A Cellular Automata-Based Approach on Assessment of Thickness of Stratified Mineral Deposits

Abstract

There has always been a large contribution of the mineral industry to a nation’s economical growth. Various characteristics of mineral deposits, such as grade, thickness, and density are estimated by different geostatistical methods like kriging, copula, distance weighting, and conditional simulation methods. Two popular distance weighting methods are inverse distance and inverse squared distance weighting methods. In the present work, we estimate the thickness of stratified deposit, that is, coal seam. The results obtained by directly applying the inverse distance method and inverse squared distance method are shown in the paper. Two cellular automata (CAs) with rules as the inverse distance weighting and inverse squared distance weighting are used in the study to estimate thickness of coal seam. It is observed that the use of CA in the estimation gives similar results as the other geostatistical estimation methods. However, cellular automata greatly simplifies the techniques of estimation and provides output in lesser time. The time taken by directly applying the distance weighting methods and the CAs is provided in the paper.