An automatic HyLogger^TM mineral mapping method using a machine-learning-based computer vision technique

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.

Keywords:

• drill-core logging
• mineral mapping
• computer vision
• k-means clustering
• machine learning
• image processing

Acknowledgements

The authors are also grateful for the technical and financial assistance provided by the Geological Survey of New South Wales. The authors would like to thank Dr Karsten Laukamp and Dr Peter Mason for their valuable comments to improve the manuscript

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by the Chongqing Fundamental Frontier Research Plan of China [Nos. cstc2017jcyjA1327 and cstc2017jcyjAX0266]. The Geological Survey of New South Wales provided financial assistance.