Multivariate simulation of block-support grades at Mehdiabad deposit, Iran

References

• Azari K, Sethna SF. 1994. Geology and ore localization study of the zinc-lead sulphide deposit of Central Valley Mehdiabad-EH-Bahadoran. Proceeding of Fourth Mining Symposium of Iran; Sept 10–14; Yazd Province, Iran. Exploration, v. 1.
   Google Scholar
• Barnett RM, Manchuk JG, Deutsch CV. 2013. Projection pursuit multivariate transform. Math Geosci. 46(3):337–359. doi: 10.1007/s11004-013-9497-7
   Web of Science ®Google Scholar
• Baumgartner R, Dusci M, Gressier J, Mayta P. 2011. Building a geometallurgical model for early-stage project development – a case study from the Canahuire epithermal Au-cu-Ag deposit, Southern Peru. In: Dominy S, editor. The First AUSIMM International Geometallurgy Conference. Melbourne: The Australasian Institute of Mining and Metallurgy; p. 53–60.
   Google Scholar
• Boucher A, Dimitrakopoulos R. 2009. Block simulation of multiple correlated variables. Math Geosci. 41(2):215–237. doi: 10.1007/s11004-008-9178-0
   Web of Science ®Google Scholar
• Boucher A, Dimitrakopoulos R. 2012. Multivariate block-support simulation of the yandi iron ore deposit, Western Australia. Math Geosci. 44(4):449–468. doi: 10.1007/s11004-012-9402-9
   Web of Science ®Google Scholar
• BRGM. 1994. Mehdiabad lead-zinc deposit, prefeasibility study. Geological Assessment. Report Unpublished Internal Report.
   Google Scholar
• Chilès J-P, Delfiner P. 2012. Geostatistics: modeling spatial uncertainty, 2nd ed. New Jersey: John Wiley & Sons.
   Google Scholar
• David M. 1988. Handbook of applied advanced geostatistical ore reserve estimation. Dev Geomath. 6:216.
   Google Scholar
• De Freitas Silva M, Dimitrakopoulos R. 2015. Simulation of weathered profiles coupled with multivariate block-support simulation of the Puma Nickel Laterite Deposit, Brazil. Les Cahiers du GERAD, G-2015-90, 1–24.
   Google Scholar
• Desbarats AJ, Dimitrakopoulos R. 2000. Geostatistical simulation of regionalized pore-size distributions using min/max autocorrelation factors. Math Geol. 32(8):919–942. doi: 10.1023/A:1007570402430
   Google Scholar
• Emery X, Ortiz JM. 2011. Two approaches to direct block-support conditional co-simulation. Comput Geosci. 37(8):1015–1025. doi: 10.1016/j.cageo.2010.07.012
   Web of Science ®Google Scholar
• Ghazanfari F. 1999. Zn–Pb mines and deposits in Iran. Unpublished Report.
   Google Scholar
• Ghorbani M. 2013. The economic geology of Iran: mineral deposits and natural resources. New York (NY): Springer Science & Business Media.
   Google Scholar
• Goodfellow R, Albor Consuegra F, Dimitrakopoulos R, Lloyd T. 2012. Quantifying multi-element and volumetric uncertainty, Coleman McCreedy deposit, Ontario, Canada. Comput Geosci. 42:71–78. doi: 10.1016/j.cageo.2012.02.018
   Web of Science ®Google Scholar
• Goovaerts P. 1993. Spatial orthogonality of the principal components computed from coregionalized variables. Math Geol. 25(3):281–302. doi: 10.1007/BF00901420
   Google Scholar
• Goovaerts P. 1997. Geostatistics for natural resources evaluation. New York (NY): Oxford University Press.
   Google Scholar
• G.S.I. 1998. Geological studies on the Mehdiabad Lead and Zinc Project. Unpublished report.
   Google Scholar
• Hosseini SA, Asghari O. 2016. Multivariate geostatistical simulation of the Gole Gohar Iron Ore deposit, Iran. J South Afr Inst Min Metall. 116(5):423–430. doi: 10.17159/2411-9717/2016/v116n5a8
   Web of Science ®Google Scholar
• Isaaks EH. 1991. The application of Monte Carlo methods to the analysis of spatially correlated data. San Francisco: Stanford University.
