Abstract:
The objective of the thesis was two-fold: to investigate the potential of implicit modelling techniques for modelling geometallurgical parameters in mine planning and to generate synthetic geometallurgical data using Generative Adversarial Networks (GAN) models. Several geometallurgical parameters, including ore grade, Bond work index (BWI), rod mill index, rock quality designation (RQD), drop weight index (DWI), Axb, and Abrasion index (Ai), were modelled in this thesis using implicit and geostatistical methods, and their results were compared.
To generate synthetic geometallurgical data, GAN-based models were used, namely, Conditional Tabular Generative Adversarial Network (CTGAN), Copula Generative Adversarial Network (CopulaGAN), and Gaussian Copula. The process was conducted in Python® using a Synthetic Data Vault (SDV) library, based on original geometallurgical data obtained from previous research papers, theses, and online databases. Geometallurgical block models were produced using implicit (Radial Basis Function) and geostatistical (Ordinary Kriging) methods and compared. The following software packages were used in this study. Leapfrog® Geo, Microsoft® Excel®, and Microsoft® Paint®. The results of the Geometallurgical Block Model (GMBM) were compared using parameters of mine planning, such as the grade-tonnage curve and resource estimations.
In conclusion, the study found that the synthetic geometallurgical data generated in this research was of high quality and demonstrated that implicit modelling methods can improve the accuracy and efficiency of mine planning by modelling geometallurgical parameters. However, more research is recommended to explore other implicit-based methods, such as the potential field and the Hermite Radial Basis Function (HRBF).
