- AutorIn
- Isabel Cecilia Contreras Acosta Helmholtz Institute Freiberg for Resource Technology - Helmholtz-Zentrum Dresden-Rossendorf
- Titel
- Improving drill-core hyperspectral mineral mapping using machine learning
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:105-qucosa2-798522
- Datum der Einreichung
- 15.02.2022
- Datum der Verteidigung
- 06.07.2022
- Abstract (EN)
- 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.
- Verweis
- A Machine Learning Framework for Drill-Core Mineral Mapping Using Hyperspectral and High-Resolution Mineralogical Data Fusion
Link: https://ieeexplore.ieee.org/document/8758206
DOI: 10.1109/JSTARS.2019.2924292 - Drill-Core Hyperspectral and Geochemical Data Integration in a Superpixel-Based Machine Learning Framework
Link: https://ieeexplore.ieee.org/document/9146200
DOI: 10.1109/JSTARS.2020.3011221 - Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping
Link: https://www.mdpi.com/2072-4292/13/12/2296
DOI: 10.3390/rs13122296 - Freie Schlagwörter (DE)
- hyperspektral, maschinelles Lernen, Bohrkerne, Kartierung, Erkundung
- Freie Schlagwörter (EN)
- hyperspectral, machine learning, drill-cores, mineral mapping, mineral exploration
- Klassifikation (DDC)
- 550
- Normschlagwörter (GND)
- Bohrkern
- Bohrkernuntersuchung
- Geochemische Prospektion
- Datenerhebung
- Hyperspektraler Sensor
- Künstliche Intelligenz
- Lagerstätte
- Mineralisation
- Mineralischer Rohstoff
- Prospektion
- Geochemie
- Spektralanalyse
- Maschinelles Lernen
- Datenanalyse
- Datenauswertung
- Mineralbestimmung
- Lagerstättenkunde
- Mineralogie
- Hyperspektraler Sensor
- GutachterIn
- Dr. Richard Gloaguen
- Prof. Dr. Jörg Benndorf
- Prof. Dr. Antonio Plaza
- BetreuerIn Hochschule / Universität
- Prof. Dr. Jens Gutzmer
- BetreuerIn - externe Einrichtung
- Dr. Richard Gloaguen
- Den akademischen Grad verleihende / prüfende Institution
- Technische Universität Bergakademie Freiberg, Freiberg
- Sonstige beteiligte Institution
- Helmholtz Institute Freiberg for Resource Technology - Helmholtz-Zentrum Dresden-Rossendorf, Freiberg
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:105-qucosa2-798522
- Veröffentlichungsdatum Qucosa
- 21.07.2022
- Dokumenttyp
- Dissertation
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
- CC BY-NC-ND 4.0
- Inhaltsverzeichnis
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