Skip to main content
SpringerLink
Log in

Variography

  • Chapter
  • First Online:
Applied Mining Geology

Part of the book series: Modern Approaches in Solid Earth Sciences ((MASE,volume 12))

Abstract

Geostatistical techniques allow a quantitative assessment of the spatial continuity of the regionalised variable. Most commonly used approach is based on estimating the squared difference between pairs of the data points separated by a vector (x). This is a basis of the variogaram, which is a special geostatistical tool applied for modelling the spatial continuity of the studied variables.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Abzalov MZ (2014) Geostatistical criteria for choosing optimal ratio between quality and quantity of the samples: method and case studies. In: Mineral resource and Ore Reserves Estimation, AusIMM Monograph 23, 2nd edn. Chapter 2: The resource database. AusIMM, Melbourne, pp 91–96

    Google Scholar 

  • Abzalov MZ, Bower J (2014) Geology of bauxite deposits and their resource estimation practices. Appl Earth Sci 123(2):118–134

    Article  Google Scholar 

  • Abzalov MZ, Humphreys M (2002a) Resource estimation of structurally complex and discontinuous mineralisation using non-linear geostatistics: case study of a mesothermal gold deposit in northern Canada. Exp Min Geol J 11(1–4):19–29

    Article  Google Scholar 

  • Abzalov MZ, Humphreys M (2002b) Geostatistically assisted domaining of structurally complex mineralisation: method and case studies. Geostatistically assisted domaining of structurally complex mineralisation: method and case studies. In: The AusIMM 2002 conference: 150 years of mining, Publication series No 6/02, pp 345–350

    Google Scholar 

  • Abzalov MZ, Mazzoni P (2004) The use of conditional simulation to assess process risk associated with grade variability at the Corridor Sands detrital ilmenite deposit. In: Dimitrakopoulus R, Ramazan S (eds) Ore body modelling and strategic mine planning: uncertainty and risk management. AusIMM, Melbourne, pp 93–101

    Google Scholar 

  • Abzalov MZ, Pickers N (2005) Integrating different generations of assays using multivariate geostatistics: a case study. Trans Inst Min Metall 114:B23–B32

    Google Scholar 

  • Abzalov MZ, van der Heyden A, Saymeh A, Abuqudaira M (2015) Geology and metallogeny of Jordanian uranium deposits. Appl Earth Sci 124(2):63–77

    Article  Google Scholar 

  • Armstrong M (1998) Basic linear geostatistics. Springer, Berlin, p 153

    Book  Google Scholar 

  • Barnes R (1991) The variogram sill and the sample variance. Math Geol 23(4):673–678

    Article  Google Scholar 

  • Bleines C, Bourges M, Deraisme J, Geffroy F, Jeanne N, Lemarchand O, Perseval S, Poisson J, Rambert F, Renard D, Touffait Y, Wagner L (2013) ISATIS software. Geovariances, Ecole des Mines de Paris, Paris

    Google Scholar 

  • Chauvet P (1982) The variogram cloud. 17th APCOM symposium, pp 757–764

    Google Scholar 

  • Chiles J-P, Delfiner P (1999) Geostatistics: modelling spatial uncertainty. Wiley, New York, p 695

    Book  Google Scholar 

  • Chu J (1993) XGAM: a 3D interactive graphic software for modelling variograms and cross variograms under conditions of positive definiteness. In: Stanford Centre for reservoir forecasting, report 6, Stanford, California

    Google Scholar 

  • David M (1977) Geostatistical ore reserve estimation. Elsevier, Amsterdam, p 364

    Google Scholar 

  • Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York, p 483

    Google Scholar 

  • Goulard M (1989) Inference in a coregionalization model. In: Armstrong M (ed) Geostatistics, vol 1. Kluwer, Dordrecht, pp 397–408

    Chapter  Google Scholar 

  • Goulard M, Voltz M (1992) Linear coregionalization model: tools for estimation and choice of cross-variogram matrix. Math Geol 24(3):269–286

    Article  Google Scholar 

  • Guibal D (2001) Variography, a tool for the resource geologist. In: Edwards AC (ed) Mineral resource and ore reserve estimation – the AusIMM guide to good practice. AusIMM, Melbourne, pp 85–90

    Google Scholar 

  • Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New York, p 561

    Google Scholar 

  • Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic, New York, p 600

    Google Scholar 

  • Olea RA (ed) (1991) Geostatistical glossary and multilingual dictionary. Oxford University Press, New York, p177

    Google Scholar 

  • Sommerville B, Boyle C, Brajkovich N, Savory P, Latscha AA (2014) Mineral resource estimation of the Brockman 4 iron ore deposit in the Pilbara region. Appl Earth Sci 123(2):135–145

    Article  Google Scholar 

  • Wackernagel H (2003) Multivariate geostatistics: an introduction with applications, 3rd edn. Springer, Berlin, p388

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. MASSA geoservices, Mount Claremont, WA, Australia

    Marat Abzalov

  2. Centre for Exploration Targeting (CET), University of Western Australia, Crawley, WA, Australia

    Marat Abzalov

Authors
  1. Marat Abzalov
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Abzalov, M. (2016). Variography. In: Applied Mining Geology. Modern Approaches in Solid Earth Sciences, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-39264-6_18

Download citation

Publish with us

Policies and ethics

Search

Navigation