A Cellular Automaton-Based Technique for Estimating Mineral Resources

Abstract

A significant contribution to the economic growth of a nation comes from the mineral industries. Therefore, the concentration of metallic or nonmetallic minerals in different regions of Earth’s crust is important to determine. The present paper studies the grade and thickness estimation of iron and coal deposits, respectively, by applying two-dimensional cellular automata (CAs). Krigging is a popular method for the estimation of mineral resources. However, krigging results in complex mathematical calculations if the number of sample points increases. Here, each cell of the cellular automaton (CA) is represented as a block. Using CAs, the grade values and thickness are estimated in a simpler and faster way. Two-dimensional CAs are used in this paper where the local rule is the ordinary krigging estimator function using the spherical variogram model. The total weight of iron as well as coal is calculated using the CA-based technique. A comparative analysis between the estimated weight of minerals and the actual extracted mineral is also given.