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

Title: An automatic HyLogger™ mineral mapping method using a machine-learning-based computer vision technique
Creator: Liu, J.; Chen, W.; Muller, M.; Chalup, S.; Wheeler, C.
Relation: Australian Journal of Earth Sciences Vol. 66, Issue 7, p. 1063-1073
Publisher Link: http://dx.doi.org/10.1080/08120099.2019.1600167
Publisher: Taylor & Francis
Resource Type: journal article
Date: 2019
Description: 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.
Subject: drill-core logging; mineral mapping; computer vision; k-means clustering; machine learning; image processing
Identifier: http://hdl.handle.net/1959.13/1415234
Identifier: uon:36877
Identifier: ISSN:0812-0099
Language: eng
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