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An overview of the geostatistical toolkit is presented, from data analysis through estimation and simulation, with a focus on problems that typically arise in the assessment and development of coal deposits. Geostatistical procedures for the data analysis are described, leading to a discussion of the importance of spatial variation and the variogram. The most common geostatistical estimation procedure, ordinary kriging, is presented as an improvement to inverse-distance methods; two ways are presented of understanding kriging without recourse to the underlying mathematics. Estimation and simulation are compared and contrasted, and the benefits of a family of equally likely scenarios are covered. The paper concludes with brief summaries of the 16 additional papers in the International Journal of Coal Geology's Special Issue on Geostatistics, and provides two indexes to guide the reader to papers according to the problems they address and according to the tools they use.

The indicator conditional simulation technique provides stochastic simulations of a variable that (i) honor the initial data and (ii) can feature a richer family of spatial structures not limited by Gaussianity. The data are encoded into a series of indicators which then are used to estimate the conditional probability distribution (cpdf) of the variable under study at any unsampled location. Once the cpdf has been estimated, any particular simulated value is obtained by straightforward Monte-Carlo drawing. Each new simulated value is included in the conditioning data set so that the next simulated values at other locations be conditioned to it. This technique has the advantage over other more traditional techniques such as the turning bands method in that it is not multiGaussian related. The user has full control of the bivariate (2-point) statistics imposed on the simulated field instead of controlling a mere covariance model. The source code is provided in C according to the ANSI standard.