Geostatistical, Spectral and Fractal Simulation of Sulphur Distribution in a Coal Seam

In this paper, a comparative performance evaluation of geostatistical, spectral, and fractal methods is made for short-scale variability prediction. The algorithms compared are: sequential Gaussian simulation method; a random fractal algorithm based on the generalised stochastic subdivision method; and a spectral simulation approach adopted for direct multi-dimensional simulation. The data for this study have been obtained from a mined out coal property. As this property was being mined, a 7315 m by 7315 m (24000 ft by 24000 ft) coal block was extensively sampled at an approximate spacing of 107 m (350 ft) and the 60-ton samples were analysed for sulphur in the 1.50 float gravity fraction. This data set is used here with the three methods to generate the simulated realisations, which are analysed for semivariogram reproduction and for simulation errors. Although all three methods converge to the true semivariogram in an average sense, the coefficient of variation of the average semivariogram for the Fourier integral method is minimum. The realisations generated by the sequential Gaussian method have minimum mean simulation error. The choice of a simulation method based on variability reproduction and simulation errors remains difficult.