Grade 3D Block Modeling and Reserve Estimation of the C-North Iron Skarn Ore Deposit, Sangan, NE Iran
Abstract - 225


Grade estimation, Block modeling, Ordinary Kriging, VIKOR, Sangan, Iran.

How to Cite

Ali Rezaei, Hossein Hassani, Parviz Moarefvand, Abbas Golmohammadi. Grade 3D Block Modeling and Reserve Estimation of the C-North Iron Skarn Ore Deposit, Sangan, NE Iran. Glob. J. Earth Sci. Eng. [Internet]. 2019 Dec. 27 [cited 2024 Feb. 20];6(1):23-37. Available from:


 Estimation of ore grade is a time and cost consuming process that requires laboratory-based and exploratory information to present the shape and the ore grade distribution of ore deposit in three dimensional space. The block size is one of the most important parameters which impacts the quality of grade estimates in a resource model. This study aims at spatial modeling of iron ore deposit using geostatistical estimation methods such as Ordinary Kriging based on error estimation, selection of the appropriate size for mining blocks using Vlse Kriterijumsk Optimizacija Kompromisno Resenje method, and performing a three-dimensional block modeling along a grade estimation study for the resource estimation in the C-North iron ore deposit, NE Iran. The variogram that was used in OK estimation was cross validated. Cross validation results showed that compared with the local model, OK with the global model was the most appropriate model for the ore body. Detailed distribution maps of total iron contents in the C-North ore deposit showed a close relationship between structural features and higher iron contents, relative to other areas of the ore deposit. Structural features included the major faults and fault zones along the axial plane. These structures are interpreted to have played a significant role in (re) mobilisation and concentration of the metals, in agreement with observations made elsewhere in the Sangan iron ore complex. Based on the estimation results, 83 million tons of resource was estimated at an average grade of 41.86 % Fe using OK method. The C-North ore deposit has been classified based on the relative estimation error variance and the Australasian Code for Reporting of Mineral Resources and Ore Reserves. It is hoped that this example, taken from very different application fields, will encourage practitioners in applying an OK method with variety of ore deposits.


Hayati M, Rajabzadeh R, Darabi M. Determination of Optimal Block Size in Angouran Mine Using VIKOR Method. J Mater Environ Sci 2015; 6: 3236-3244.

Kameshwara R, Raghavendra R, Chinna Allu N. Assessing grade domain of iron ore deposit using geostatistical modelling: A case study. Journal of the Geological Society of India 2014; 83: 549-554.

Choudhury S. Comparative Study on Linear and Non-Linear Geostatistical Estimation Methods: A Case Study on Iron Deposit. Development for Mining of Mineral and Fossil Energy Resources 2015; 131-139.

Hustrulid W, Kuchta M. Open Pit Mine Planning and Design, Taylor & Francis, ISBN 9780415407410, USA 2006.

Daya AA. Reserve estimation of central part of Choghart north anomaly iron ore deposit through ordinary kriging method. International Journal of Mining Science and Technology 2012; 22: 573-577.

Shademan khakestar M, Hassani H, Moarefvand P. Determining the best search neighbourhood in reserve estimation, using geostatistical method: A case study anomaly No. 12A iron deposit in central Iran. Journal of the Geological Society of India 2013; 81: 581-585.

Shahbeik S, Afzal P, Moarefvand P, Qumarsy M. Comparison between ordinary kriging (OK) and inverse distance weighted (IDW) based on estimation error. Case study: Dardevey iron ore deposit, NE Iran. Arab J Geosci 2014; 7: 3693-3704.

Mery N, Emery X, Cáceres A, Ribeiro D, Cunha E. Geostatistical modeling of the geological uncertainty in an iron ore deposit. Ore Geology Reviews 2017; 88: 336-351.

Tahernejad MM, Khalokakaie R, Ataei M. Analyzing the effect of ore grade uncertainty in open pit mine planning; a case study of the Rezvan iron mine, Iran. International Journal of Mining and Geo-Engineering 2018; 1: 53-60.

Kim S, Choi Y, Park H. New Outlier Top-Cut Method for Mineral Resource Estimation via 3D Hot Spot Analysis of Borehole Data. Minerals 2018; 8: 348.

Tutmez. An uncertainty oriented fuzzy methodology for grade estimation. Computers and Geosciences 2003; 33: 280-288.

