Mo porphyry deposit, Central Iran. Variography, block modelling and cell declustering for dataset with respect to Cu concentrations as the main target in this deposit were conducted firstly. Then, Cu distribution model was carried out by the OK and estimated variances were calculated for all voxels. According to a C-V log-log plot, three populations for estimated variances were detected. ‘’Measured’’ resources contain voxels with estimated variances lower than 0.08 and more than 7 samples. Estimated variances varied between 0.08 and 0.24 in which more than 3 samples were engaged for estimation of ‘’Indicated’’ resources. ‘’Inferred’’ resources include estimated variances over 0.24 which are located in marginal parts of this deposit. Results derived via this study reveal that the C-V fractal modelling can be used for resources classification in different ore deposits.

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