Mathematical Geology

, Volume 18, Issue 1, pp 93–117 | Cite as

Matheronian geostatistics—Quo vadis?

  • G. M. Philip
  • D. F. Watson


Components of geostatistical estimation, developed as a method for ore deposit assessment, are discussed in detail. The assumption that spatial observations can be treated as a stochastic process is judged to be an inappropriate model for natural data. Problems of semivariogram formulation are reviewed, and this method is considered to be inadequate for estimating the function being sought. Characteristics of bivariate interpolation are summarized, highlighting kriging limitations as an interpolation method. Limitations are similar to those of inverse distance weighted observations interpolation. Attention is drawn to the local bias of kriging and misplaced claims that it is an “optimal” interpolation method. The so-called “estimation variance,” interpreted as providing confidence limits for estimation of mining blocks, is shown to be meaningless as an index of local variation. The claim that geostatistics constitutes a “new science” is examined in detail. Such novelties as exist in the method are shown to transgress accepted principles of scientific inference. Stochastic modeling in general is discussed, and purposes of the approach emphasized. For the purpose of detailed quantitative assessment it can provide only prediction qualified by hypothesis at best. Such an approach should play no part in ore deposit assessment where the need is for local detailed inventories; these can only be achieved properly through local deterministic methods, where prediction is purely deductive.

Key words

deterministic “estimation variance” interpolation geostatistics kriging least-squares prediction ore deposit assessment probabilistic semivariogram statistical inference 


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Copyright information

© Plenum Publishing Corporation 1986

Authors and Affiliations

  • G. M. Philip
    • 1
  • D. F. Watson
    • 1
  1. 1.Department of Geology and GeophysicsThe University of SydneyAustralia

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