Mineralogy and Petrology

, Volume 112, Supplement 2, pp 673–684 | Cite as

Microdiamond grade as a regionalised variable – some basic requirements for successful local microdiamond resource estimation of kimberlites

  • Johann StiefenhoferEmail author
  • Malcolm L. Thurston
  • David E. Bush
Original Paper


Microdiamonds offer several advantages as a resource estimation tool, such as access to deeper parts of a deposit which may be beyond the reach of large diameter drilling (LDD) techniques, the recovery of the total diamond content in the kimberlite, and a cost benefit due to the cheaper treatment cost compared to large diameter samples. In this paper we take the first step towards local estimation by showing that micro-diamond samples can be treated as a regionalised variable suitable for use in geostatistical applications and we show examples of such output. Examples of microdiamond variograms are presented, the variance-support relationship for microdiamonds is demonstrated and consistency of the diamond size frequency distribution (SFD) is shown with the aid of real datasets. The focus therefore is on why local microdiamond estimation should be possible, not how to generate such estimates. Data from our case studies and examples demonstrate a positive correlation between micro- and macrodiamond sample grades as well as block estimates. This relationship can be demonstrated repeatedly across multiple mining operations. The smaller sample support size for microdiamond samples is a key difference between micro- and macrodiamond estimates and this aspect must be taken into account during the estimation process. We discuss three methods which can be used to validate or reconcile the estimates against macrodiamond data, either as estimates or in the form of production grades: (i) reconcilliation using production data, (ii) by comparing LDD-based grade estimates against microdiamond-based estimates and (iii) using simulation techniques.


Microdiamond Variography Variance-support curve Size frequency distribution Simulation 



The authors would like to thank the De Beers Group of Companies, Debswana, De Beers Canada Inc. and Anglo American Corporation for permission to publish this work. Mr. Cuan Lohrentz is acknowledged for his assistance in the computational aspects of the simulation studies. Dr Christian Lantuéjoul is acknowledged for his input in the formulation of the non-adjacent sample covariance. We thank the two anonymous reviewers and editor Alan Kobussen for their thorough efforts which improved the quality of this publication.

Supplementary material

710_2018_566_MOESM1_ESM.pdf (387 kb)
ESM 1 (PDF 387 kb)


  1. Bush DE (2010) An overview of the estimation of kimberlite diamond deposits. The Southern African Iinstitute of mining and metallurgy. Diamonds – source to use 2010. pp 73–84Google Scholar
  2. Chapman JG, Boxer GL (2004) Size distribution analyses for estimating diamond grade and value. Lithos 76:369–375CrossRefGoogle Scholar
  3. Chiles J-P, Delfiner P (1999) Geostatistics – modeling spatial uncertainty. Wiley USAGoogle Scholar
  4. David M (1977) Geostatistical ore reserve estimation. Elsevier Scientific, Amsterdam New York, 364 ppGoogle Scholar
  5. Deakin AS, Boxer GL (1989) Argyle AK1 diamond size distribution: the use of fine diamonds to predict the occurrence of commercial sized diamonds. Proceedings of the 4th international kimberlite conference: kimberlites and related rocks, Vol 2, their mantle/crust setting, diamonds and diamond exploration. Special publication – Geological Society of Australia, 14, pp 1117–1122Google Scholar
  6. Dohm CE (2004) Quantifiable mineral resource classification: A logical approach. Leuangthong O and Deutsch CV (eds) Geostatistics Banff 2004, pp 333–342Google Scholar
  7. Fulop A, Kopylova M, Ellemers P, Squibb C (2017) Geology of the snap Lake kimberlite dyke, northwest territories, Canada, and its metasomatic interaction with granite. 11th international kimberlite conference extended abstract no. In: 11IKC-4528Google Scholar
  8. Hammer PTC, Clowes RM, Ramachandran K. (2004) Seismic reflection imaging of thin, kimberlite dykes and sills:exploration and deposit characterization of the Snap Lake dyke, Canada. Proc. 8th international kimberlite conference: The C. Roger Clement volume (1). Lithos special publication, 76/1–4, pp 359–367Google Scholar
  9. Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New York, 561 ppGoogle Scholar
  10. Journel AG, Huijbregts CJ (1978) Mining geostatistics. Blackburn press, USAGoogle Scholar
  11. Kirkley MB, Mogg T, McBean D (2003) Snap Lake field trip guide. In: Kjarsgaard BA (ed) 8th international kimberlite conference, slave province and northern Alberta field trip guidebook, pp 1–12Google Scholar
  12. Matheron G (1971) The theory of regionalized variables and its applications. Center for geostatistics, FontainebleauGoogle Scholar
  13. Matheron G (1973) The intrinsic random functions and its applications. Center for geostatistics, FontainebleauGoogle Scholar
  14. McBean D, Kirkley M, Revering C (2003) Structural controls on the morphology of the Snap Lake kimberlite dyke. Extended abstracts of the 8th international kimberlite conference, Victoria, Canada, pp 69–74Google Scholar
  15. Myers DE (1989) To be or not to be……stationary? That is the question. Math Geol 21(3):347–362CrossRefGoogle Scholar
  16. Parker HM, Dohm CE (2014) Evolution of mineral resource classification from 1980 to 2014 and current best practice. Finex ‘14 Julius Wernher memorial lecture. Minsouth, a society of the IOM3. London. Accessed 23 October 2015
  17. Rombouts L (1995) Sampling and statistical evaluation of diamond deposits. J Geochem Explor 53:351–367CrossRefGoogle Scholar
  18. Sinclair AJ, Blackwell GH (2002) Applied mineral inventory estimation. Cambridge University PressGoogle Scholar
  19. Stiefenhofer, J. (2013). The use of chemical and metallurgical parameters to enhance the economic value of kimberlite resource models. The Southern African Institute of Mining and Metallurgy. Diamonds – Source to use 2013. Proceedings vol, pp 1–18Google Scholar
  20. Stiefenhofer J, Thurston ML, Rose DM, Chinn IL, Ferreira JJ (2016) Principles relating to the use of micro-diamonds for resource estimation: 1 – the impact of mantle and kimberlite processes. Can Inst Min J 7(4)Google Scholar
  21. Van Straaten B, Kopylova M, Russell K, Webb K, Scott Smith B (2008) Discrimination of diamond resource and non-resource domains in the victor north pyroclastic kimberlite, Canada. J Volcanol Geoth Res 174:128–138CrossRefGoogle Scholar
  22. Vann J, Guibal D (1998) Beyond ordinary kriging – a review of non-linear estimation. In: Vann J (ed) Beyond ordinary kriging: non-linear geostatistical methods in practice. The geostatistical association of Australasia, Perth, pp 6–25Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Johann Stiefenhofer
    • 1
    • 2
    Email author
  • Malcolm L. Thurston
    • 1
  • David E. Bush
    • 3
  1. 1.Technical and SustainabilityDe Beers Group Services (Pty) LtdJohannesburgSouth Africa
  2. 2.MinRes, Corporate DivisionAnglo American Operations LtdJohannesburgSouth Africa
  3. 3.Z Star Mineral Resource Consultants (Pty) LtdCape TownSouth Africa

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