Automated recognition of drill core textures: A geometallurgical tool for mineral processing prediction

作者：Perez Barnuevo Laura^*; Levesque Sylvie; Bazin Claude

来源：Minerals Engineering, 2018, 118: 87-96.

DOI：10.1016/j.mineng.2017.12.015

• Rock texture automated recognition
• Drill core characterization
• Digital image analysis
• Geometallurgy

In a previous work, the use of drill core texture as a geometallurgical indicator was explored for the Mont -Wright iron ore deposit. At this deposit, the link between texture and mineral performance during comminution and heavy liquid separation was assessed by laboratory tests. Additionally, the micro-texture associated to each macro-texture was characterized by Mineral Liberation Analyzer (MLA). As a result, a classification of drill core textures calibrated to mineral processing performance was established.
To integrate the ore mesotexture into predictive block models, a core logging tool for automated textural pattern recognition is being developed. This paper presents the first step in this development: a methodology for the automated recognition of drill core textures. The proposed methodology is based on 2-D digital image analysis of drill cores. Texture information is extracted from digital images using gray level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM). Based on the information provided by these two methods, images were classified into six texture categories using multivariate discriminant analysis. A high classification success was obtained: 88% of the drill core images were correctly classified into their textural pattern category.

• 出版日期2018-3-15