   Google Scholar
• Journel AG, Huijbregts CJ. 1978. Mining geostatistics. London: Academic Press.
   Google Scholar
• Leuangthong O, Deutsch CV. 2003. Stepwise conditional transformation for simulation of multiple variables. Math Geol. 35(2):155–173. doi: 10.1023/A:1023235505120
   Google Scholar
• Lishchuk V. 2016. Geometallurgical programs–critical evaluation of applied methods and techniques. Luleå: Luleå University of Technology.
   Google Scholar
• Lopes JA, Rosas CF, Fernandes JB, Vanzela GA. 2011. Risk quantification in grade-tonnage curves and resource categorization in a lateritic nickel deposit using geologically constrained joint conditional simulation. J Min Sci. 47(2):166–176. doi: 10.1134/S1062739147020043
   Web of Science ®Google Scholar
• Mueller UA, Ferreira J. 2012. The U-WEDGE transformation method for multivariate geostatistical simulation. Math Geosci. 44(4):427–448. doi: 10.1007/s11004-012-9384-7
   Web of Science ®Google Scholar
• Reichert J. 2007. A metallogenetic model for carbonate-hosted non-sulphide zinc deposits based on observations of Mehdiabad and Irankuh, Central and Southwestern Iran. Halle (Saale): Universität Halle-Wittenberg.
   Google Scholar
• Reichert J, Borg G, Rashidi B. 2003. Mineralogy of calamine ore from the Mehdiabad zinc–lead deposit, Central Iran. In: Eliopoulos DG, editor. Mineral exploration and sustainable development. Rotterdam: Millpress; p. 97–100.
   Google Scholar
• Rondon O. 2012. Teaching aid: minimum/maximum autocorrelation factors for joint simulation of attributes. Math Geosci. 44(4):469–504. doi: 10.1007/s11004-011-9329-6
   Web of Science ®Google Scholar
• Rosenblatt M. 1956. Remarks on some nonparametric estimates of a density function. Ann Math Stat. 27(3):832–837. doi: 10.1214/aoms/1177728190
   Google Scholar
• da Silva CZ. 2013. A descorrelação de variáveis com fatorização MAF em estimativa de teores [MSc. Dissertação]. Porto Alegre: Universidade Federal do Rio Grande do Sul.
   Google Scholar
• Da Silva CZ, Costa JF. 2014. Minimum/maximum autocorrelation factors applied to grade estimation. Stoch Environ Res Risk Assess. 28(8):1929–1938. doi: 10.1007/s00477-014-0879-2
   Web of Science ®Google Scholar
• Switzer P, Green AA. 1984. Min/max autocorrelation factors for multivariate spatial imagery. Stanford (CA): Department of Statistics, Stanford University. Technical Report No. 6.
   Google Scholar
• Tercan AE, Sohrabian B. 2013. Multivariate geostatistical simulation of coal quality data by independent components. Int J Coal Geol. 112:53–66. doi: 10.1016/j.coal.2012.10.007
   Web of Science ®Google Scholar
• Tolosana-Delgado R, Mueller U, van den Boogaart KG, Ward C, Gutzme J. 2015. Improving processing by adaption to conditional geostatistical simulation of block compositions. J South Afr Inst Min Metall. 115(1):13–26. doi: 10.17159/2411-9717/2015/v115n1a2
   Web of Science ®Google Scholar
• Tran T, Murphy M, Glacken I. 2006. Semivariogram structures used in multivariate conditional simulation via minimum/maximum autocorrelation factors. In: Pirard E, Dassargues A, Havenith HB, editors. Proceedings XI International Congress, IAMG, Liège.
   Google Scholar
• Vargas-Guzmán JA. 2004. Fast modeling of cross-covariances in the LMC: a tool for data integration. Stoch Environ Res Risk Assess. 18(2):91–99. doi: 10.1007/s00477-003-0131-y
   Web of Science ®Google Scholar
• Wackernagel H. 2003. Multivariate geostatistics: an introduction with applications. 2nd ed. Berlin: Springer.
   Google Scholar