semivariogram

semivariogram

[‚sem·i′ver·ē·ə‚gram]
(statistics)
A mathematical function used to quantify the dissimilarity between groups of values.
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The empirical semivariogram is then fitted using spatial variability models such as spherical, exponential, circular and, gaussian etc.
Dermanis (1984) noted that the LSC with a priori modelled covariance is similar to the kriging prediction method (with semivariogram modelling).
The spatial dependence of noise levels inside the facility during the nursery phase was verified by fitting semivariogram and interpolation by ordinary kriging.
To verify the spatial variability of variables over time, the results were analyzed by geostatistical methods of semivariogram analysis (Vieira, 2000).
Usually, the evaluation of this variability is done using the experimental semivariogram and; consequently, of the estimation of the model parameters of the semivariogram, which, in most cases, are the nugget effect, the contribution, the sill and the range (SEIDEL & OLIVEIRA, 2013, 2014).
Stochastic methods employ the spatial correlations between values at neighboring points and a semivariogram, which measures the spatial correlation as a function of the distance between data points, should be fitted.
The semivariogram function is used to quantitatively assess the spatial variability of a physical variable in geostatistics.
and gives us an elementary vision of the interaction that exists between Z([x.sub.1]) and Z([x.sub.2]), and the semivariogram, defined between the two random variables, is given by the expression
From a given data sample Z([x.sub.i]), the semivariogram [gamma](h) can be estimated according to (18).
Furthermore, no patterns were observed in the residual plots and in the semivariogram of Pearson residuals, suggesting that the final model adequately described the underlying data (Fig.
where [gamma](h) [[L.sup.2]] is the semivariogram, [Z.sub.i] [L] and [Z.sub.i+1] [L] are height of the 2D profile from baseline, and N is the number of pairs of Z at a lag distance h between them.
For the spatial distribution analysis of the production, the geostatistics was used, from the semivariogram modeling and the creation of kriging maps.