Using GEOVIA Surpac 3D Software Instead of Manual Methods to Accurately Estimate Ore Reserve Potential in Libyan Quarries and Mines.
DOI:
https://doi.org/10.66411/jer.v41i1.120Keywords:
GEOVIA Surpac, Libyan mining industry, 3D visualization, drillhole dataset, mineral resourcesAbstract
The sustainable development of mineral resources requires accurate estimations of ore reserves. However, most mines in Libya today still rely on traditional mining approaches that are plagued by inaccuracy. The present study explores how the software GEOVIA Surpac can be used to modernize Libyan mining industry by offering a three-dimensional (3D) digital strategy for estimating ore reserves. A representative drillhole dataset constructed by Surpac describes the recommended workflow, starting with database preparation and ending with reserve reporting. The demonstration clearly shows Surpac’s superiority compared to manual approaches, especially with regard to reproducibility, 3D visualization, and reporting standards compliance. Furthermore, the results validate GEOVIA Surpac’s ability in building highly accurate digital estimation reference frameworks for mining Libyan ore reserves.
References
[1] I. Clark, Practical Geostatistics, vol. 3. London, U.K.: Applied Science Publishers, 1979, p. 129
[2] C. C. Popoff, Computing Reserves of Mineral Deposits: Principles and Conventional Methods. Washington, DC, USA: U.S. Department of the Interior, Bureau of Mines, 1966.
[3] R. Dimitrakopoulos and M. Godoy, “Grade Control Based on Economic Ore/Waste Classification Functions and Stochastic Simulations: examples, comparisons and applications,” Mining Technology, vol. 123, no. 2, pp. 90–106, 2014, doi: 10.1179/1743286314Y.0000000062. DOI: https://doi.org/10.1179/1743286314Y.0000000062
[4] A. G. Journel and C. J. Huijbregts, Mining Geostatistics. London, U.K.: Academic Press, 1978.
[5] R. A. Bilonick, “An Introduction to Applied Geostatistics,” vol. 1706, no. 1991, 2012, doi: 10.1080/00401706.1991.10484886. DOI: https://doi.org/10.2307/1269430
[6] E. J. Cowan, R. K. Beatson, W. R. Fright, T. J. McLennan, and T. J. Mitchell, “Rapid geological modelling,” in Applied Structural Geology for Mineral Exploration and Mining, International Symposium, pp. 23–25, 2002.
[7] JORC Committee, Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves, Melbourne, Australia: The Joint Ore Reserves Committee, 2012, pp. 1–44.
[8] Canadian Institute of Mining, Metallurgy and Petroleum (CIM), CIM Definition Standards for Mineral Resources and Mineral Reserves, Montreal, QC, Canada: CIM, 2014.
[9] W. A. Hustrulid, M. Kuchta, and R. K. Martin, Open Pit Mine Planning and Design, 3rd ed. Boca Raton, FL, USA: CRC Press, 2013.
[10] M. M. Sahoo and B. K. Pal, “Geological modelling of a deposit and application using Surpac,” Journal of Mines, Metals and Fuels, vol. 65, no. 7, pp. 417–422, 2017.
[11] X. Li, D. Li, Z. Liu, G. Zhao, and W. Wang, “Determination of the minimum thickness of crown pillar for safe exploitation of a subsea gold mine based on numerical modelling,” International Journal of Rock Mechanics and Mining Sciences, vol. 57, pp. 42–56, 2013., doi: 10.1016/j.ijrmms.2012.08.005. DOI: https://doi.org/10.1016/j.ijrmms.2012.08.005
[12] M. E. Rossi and C. V. Deutsch, Mineral Resource Estimation. Cham, Switzerland: Springer, 2013. DOI: https://doi.org/10.1007/978-1-4020-5717-5
[13] T. Busuyi, V. Oluwatosin, and A. Emman, “Geoscience Frontiers A comparative study of geometric and geostatistical methods for qualitative reserve estimation of limestone deposit,” Geosci. Front., vol. 12, no. 1, pp. 243–253, 2021, doi: 10.1016/j.gsf.2020.02.019. DOI: https://doi.org/10.1016/j.gsf.2020.02.019
[14] W. S. Bargawa and N. A. Amri, “Mineral Resources Estimation Based on Block Modeling,” in AIP Conference Proceedings, vol. 1705, no. 1, p. 020001, 2016. DOI: https://doi.org/10.1063/1.4940249
[15] B. O. F. Technology, “Modeling of Opencast Mines Using Surpac and Its Optimization,” Ph.D. dissertation, 2012.
[16] S. Coward and C. V. Deutsch, “Geostatistical Modeling and Estimation in Complex Gold Deposits,” in Proc. International Mining Geology Conference, Melbourne, Australia: Australasian Institute of Mining and Metallurgy (AusIMM), 2015.
[17] P. Goovaerts, Geostatistics for Natural Resources Evaluation. New York, NY, USA: Oxford University Press, 1997. DOI: https://doi.org/10.1093/oso/9780195115383.001.0001
[18] K. Kaheni, A. R. Mokhtari, and H. Farhadian, “Geostatistical assessment of ore reserve estimation accuracy in the Hired Gold Deposit,” Journal of Geomine, vol. 3, no. 3, pp. 155–164, Sep. 2025.
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