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A variogram is evaluated at various lag distances h separated by the variogram increment d.
Table 2 shows the semi-variance values of these locations at lag distances of selected 1,000 m chosen.
The experimental test parameters considered are number of bolts, pitch and shear lag distances and ratio of connected leg length to unconnected leg length.
The program, here coined as textural neural network (TEXTNN), comprises the computation of variogram maps in the frequency domain for specific lag distances in the neighborhood of a pixel.
Feature vectors are built with textural information composed of semivariance values at these lag distances and, moreover, with histogram measures of mean, standard deviation and weighted-rank fill ratio.
Finally, MAF is the most suitable approach for doing simulation of iron ore deposits, where MAF can be implemented in any lag distances and has no specific requirements for the number of samples.
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The optimum lag distance has been found to be 2,000 m in case of spherical model.
The functional relationship between the variance and the lag distance is computed mathematically by Eq. 4 (Webster and Oliver 2007).
It can also be seen that as the lag distance increases, the RMSE between the semi-variance values increases.
Soil WC, cotton lint yield, N uptake, and clay content were crosscorrelated with site elevation across a lag distance of ±30 40 m.
A variogram measures the variance between pairs of points at a distance of "h" (lag distance) in terms of function in "h".
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