Spatial modelling and classification of altered coal using random forest-based methods at Moatize Basin, Mozambique

Abstract

Coal basins affected by intrusion pose distinctive challenges, such as the complexity of determining the extent of impacted coal from borehole cuttings and the potential for inaccuracies in the spatial modelling of coal quality parameters. It is more challenging in complex thinly bedded coal seams, characterised by multiple sub-layers of varying thickness and coal quality. This study employs machine learning techniques in the highly complex thinly bedded Permian-aged Chipanga coal seam in the Moatize Basin, Mozambique. The study aims to assess the efficacy of these techniques for the spatial modelling and classification of altered coals. The results demonstrate that the random forest-based classifier can discriminate between altered and fresh coal with a range of accuracy of 97%–100%. Furthermore, among the five spatial methods assessed, geographical quantile regression forest emerges as one of the top-performing approaches for assessing the impact of magmatic intrusions on coal properties within the Moatize Basin. Although previous studies have shown the ability of these methods for spatial modelling and coal classification on single coal seam, these techniques have not been used in complex thin-bedded coal seams. Therefore, the findings of this study highlight the implications of these methods in navigating the complexities of thin-bedded coal seams.