Hello List FYI, a few comments related to the ongoing discussion re Geostats Scam.
Stephen Henley makes some valid points on the shortcomings of geostatistics. In particular, I have also been troubled by the application of models "invariant under spatial translation" to real world data. "The proper selection of data" for estimation is considered by many to be the fundamental mantra of ore resource estimation. Typically, the selection of data for variography and the estimation of block model grades is controlled through manually interpreted models of lithology, alteration, grade shells, structural domains and so on. Although these models are practical at the scale of the deposit, they are not practical at local scales where local scale is defined by distances as short as 10 m, over which abrupt changes in the direction of geologic trends such as grade, fault direction, fracture patterns, and rock type contacts are observed. Obviously, it is not practical to control the selection of data at this scale using manually interpreted models. However, the good news is that the proper selection of data for kriging can be achieved at a local scale by aligning an anisotropic search ellipsoid with the local geologic trend(s) on a block by block basis. The idea is simple. Before each block is estimated, the anisotropy ratios of the local search neighborhood are adjusted and the axes aligned with local trends in the data. The method has come to be known as local anisotropy kriging or LAK. The results are remarkable. I have an example where LAK applied to grade control out performs ordinary kriging by reducing dilution and ore loss. You can read more about this relatively new implementation of an old idea by visiting www.isaaks.com and clicking on "Geo Docs". I would also add a note regarding popular geostatistical misconceptions, and there are several. For example, Krige, Deutsch, Vann and many others have published papers admonishing conditional bias. However, in mining applications (where geostatistics has its roots) conditional bias is actually irrelevant, unless the estimates are used for grade control. See "The Kriging Oxymoron" at www.isaaks.com "Geo Docs" for a peer reviewed paper on the subject. I recently read a paper by J Vann, S Jackson, and O Bertoli (2003) that actually proposes a method for designing the kriging search neighborhood based on minimizing conditional bias. Horrifying - do they not realize that such practice actually increases the estimation error of the predicted tonnes and grade above cutoff? This paper is probably the worst (best?) example I have seen of a faulty misconception in 25 years of ore resource assessment. One can almost understand why geostatistics might be labeled a scam. However, I'm not sure I agree with Stephen where he appears to suggest that the "vast array of methods and an array of intensely mathematical published papers" are somewhat responsible for providing the means to deliver "whatever the client wants". The difference between a professional and an amateur practitioner is knowing which is the correct tool for the job and how to use it properly. I'd argue that a packed toolbox is not the problem but rather, the inexperienced or dishonest practitioner is the problem. And finally, I have done some research on the subject of computing "weighted variances" since a number of "weighted variance" estimators can be found in the literature including Mr. Merks' version. Each of the estimators I found provided a different estimate of the weighted variance and to make matters worse, not one was shown to be a valid statistical estimator -- they were simply stated without derivation(see footnote). However, the good news is that with careful work and with help from Colin Daly and Don Myers, I now have the mathematical derivation of an unbiased estimator for the population variance given a sample of N (iid) observations with associated weights. Now, it turns out that in spite of all the huffing and puffing by our colleague Mr. Merks, the "weighted variance" estimator that he so loudly champions is biased under the iid model! Perhaps Mr. Merks' time could have been better spent looking for an unbiased variance estimator rather than stalking the "lost variance". :-) A copy of this work will be made available to the list following publication. ---------------------------------------------------------------------------- -------------------- Derivation -- A logical or mathematical process indicating through a sequence of statements that a result such as a theorem or a formula necessarily follows from the initial assumptions. Edward Isaaks Reference J Vann, S Jackson and O Bertoli, (2003), "Quantitative Kriging Neighbourhood Analysis for the Mining Geologist - A Description of the Method With Worked Case Examples", 5th International Mining Geology Conference, AusIMM.
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