期刊
NATURAL RESOURCES RESEARCH
卷 29, 期 6, 页码 3547-3565出版社
SPRINGER
DOI: 10.1007/s11053-020-09685-5
关键词
X-ray computed micro-tomography (mu CT); Machine learning; Texture quantification; Local binary pattern; Co-occurrence matrices
资金
- European Unions Horizon 2020 research and innovation program [722677]
- Academy of Finland through the RAMI infrastructure project [293109]
- Lulea University of Technology
- Marie Curie Actions (MSCA) [722677] Funding Source: Marie Curie Actions (MSCA)
- Academy of Finland (AKA) [293109, 293109] Funding Source: Academy of Finland (AKA)
Texture is one of the critical parameters that affect the process behavior of ore minerals. Traditionally, texture has been described qualitatively, but recent works have shown the possibility to quantify mineral textures with the help of computer vision and digital image analysis. Most of these studies utilized 2D computer vision to evaluate mineral textures, which is limited by stereological error. On the other hand, the rapid development of X-ray microcomputed tomography (mu CT) has opened up new possibilities for 3D texture analysis of ore samples. This study extends some of the 2D texture analysis methods, such as association indicator matrix (AIM) and local binary pattern (LBP) into 3D to get quantitative textural descriptors of drill core samples. The sensitivity of the methods to textural differences between drill cores is evaluated by classifying the drill cores into three textural classes using methods of machine learning classification, such as support vector machines and random forest. The study suggested that both AIM and LBP textural descriptors could be used for drill core classification with overall classification accuracy of 84-88%.
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