COMPARISON OF SEQUENTIAL GAUSSIAN SIMULATION AND SIMPLE KRIGING FOR GRADE PREDICTION

Abstract

Accurate prediction of grades plays an important role in the mining industry: differentiation of valuable ore and non-profitable waste material is a key step in Mine Planning. This paper delves into the comparison between sequential Gaussian simulation (SGS) and simple kriging methodologies concerning their efficacy in grade prediction and the classification of ore and waste materials. The study investigates the application of both systems to predict the ore grades within iron deposit. It investigates their abilities to accurately predict the spatial distribution of ore grades across varied geological formations. Furthermore, this research aims to ascertain whether SGS methods exhibit superior performance in classifying materials into ore and waste categories compared to traditional simple kriging systems. The findings of this study are expected to provide valuable insights into the strengths and limitations of SGS and simple kriging methods for grade prediction in mining operations. This comparative analysis aims to aid mining engineers and professionals in selecting the most effective methodology for optimizing resource delineation and decision-making processes in mining projects.