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Travaux de D. G. Krige sur l’´evaluation des gisements dans les mines d’or sud-africaines. Annales des Mines, Paris, vol. 12, p. 3-49. 5. Journel, A. G. 1982. The indicator approach to estimation of spatial distributions. In: Johnson, T. , and Barnes, R. , eds, Proceedings of the 17th APCOM International Symposium, Society of Mining Engineers of the AIME, New York, p. 793-806. 6. Krige, D. , 1951, A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of the Chemical, Metallurgical and Mining Society of South Africa, dec.

The same question formulated on the dispersion variance of samples within a given panel, in linear geostatistics, would generally receive a negative answer for skewed distributions. Rich panels would generally contain richer and more variant samples: this corresponds to a proportional effect (between the variance and the mean, or a function of the mean), or in statistical language, a heteroscedasticity. This does not imply non-stationarity as is often thought: exponentiating a 1D stationary autoregressive Gaussian process yields to a stationary lognormal process presenting such a proportional effect.

Are related to the “spatial structure” represented by the variogram. But this will be more conveniently developed later within the framework of Random Function. 3 Intrinsic approach versus transitive approach When developing linear geostatistics, Matheron distinguished two approaches, the transitive approach (seldom used), and the intrinsic approach. In the transitive approach, the phenomenon to be studied (orebody, fish stock. . ) is supposed to be known, for instance, on a regular rectangular grid defined by its orientation, its mesh size, and its origin.

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