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INTRODUCTION TO GEOSTATISTICS And VARIOGRAM ..

08/01/2018 · The use of geostatistics requires at least that the intrinsic hypothesis be satisFed

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1 INTRODUCTION TO GEOSTATISTICS And VARIOGRAM ANALYSIS ..

The penalty paid for such relaxation of the stationarity assumption in geostatistical models which have been developed to use the weakest stationarity assumptions is that they are all much more difficult to use and more difficult to understand, and also - like universal kriging - have methodological problems such as the difficulty of obtaining a valid semivariogram model.

29/07/2014 · INTRODUCTION TO GEOSTATISTICS ..

Without a semivariogram - which requires the intrinsic form of stationarity (or a simple transformation to it, such as universal kriging purports to offer) - there is no geostatistics.

Geostatistics in Hydrology: Kriging interpolation

Ordinary kriging, the starting point in the development of other geostatistical techniques, has a number of serious limitations, chief among w ...

Geostatistics have become the dominant tool for probabilistic estimation of properties of heterogeneous formations at points where data are not available. Ordinary kriging, the starting point in development of other geostatistical techniques, has a number of serious limitations, chief among which is the intrinsic hypothesis of the (second order) stationarity of the underlying random field. Attempts to overcome this limitation have led to the development of ever more complex flavors of kriging. We pursue an opposite strategy that consists of finding the simplest possible technique that is adequate for the task of facies delineation. Guided by the principle of parsimony, we identify Nearest Neighbor classification (NNC) as a viable alternative to geostatistics among deterministic techniques. We demonstrate that when used for the purpose of facies delineation, the NNC, which has no fitting parameters and operational assumptions, outperforms indicator kriging, which has several parameters.

Ordinary kriging, the starting point in the development of other geostatistical techniques, has a number of serious limitations, chief among which is the intrinsic hypothesis of the (second-order) stationarity of the underlying random field.

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Also, like non-geostatistical methods(though not as severely), ordinary kriging sometimes overestimates thegrade in skewed distributions.

There are a few important circumstances under which geostatistics can be applied even if the intrinsic hypothesis does not hold. Of special interest is the situation when the regionalized variable is not first-order stationary because the drift has a systematic trend, as shown in Figure 2.1. Removing the drift from the regionalized variable results in a difference called the residual. Regionalized variable theory is still applicable if the intrinsic hypothesis holds for the residuals.

In the specialized language used in geostatistics, M() and γ() are referred to as the drift and the semivariance or intrinsic function. The units of M() are the same as the units of Z(), and the units of γ() are the square of the units of Z(). Provided the intrinsic hypothesis is met, both moments can be estimated.

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The presence of a trend in the data invalidates this hypothesis

Ordinary kriging, the starting point in development of othergeostatistical techniques, has a number of serious limitations, chiefamong which is the intrinsic hypothesis of the (second order)stationarity of the underlying random field.

referred to as the intrinsic hypothesis…

Ordinary kriging, the starting point in the development ofother geostatistical techniques, has a number of serious limitations,chief among which is the intrinsic hypothesis of the (second-order)stationarity of the underlying random field.

The Importance of Domaining and Stationarity to Geologists

Geostatisticians have sometimes tried to overcome this by asserting that there might be a ‘proportional effect' (local variance is proportional to local mean value), but this would hold true only for lognormal distributions, which are usually inappropriate (indeed this is why lognormal kriging is relatively little used).

of the data and utilise geostatistics to interpolate the various ..

Ordinary kriging, the starting point in the development of other geostatistical techniques, has a number of serious limitations, chief among which is the intrinsic hypothesis of the (second-order) stationarity of the underlying random field.

the presence of a trend in the data invalidates this hypothesis

Fytas et al (1990) also statedthat parametric geostatistics (ordinary kriging) performs well in depositswith a coefficient of variation close to (or less than) one.

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