David M. Geostatistical Ore Reserve Estimation, Elsevier, Amsterdam 1970.

Boisvert JB, Ortiz JM, Deutsch CV. Local recoverable reserves prediction with block LU simulation. International Journal of Mining and Mineral Engineering 2008; 1: 3-21.

Tahmasebi P, Hezarkhani A. Application of adaptive neuro-fuzzy inference system for grade estimation; case study, Sarcheshmeh porphyry copper deposit, Kerman, Iran. Australian Journal of Basic and Applied Sciences 2010; 4: 408-420.

Martins AC, Nader B, De Tomi G. A novel application of cellular automata for the evaluation and modelling of mineral resources. International Journal of Mining and Mineral Engineering 2011; 3: 303-315.

Journel AG, Huijbregts CJ. Mining geostatistics. Academic press 1978; 600.

Brown WM, Gedeon TD, Groves DI, Barners RG. Artificial networks: a new method for mineral prospectivity mapping. Australian Journal of Earth Sciences 2000; 47: 757-770.

Harris JR, Grunsky E. Predictive lithological mapping of Canada's north using random forest classification applied to geophysical and geochemical data. Computers & Geosciences 2015; 88: 9-25.

Haldar SK. Mineral Exploration: Principles and Applications. Elsevier Science Publishing Co Inc, United States 2013.

Wang G, Huang L. 3D geological modeling for mineral resource assessment of the Tongshan Cu deposit, Heilongjiang Province, China. Geoscience Frontiers 2012; 3: 483-491.

Rezaei A, Hassani H, Moarefavand P, Golmohammadi A. Determination of unstable tectonic zones in C–North deposit, Sangan, NE Iran using GPR method: Importance of structural geology. Journal of Mining and Environment 2019; 10: 177-195.

Sepidbar F, Mirnejad H, Mi C. Mineral chemistry and Ti in zircon thermometry: Insights into magmatic evolution of the Sangan igneous rocks, NE Iran. Journal of Chemie Der Erde 2018; 78(2): 205-214.

Malekzadeh Shafaroudi A, Karimpour MH, Golmohammadi A. Zircon U–Pb geochronology and petrology of intrusive rocks in the C-North and Baghak districts, Sangan iron mine, NE Iran. Journal of Asian Earth Sciences 2013; 64: 256-271.

Golmohammadi A, Karimpour MH, Malekzadeh Shafaroudi A, Mazaheri SA. Alteration-mineralization, and radiometric ages of the source pluton at the Sangan iron skarn deposit, northeastern Iran. Ore Geol Rev 2015; 65: 545-563.

Kermani A, Forster H. Petrography, Mineralogical and geochemical investigations of the Sangan Iron ore deposit, northeastern Iran. Proceedings of Third Mining Symposium of Iran 1991.

Mazhari N, Malekzadeh Shafaroudi A, Ghaderi M, Star Lackey J, Lang Farmer G, Karimpour MH. Geochronological and geochemical characteristics of fractionated I-type granites associated with the skarn mineralization in the Sangan mining region, NE Iran 2017.

Rezaei A, Hassani H, Moarefvand P, Golmohammadi A. Modeling the effect of Structural Pattern on Mineralization in Sangan Central Iron Ore Mine, Iran. PhD Thesis, Amirkabir University of Technology (AUT), Tehran, Iran 2019; p. 364.

Fauvelet E, Eftekhar-Nezhad J. Explanatory text of the taybad quadrangle map1: 250000, Geological Survey of Iran 1990.

Rezaei A, Hassani H, Moarefvand P, Golmohammadi A. Lithological mapping in Sangan region in Northeast Iran using ASTER satellite data and image processing methods. Journal of Geology, Ecology and Landscapes 2020; 4(1): 59- 70.

Rezaei A, Hassani H, Moarefvand P, Golmohammadi A. Investigation the Effect of Structural Pattern on Mineralization Model In the C-North Ore Deposit, Sangan, NE Iran. Journal of Mineral Resources Engineering (JMRE) 2019; 4(2): 1-5.

Li X, Xie Y, Guo Q, Li L. Adaptive ore grade estimation method for the mineral deposit evaluation. Mathematical and Computer Modelling 2010; 52: 1947-1956.

Isaaks EH, Srivastava RM. An Introduction to Applied Geostatistics. Oxford University Press, New York, USA 1989.

