Fractal And Conditional Simulation Of Sulfur Distribution In A Coal Seam

Forecasting mined grade variability from exploration information is a challenging task in mine planning. For effective planning of activities, such as workplace scheduling, ore grade quality control, etc., development of realistic numerical simulation models of in situ variability from exploration information is an essential first step. Geostatistical conditional simulation (GCS) is one of the well known methods to develop such simulations. Recently, stochastic fractal models based on the assumptions of scale-invariance and self-similarity have been used for developing simulations of several natural phenomena. It has been suggested that these models might permit the generation of short-scale variability information from exploration data with greater fidelity when the data satisfies the attendant assumptions. In this paper, stochastic fractal models are reviewed, and their relationship to geostatistical variogram models is examined. A comparison of the two simulation techniques has been performed using coal sulfur data from a mined-out coal property. The results of this comparative analysis are also presented in the paper.