An automatic HyLoggerTM mineral mapping method using a machine-learning-based computer vision technique. Issue 7 (3rd October 2019)

• Record Type: Journal Article
• Authors: Liu, J.; Chen, W.; Muller, M.; Chalup, S.; Wheeler, C.
• Is Part Of: Australian journal of earth sciences. Volume 66:Issue 7(2019)
• Page Start: 1063
• Page End: 1073
• Publication Date: 2019-10-03
• Subjects: drill-core logging -- mineral mapping -- computer vision -- k-means clustering -- machine learning -- image processing; Earth sciences -- Australia -- Periodicals; Earth sciences -- Periodicals; Geology -- Australia -- Periodicals; Geology -- Periodicals; 559.405
• DOI: 10.1080/08120099.2019.1600167
• Languages: English
• ISSNs: 0812-0099

Abstract

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.