###### Abstract  
The estimation of fuel characteristics and spatial variability in multi-seam coal deposits is of great significance for the optimal mine planning and exploitation, as well as for the optimization of the corresponding power plants operation. It is mainly based on the quality properties of the coal (i.e., Lower Calorific Value (LCV), ash content, CO\({}_{2}\), and moisture). Even though critical, these properties are not always measured in practice for all available borehole samples, or, they are generally estimated by using non-parametric statistics. Therefore, spatial modeling of LCV can become problematic due to the limited number of data. Thus, the use of other available correlated attributes might be helpful. In this research, techniques of multivariate geostatistics were used to estimate and evaluate the spatial distribution of quality properties in a multi-seam coal deposit, with special reference to the LCV. More specifically, kriging, cokriging, and Principal Component Analysis (PCA) techniques were tested in a case study as estimators of the LCV, using an extensive set of borehole data from the South Field lignite mine in Ptolemais, Greece. The research outcomes show that the application of kriging with two PCA factors and the use of inverse transform result in the best LCV estimates. Moreover, cokriging with two auxiliary variables gives more accurate values for a LCV estimate, in relation to the kriging technique. The research outcomes could be considered significant for the coal mining industry, since the use of correlated quality attributes for the estimation of LCV may contribute to a reduction of the estimation uncertainty at no additional drilling cost. geostatistical modeling; ordinary kriging; cokriging; principal component analysis; multi-seam coal deposits; quality parameters 2020 10 620  
process combines with spatial block modeling, as well as with geologic modeling in order to estimate the spatial variability of the coal reserves and the corresponding energy content. The main criteria used for the formation of the exploitable compositing coal blocks include the minimum thickness of coal blocks and waste layers that can be selectively excavated by the mining equipment, the cut-off quality properties of the run of mine coal (e.g., maximum ash content and minimum Lower Calorific Value (LCV)) and the dilution with waste and mining lignite loss at the top and bottom of each block. The composite blocks of exploitable coal may include thin waste layers, while the blocks of waste material may include thin coal seams which cannot be selectively excavated by the available equipment. The composition of seams into blocks of exploitable coal and waste material is a computer-aided iterative process based on suitable algorithms [3,4]. The analysis of the compositing process indicates that the accurate determination of the exploitable coal blocks, based on the original drill-hole data, and the spatial modeling of a coal deposit have a significant impact on the successful strategic planning of the corresponding mining project [5]. In continuous surface mining projects for the exploitation of multi-seam coal deposits, the investigation of the parameters that affect the coal deposit modeling and the evaluation results are significant, considering the complicated and continuously varying mining conditions and the requirements for highly efficient exploitation technologies and enhanced recovery of the mineral resources [2]. In such projects, there is an inherent uncertainty mainly related to the spatial variability of the mineral deposit parameters. This uncertainty should be taken into account in all stages of mine planning and design as well as in production scheduling and the economic evaluation of the projects [6].