MMIJ Annual Meeting

The main sources of uncertainty in geological studies are scarcity and biased distribution of available data as well as the geological heterogeneity in the area of interest. Understanding this last factor is paramount for any project aiming at modelling mineral resources. To succeed in such goal, the team of specialists usually puzzles over to define the geological units and their geometries to estimate the extensions of mineralized zones in subsurface. Machine learning techniques offer a wide range of tools, e.g., clustering analysis, to optimize and speed up workflows in mining planning. The study tries to combine an unsupervised learning algorithm (agglomerative hierarchical clustering) and a conditional geostatistical simulation (truncated-Gaussian simulation) to consider the spatial correlation of the samples and provide a framework for checking existing domains. Such workflow is employed to data collected in a low-sulfidation epithermal deposit, where gold is the target resource. It is hosted in veins and poses an additional challenge because they are conditioned by geological structures. The proposed methodology satisfactorily highlights the mineralized zones as well as their extensions in the depth direction and shed light on the mechanisms of formation of the studied deposit.