References
- Amorim LQ, Ribeiro DT. 1996. An useful ore reserve classification criterion based on indicator kriging. In: Hennies W.T., da Silva L.A., Chaves A.P., editor. Proceedings of mine planning and equipment selection. Rotterdam: Balkema; p. 117–121.
- CRIRSCO. 2013. International reporting template for the public reporting of exploration results, mineral resources, and mineral reserves.
- Deutsch CV. 2007. The slope of regression for kriging estimators. Centre Comput Geostat. 9:311.
- Deutsch JL, Szymanski J, Deutsch CV. 2014. Checks and measures of performance for kriging estimates. J South Afr Inst Min Metall. 114:223.
- Duggan GA, Stiefenhofer J, Thurston M. 2017. Development of a best-practice mineral resource classification system for the De Beers group of companies. J South Afr Inst Min Metall. 117:1127–1132. doi:10.17159/2411-9717/2017/v117n12a6.
- Emery X, Ortiz JM, Rodríguez JJ. 2006. Quantifying uncertainty in mineral resources by use of classification schemes and conditional simulations. Math Geol. 38:445–464. doi:10.1007/s11004-005-9021-9.
- Isaaks EH, Srivastava RM. 1989. Applied geostatistics. New York: Oxford university press.
- JORC. 2012. Australasian code for reporting of exploration results, 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.
- Journel AG. 1983. Nonparametric estimation of spatial distributions. J Int Assoc Math Geol. 15:445–468. doi:10.1007/BF01031292.
- Journel AG, Huijbregts CJ. 1978. Mining geostatistics. New York: Academic Press.
- Krige DG. 1996. A practical analysis of the effects of spatial structure and of data available and accessed, on conditional biases in ordinary kriging. Geostat Wollongong. 96:799–810.
- Matheron G. 1963. Principles of geostatistics. Econ Geol. 58:1246–1266. doi:10.2113/gsecongeo.58.8.1246.
- Mohanlal K, Stevenson P. 2010. Anglo American Platinum’s approach to resource classification case study – Boschkoppie/Styldrift minewide UG2 project. In: The 4th International Platinum Conference, Platinum in Transition ‘Boom or Bust’. The Southern African Institute of Mining and Metallurgy.
- Parker HM, Dohm CE. 2014. Evolution of mineral resource classification from 1980 to 2014 and current best practice. In: FINEX 2014 Conference.
- Parrish IS. 1993. Tonnage factor – a matter of some gravity. Min Eng. 45:1268–1271.
- Ribeiro DT, Monteiro Filho CG, Souza LEd, Costa JFCL, Almeida DDPMd. 2012. Utilização de critérios geoestatísticos para comparação de malha de sondagem visando à maximização da quantidade de recursos. Rem: Revista Escola de Minas. 65:113–118. doi:10.1590/S0370-44672012000100016.
- Rivoirard J. 1987. Two key parameters when choosing the kriging neighborhood. Math Geol. 19:851–856. doi:10.1007/BF00893020.
- Rossi ME, Deutsch CV. 2014. Mineral resource estimation. New York: Springer Science & Business Media.
- SAMREC. 2009. The South African code for the reporting of exploration results, mineral resources and mineral reserves.
- Silva DSF. 2015. Mineral resource classification and drill hole optimization using novel geostatistical algorithms with a comparison to traditional techniques. https://doi.org/10.7939/R3VT1GV9M.
- Verly G, Parker HM. 2021. Conditional simulation for mineral resource classification and mining dilution assessment from the early 1990s to now. Math Geosci. 53:279–300. doi:10.1007/s11004-021-09924-2.