Optimising the placement of additional drill holes to enhanced mineral resource classification: a case study on a porphyry copper deposit

References

• X. Emery, J.M. Ortiz, and J.J. Rodrguez, Quantifying uncertainty in mineral resources by use of classification schemes and conditional simulations, Math. Geol. 38 (2006), pp. 445–464. doi:10.1007/s11004-005-9021-9.
   Google Scholar
• C.V. Deutsch, O. Leuangthong, and J. Ortiz, Case for geometric criteria in resources and reserves classification, Trans. So.c Min. Metall. Explor. Inc. (2007), pp. 301–322.
   Google Scholar
• D. Silva and J. Boisvert, Mineral resource classification: A comparison of new and existing techniques, J. South Afr. Inst. Min. Metall. 114 (2014), pp. 265–273.
   Web of Science ®Google Scholar
• R. Dimitrakopoulos, M. Godoy, and C. Chou, Resource/reserve classification with integrated geometric and local grade variability measures, Proceedings, Orebody modelling and strategic mine planning, Perth, 2009, pp. 207–214.
   Google Scholar
• S. Owusu and K. Dagdelen, Critical review of mineral resource classification techniques in the gold mining industry, Mining goes digital: proceedings of the 39th international symposium ‘application of computers and operations research in the mineral industry’(APCOM 2019), Wroclaw, Poland, 2019, pp. 201–209.
   Google Scholar
• G. Froyland, M. Menabde, P. Stone, and D. Hodson, The value of additional drilling to open pit mining projects, in Advances in applied strategic mine planning, 2018, pp. 119–138.
   Google Scholar
• D. Bhattacharjya, J. Eidsvik, and T. Mukerji, The value of information in spatial decision making, Math. Geosci. 42 (2) (2010), pp. 141–163. doi:10.1007/s11004-009-9256-y.
   Web of Science ®Google Scholar
• S. Soltani-Mohammadi and A. Hezarkhani, A simulated annealing-based algorithm to locate additional drillholes for maximizing the realistic value of information, Nat. Resour. Res. 22 (3) (2013), pp. 229–237. doi:10.1007/s11053-013-9212-x.
   Google Scholar
• J. Caers, C. Scheidt, Z. Yin, L. Wang, T. Mukerji, and K. House, Efficacy of information in mineral exploration drilling, Nat. Resour. Res. 31 (3) (2022), pp. 1157–1173. doi:10.1007/s11053-022-10030-1.
   Web of Science ®Google Scholar
• M. Gershon, L.E. Allen, and G. Manley, Application of a new approach for drillholes location optimization, Int. J. Min. Reclam. Environ. 2 (1) (1988), pp. 27–31. doi:10.1080/09208118808944133.
   Google Scholar
• S. Soltani, A. Hezarkhani, A. Erhan Tercan, and B. Karimi, Use of genetic algorithm in optimally locating additional drill holes, J. Min. Sci. 47 (1) (2011), pp. 62–72. doi:10.1134/S1062739147010084.
   Web of Science ®Google Scholar
• S. Soltani-Mohammadi, B. Sohrabian, M. Safa, and A.E. Tercan, A monte carlo simulation-based algorithm for updating combined variance cost function in optimally locating additional drillholes, Int. J. Min. Reclam. Environ. 36 (5) (2022), pp. 305–322. doi:10.1080/17480930.2022.2036558.
   Web of Science ®Google Scholar
• A.H. Morshedy and H. Memarian, A novel algorithm for designing the layout of additional boreholes, Ore Geol. Rev. 67 (2015), pp. 34–42. doi:10.1016/j.oregeorev.2014.11.012.
   Web of Science ®Google Scholar
• S. Soltani-Mohammadi, M. Safa, and H. Mokhtari, Comparison of particle swarm optimization and simulated annealing for locating additional boreholes considering combined variance minimization, Comput. Geosci. 95 (2016), pp. 146–155. doi:10.1016/j.cageo.2016.07.020.
   Web of Science ®Google Scholar
• F. Szidarovszky, Multiobjective observation network design for regionalized variables, Int. J. Mining Eng. 1 (1983), pp. 331–342. doi:10.1007/BF00881549.
