Abstract
HyLogger profile scanning is commonly utilised for drill-core logging but the limited scanning area may not detect all important geological features. The study presented in this paper aims to develop a mineral mapping solution for this core-logging process by leveraging the colour image captured during the scanning process. A machine-learning-based computer vision program was developed by implementing a k-means clustering and a global colour profiling algorithm. A suite of drill-core images was used to validate the developed program. Results indicate that there is a direct correlation between the mineral assemblage of a rock type and its colour specifications. The identified mineral type and relative abundance were comparable with HyLogger scan results.
Original language | English |
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Pages (from-to) | 1063-1073 |
Number of pages | 11 |
Journal | Australian Journal of Earth Sciences |
Volume | 66 |
Issue number | 7 |
DOIs | |
Publication status | Published - 3 Oct 2019 |
Externally published | Yes |
Keywords
- computer vision
- drill-core logging
- image processing
- k-means clustering
- machine learning
- mineral mapping