Beyond ordinary kriging: Non-linear geostatistical methods in practice / За ординарного кригинга: Нелинейные геостатистические методы на практике

Many geostatistical variables have sample distributions that are highly positively skewed. Because of this, significant deskewing of the histogram and reduction of variance occurs when going from sample to block support, where blocks are of larger volume than samples. When making estimates in both mining and non-mining applications we often wish to map the spatial distribution on the basis of block support rather than sample support. The SMU or selective mining unit in mining geostatistics refers to the minimum support upon which decisions (traditionally: ore/waste allocation decisions) can be made. The SMU is usually significantly smaller than the sampling grid dimensions, in particular at exploration/feasibility stages. Linear estimation of such small blocks (for example by inverse distance weighting – IDW – or ordinary Kriging – OK) results in very high estimation variances, i.e. the small block linear estimates have very low precision. A potentially serious consequence of the “small block linear estimation” approach is that the grade-tonnage curves are distorted  i.e. prediction of the content of an attribute above a cut-off based on these estimates is quite different to that based on true block values. Assessment of project economics (or other critical decision making) based on such distorted grade-tonnage curves will be riskier than necessary <...>