   Web of Science ®Google Scholar
• J.K. Yamamoto, Quantification of uncertainty in ore-reserve estimation: Applications to chapada copper deposit, state of goiás, brazil, Nat. Resour. Res. 8 (2) (1999), pp. 153–163. doi:10.1023/A:1021894703729.
   Google Scholar
• J.K. Yamamoto, An alternative measure of the reliability of ordinary kriging estimates, Math. Geol. 32 (4) (2000), pp. 489–509. doi:10.1023/A:1007577916868.
   Google Scholar
• J.P. Chiles and P. Delfiner, Geostatistics: Modeling Spatial Uncertainty, 713, John Wiley & Sons, New Jersey, USA, 2012.
   Google Scholar
• P. Bogaert and D. Russo, Optimal spatial sampling design for the estimation of the variogram based on a least squares approach, Water Resour. Res. 35 (4) (1999), pp. 1275–1289. doi:10.1029/1998WR900078.
   Web of Science ®Google Scholar
• J.W. Van Groenigen, W. Siderius, and A. Stein, Constrained optimisation of soil sampling for minimisation of the kriging variance, Geoderma 87 (3–4) (1999), pp. 239–259. doi:10.1016/S0016-7061(98)00056-1.
   Web of Science ®Google Scholar
• A. Hassanipak and M. Sharafodin, Get: A function for preferential site selection of additional borehole drilling, Explor. Min. Geol. 13 (1–4) (2004), pp. 139–146. doi:10.2113/gsemg.13.1-4.139.
   Google Scholar
• A.G. Journel and C.J. Huijbregts. Mining geostatistics, Academic Press, London, United Kingdom, 1976.
   Google Scholar
• A. Arik, Uncertainty, confidence intervals and resource categorization: A combined variance approach, Proceedings, ISGSM Symposium, Perth, Australia, 1999.
   Google Scholar
• M. Monteiro da Rocha and J.K. Yamamoto, Comparison between kriging variance and interpolation variance as uncertainty measurements in the capanema iron mine, state of minas gerais—brazil, Nat. Resour. Res. 9 (3) (2000), pp. 223–235. doi:10.1023/A:1010195701968.
   Google Scholar
• R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, MHS’95. Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan, Ieee, 1995, pp. 39–43.
   Google Scholar
• M.G. Sahab, V.V. Toropov, and A.H. Gandomi, A review on traditional and modern structural optimization: Problems and techniques, Metaheuristic Appl. In Struct. And Infrastructures 6 (2013), pp. 25–47.
   Google Scholar
• Y.M. Cheng, L. Li, S.C. Chi, and W. Wei, Particle swarm optimization algorithm for the location of the critical non-circular failure surface in two-dimensional slope stability analysis, Comput. Geotech. 34 (2) (2007), pp. 92–103. doi:10.1016/j.compgeo.2006.10.012.
   Web of Science ®Google Scholar
• J.A. Ferland, J. Amaya, and M.S. Djuimo, Application of a particle swarm algorithm to the capacitated open pit mining problem, Auton. Robots & Agents 76 (2007), pp. 127–133.
   Google Scholar
• J. Fernández-Martnez, E. Garca-Gonzalo, and J. Fernández-Alvarez, Theoretical analysis of particle swarm trajectories through a mechanical analogy, Int. J. Comput. Intell. Res. 4 (2) (2008), pp. 93–105. doi:10.5019/j.ijcir.2008.129.
   Google Scholar
• M. Clerc and J. Kennedy, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. Evol. Comput. 6 (1) (2002), pp. 58–73. doi:10.1109/4235.985692.
   Web of Science ®Google Scholar
• A.J. Sinclair and G.H. Blackwell, Applied Mineral Inventory Estimation, Cambridge University Press, Cambridge, United Kingdom, 2002.
   Google Scholar
• B. Jafrasteh and N. Fathianpour, Optimal location of additional exploratory drillholes using afuzzy-artificial bee colony algorithm, Arabian J. Geosci. 10 (9) (2017), pp. 1–16. doi:10.1007/s12517-017-2948-6.
   Web of Science ®Google Scholar
• G.E. Zagré, D. Marcotte, M. Gamache, and F. Guibault, New tabu algorithm for positioning mining drillholes with blocks uncertainty, Nat. Resour. Res. 28 (3) (2019), pp. 609–629. doi:10.1007/s11053-018-9412-5.
   Web of Science ®Google Scholar