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
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:79852 |
| Date | 21 July 2022 |
| Creators | Contreras Acosta, Isabel Cecilia |
| Contributors | Gloaguen, Richard, Gutzmer, Jens, Benndorf, Jörg, Plaza, Antonio, Technische Universität Bergakademie Freiberg, Helmholtz Institute Freiberg for Resource Technology - Helmholtz-Zentrum Dresden-Rossendorf |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 10.1109/JSTARS.2019.2924292, 10.1109/JSTARS.2020.3011221, 10.3390/rs13122296 |
Page generated in 0.008 seconds