Application of kriging and simulated annealing for spatial variability modelling of a coal seam

Abstract

Spatial variability models for characterisation of coal seams may be achieved by use of geostatistical techniques, namely kriging and conditional simulation. While kriging provides only one realisation of a true phenomenon, conditional simulation techniques generate a family of equally probable images, of which the ones closest to the reality are chosen by conditioning the simulated values to be equal to known values at sampling locations. The techniques are considered to be more appropriate and result oriented for spatial variability modelling as compared with kriging that produces smoothed estimates while minimising the estimation variance. Among various simulation techniques, simulated annealing with its powerful algorithm is a more well known technique for geostatistical imaging on a node-by-node basis. In the present study, geostatistical imaging of a coal seam in an Indian coalfield has been carried out for spatial variability modelling employing kriging as well as simulated annealing techniques. It is observed that the simulation technique provides a better framework to geostatistical imaging for spatial variability modelling of the coal seam and the results of simulation are more realistic as compared with kriging.