Asghari O, Madani Esfahani N. A new approach for the geological risk evaluation of coal resources through a geostatistical simulation. Case study: Parvadeh III coal deposit. Arab J Geosci 2013; 6: 957-970.

Monjezi M, Rajabalizadeh Kashani M, Ataei M. A comparative study between sequential Gaussian simulation and kriging method grade modeling in open-pit mining. Arab J Geosci 2013; 6: 123-128.

Krige DGA. Statistical Approach to Some Mine Valuations and Allied Problems at the Witwatersrand. Master’s Thesis, University of Witwatersrand, Johannesburg, South Africa 1951.

Armstrong M. Basic Linear Geostatistics; Springer: Berlin/Heidelberg, Germany 1998; 15-115.

Bertoli OJ. Quantitative Kriging Neighbourhood Analysis for the Mining - A Description of the Method with Worked Case Examples. Bendigo, Vic: 5th International Mining Geology Conference 2003.

Chilès JP, Delfiner P. Geostatistics: Modeling Spatial Uncertainty, Wiley, New York 2012.

Pokhrel RM, Kuwano J, Tachibana S. A kriging method of interpolation used to map liquefaction potential over alluvial ground. Engineering Geology 2013; 152: 26-37.

Deutsch CV, Journel AG. GSLIB: Geostatistical Software Library and User's Guide. 2nd ed. Oxford University Press, New York 1998; 369.

Matheron G. Principles of Geostatistics. Econ Geol 1963; 58: 1246-1266.

Hu H, Shu H. An improved coarse-grained parallel algorithm for computational acceleration of ordinary Kriging interpolation. Computers and Geosciences 2015; 78: 44-52.

VerHoef JM, Cressie N. Multivariable spatial prediction. Math Geol 1993; 252: 219-239.

Calder CA, Cressie N. Kriging and variogram models. Elsevier, Oxford 2009; 49-55.

Jara RM, Couble A, Emery X, Magri EJ, Ortiz JM. Block size selection and its impact on open-pit design and mine planning. The Journal of The South African Institute of Mining and Metallurgy 2006; 106: 205-212.

Araújo, Cristina da Paixão, Costa, João Felipe Coimbra Leite, Koppe, Vanessa Cerqueira. Improving short-term grade block models: alternative for correcting soft data. REM - International Engineering Journal 2018; 71(1): 117-122.

Stephenson PR, Vann J. Common Sense and Good Communication in Mineral Resource and Ore Reserve Estimation, in Mineral Resource and Ore Reserve Estimation – The AusIMM Guide to Good Practice (Ed: A C Edwards), (The Australasian Institute of Mining and Metallurgy: Melbourne) 2001; 13-20.

Journel A. Geostatistics: roadblocks and challenges, in A. Soares, ed, Geostatistics-Troia 1993; 1: 213-224.

Emery X. Simple and ordinary kriging, multi-gaussian kriging for estimating recoverable reserves. Mathematical Geology 2005; 37: 295-319.

Emery X, Ortiz JM. Two approaches to direct block-support conditional co-simulation. Computers & Geosciences 2011; 37: 1015-1025.

Opricovic S, Tzeng GH. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research 2004; 156: 445-455.

Opricovic S. Multicriteria Optimization of Civil Engineering Systems, Faculty of Civil Engineering, Belgrade 1998.

Abedi M, Mohammadi R, Norouzi GH, Mohammadi MSM. A comprehensive VIKORmethod for integration of various exploratory data in mineral potential mapping. Arab J Geosci 2016; 9: 482.

Michel D. Geostatistical ore reserve estimation. New York: Elsevier Scientific Publishing Co. 1982.

Snowden DV. Practical interpretation of resource classification guidelines. In: AusIMM Annual Conference, Perth 1996.

Dowd PA. A review of recent developments in geostatistics. Comput Geosci 1992; 17: 1481-500.

JORC. Australasian Code for Reporting of Identified Mineral Resources and Ore Reserves (The JORC Code). The Joint Ore Reserves Committee of the Australasian Institute of Mining and Metallurgy, Australian Institute of Geoscientists, and Minerals Council of Australia 2012.

Rezaei A, Hassani H, Tziritis E, Fard Mousavi SB, Jabbari N. Hydrochemical characterization and evaluation of groundwater quality in Dalgan basin, SE Iran. Journal of Groundwater for Sustainable Development 2020; 10: 2352-801X, 1-13.

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