Coal resource estimation using Gaussian copula

Abstract

Coal resource estimation is an important part of coal resource modeling. Kriging, inverse distance weighting and nearest neighborhood are traditional methods used in coal resource estimation. These methods are based on weighted average of local data values but data values are not involved in determination of the weights. For example, kriging uses variogram function, inverse distance weighting Euclidian distance and nearest neighborhood the nearest data. In this study an estimation method based on Gaussian copula is suggested which combines sampling configuration and local data values. The method is applied to coal field located in Thrace region which is at the northwest side of the Turkey. Lower Calorific Value is the variable under study. Following the construction of database and geological solid model of the coal seam, the mean calorific value of each block is estimated by Gaussian copula. The copula estimates are compared with the kriged estimates. The results show that the estimation with Gaussian copula captures local variability well and produces estimates with less amount of smoothing than kriging.