Geometallurgical resource estimation using a modified geostatistical approach; a case study of Sungun porphyry copper de
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ORIGINAL PAPER
Geometallurgical resource estimation using a modified geostatistical approach; a case study of Sungun porphyry copper deposit, Iran Farzaneh Khorram 1 & Omid Asghari 1 & Hossein Memarian 2 Received: 20 November 2018 / Accepted: 18 April 2020 # Saudi Society for Geosciences 2020
Abstract Geometallurgical modeling involves a comprehensive approach to ore characterization in terms of critical processing attributes, particularly in complex deposits. In this study, an algorithm based on localized uniform conditioning (LUC) method is implemented for creation of spatial geometallurgical domains at Sungun porphyry copper deposit. Localized uniform conditioning (LUC) as a nonlinear method is appropriate for optimal grade estimation of small blocks when the input data spacing is too coarse. In some deposits with complicated geometry, the accuracy of the local estimation through this method depends on the block ranking techniques. The proposed idea is the application of direct block simulation (DBSIM) method realizations for the ranking purpose. The total and oxide copper grades were estimated using the main localized uniform conditioning and proposed algorithm at 30 × 30 × 15 m3 blocks. The outcomes show a satisfactory conformity with the geological facts of the Sungun deposit. Also, the comparison between the results of both methods and the real data confirms the superiority of the proposed method. Finally, the precise destination of each block in the mineral processing circuit has been predicted based on the estimated total and oxide copper grade. The results illustrate that the applied method is able to preserve the spatial variation of attributes and provides an accurate ranking and robust recoverable resource estimation. Keywords Geometallurgical modeling . Porphyry copper deposit . Localized uniform conditioning . Ore deposit . Geostatistical method
Introduction Geometallurgy is a growing area of mining and new perspective to mineral exploration and production, which brings together aspects of geostatistics and mineralogical properties (Lamberg 2011; Coward and Dowd 2015). Responsible Editor: Abdullah Erhan Tercan * Farzaneh Khorram [email protected] Omid Asghari [email protected] Hossein Memarian [email protected] 1
Simulation and Data Processing Laboratory, School of Mining Engineering, University College of Engineering, University of Tehran, Tehran, Iran
2
School of Mining Engineering, University College of Engineering, University of Tehran, Tehran, Iran
Geometallurgical methodologies enable experts to consider ore variability and make critical decisions about project operation and economic performance (Coward and Dowd 2015; Vann et al. 2012; Williams 2013). In order to achieve the improved management of mineral resources, modeling of geometallurgical variables are becoming increasingly important. Such models aim to provide quantitative prediction of quality of concentrates and tailings (Boisvert et al. 2013). The implementation of geometallurgy in mining projects relies on the developm
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