Driven by the need to use mineral resources more sustainably, and the increasing complexity of ore deposits still available for commercial exploitation, the acquisition of quantitative data on mineralogy and microfabric has become an important need in the execution of exploration and geometallurgical test programmes. Hyperspectral drill-core scanning has the potential to be an excellent tool for providing such data in a fast, non- destructive and reproducible manner. However, there is a distinct lack of integrated methodologies to make use of these data through-out the exploration and mining chain. This thesis presents a first framework for the use of hyperspectral drill-core scanning as a pillar in exploration and geometallurgical programmes. This is achieved through the development of methods for (1) the automated mapping of alteration minerals and assemblages, (2) the extraction of quantitative mineralogical data with high resolution over the drill-cores, (3) the evaluation of the suitability of hyperspectral sensors for the pre-concentration of ores and (4) the use of hyperspectral drill- core imaging as a basis for geometallurgical domain definition and the population of these domains with mineralogical and microfabric information.:Introduction Materials and methods Assessment of alteration mineralogy and vein types using hyperspectral data Hyperspectral imaging for quasi-quantitative mineralogical studies Hyperspectral sensors for ore beneficiation 3D integration of hyperspectral data for deposit modelling Concluding remarks References

Considering the ever-growing global demand for raw materials and the complexity of the geological deposits that are still to be found, high-quality extensive mineralogical information is required. Mineral exploration remains a risk-prone process, with empirical approaches prevailing over data-driven strategy. Amongst the many ways to innovate, hyperspectral imaging sensors for drill-core mineral mapping are one of the disruptive technologies. This potential could be multiplied by implementing machine learning. This dissertation introduces a workflow that allows the use of supervised learning to map minerals by means of ancillary data commonly acquired during exploration campaigns (i.e., mineralogy, geochemistry and core photography). The fusion of hyperspectral with such ancillary data allows not only to upscale to complete boreholes information acquired locally, but also to enhance the spatial resolution of the mineral maps. Thus, the proposed approaches provide digitally archived objective maps that serve as vectors for exploration and support geologists in their decision making.:List of Figures xviii List of Tables xix List of Acronyms xxi 1 Introduction 1 1.1 Mineral resources and the need for innovation . . . . . . . . . . . . . 2 1.2 Spectroscopy and hyperspectral imaging . . . . . . . . . . . . . . . . 5 1.2.1 Imaging spectroscopy ....................... 6 1.2.2 Spectroscopy of minerals ..................... 8 1.2.3 Mineral mapping.......................... 12 1.2.4 Mineral mapping in exploration ................. 15 1.2.5 Drill-core mineral mapping.................... 16 1.3 Machine learning .............................. 19 1.3.1 Supervised learning for drill-core hyperspectral data . . . . . 20 1.4 Motivation and approach ......................... 22 2 Hyperspectral mineral mapping using supervised learning and mineralogical data 25 Preface ....................................... 25 Abstract....................................... 26 2.1 Introduction ................................. 27 2.2 Data acquisition............................... 30 2.2.1 Hyperspectral data......................... 30 2.2.2 High-resolution mineralogica ldata . . . . . . . . . . . . . . . 31 2.3 Proposed system architecture ....................... 33 2.3.1 Re-sampling and co-registration ................. 33 2.3.2 Classification ............................ 35 2.4 Experimental results ............................ 36 2.4.1 Data description .......................... 36 2.4.2 Experimental setup......................... 37 2.4.3 Quantitative and qualitative assessment . . . . . . . . . . . . . 37 2.5 Discussion.................................. 40 2.6 Conclusion.................................. 42 3 Geochemical and hyperspectral data integration 45 Preface ....................................... 45 Abstract....................................... 46 3.1 Introduction ................................. 47 3.2 Basis for the integration of geochemical and hyperspectral data . . . 50 3.3 Proposed approach ............................. 51 3.3.1 Geochemical data labeling..................... 51 3.3.2 Superpixel segmentation ..................... 53 3.3.3 Classification ............................ 53 3.4 Experimental results ............................ 54 3.4.1 Data description .......................... 54 3.4.2 Data acquisition........................... 55 3.4.3 Experimental setup......................... 55 3.4.4 Assessment of the geochemical data labeling . . . . . . . . . . 58 3.4.5 Quantitative and Qualitative Assessment . . . . . . . . . . . . 58 3.5 Discussion.................................. 61 3.6 Conclusion.................................. 63 4 Improved spatial resolution for mineral mapping 65 Preface ....................................... 65 Abstract....................................... 66 4.1 Introduction ................................. 67 4.2 Methods: Resolution Enhancement for Mineral Mapping . . . . . . . 69 4.2.1 Hyperspectral Resolution Enhancement . . . . . . . . . . . . . 69 4.2.2 Mineral Mapping.......................... 71 4.2.3 Supervised Classification ..................... 71 4.3 Case Study.................................. 72 4.3.1 Data Acquisition .......................... 72 4.3.2 Resolution Enhancement Application . . . . . . . . . . . . . . 74 4.3.3 Evaluation of the Resolution Enhancement . . . . . . . . . . . 75 4.4 Results .................................... 76 4.4.1 Mineral Mapping.......................... 76 4.4.2 Supervised Classification ..................... 77 4.4.3 Validation .............................. 80 4.5 Discussion.................................. 82 4.6 Conclusions ................................. 84 5 Bibliography 92