Hyperspectral drill-core scanning in geometallurgy
| Autor: | Tusa, Laura |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
hyperspectral
drill-core geometallurgy mineralogy exploration Hyperspektralanalyse Bohrkerne Geometallurgie Mineralogie Exploration info:eu-repo/classification/ddc/550 ddc:550 Bohrkern Bohrkernuntersuchung Geochemische Prospektion Datenerhebung Hyperspektraler Sensor Künstliche Intelligenz Lagerstätte Mineralisation Mineralischer Rohstoff Prospektion Geochemie Spektralanalyse Maschinelles Lernen Datenanalyse Datenauswertung Mineralbestimmung Lagerstättenkunde |
| Druh dokumentu: | Text<br />Doctoral Thesis |
| Popis: | 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 |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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