Textural Quantification and Classification of Drill Cores for Geometallurgy: Moving Toward 3D with X-ray Microcomputed T
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https://doi.org/10.1007/s11053-020-09685-5
Original Paper
Textural Quantification and Classification of Drill Cores for Geometallurgy: Moving Toward 3D with X-ray Microcomputed Tomography (lCT) Pratama Istiadi Guntoro ,1,3 Yousef Ghorbani,1 Alan R. Butcher,2 Jukka Kuva,2 and Jan Rosenkranz1 Received 27 January 2020; accepted 27 April 2020
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 (lCT) 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%. KEY WORDS: X-ray computed micro-tomography (lCT), Machine learning, Texture quantification, Local binary pattern, Co-occurrence matrices.
INTRODUCTION Geometallurgy can be referred to as the establishment of a link between geology and downstream processes with the aim to maximize economical value, reduce production risks, and guide the managerial decision-making process (Dominy et al. 2018; Lishchuk et al. 2020). A geometallurgical program is undertaken by creating a spatial model of the ore-
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Division of Minerals and Metallurgical Engineering, Lulea˚ University of Technology, 971 87, Lulea˚, Sweden. 2 Geological Survey of Finland GTK, PO Box 96 02151 Espoo, Finland. 3 To whom correspondence should be addressed; e-mail: [email protected]
body that predicts how each ore block behaves in a mineral processing circuit (Lund et al. 2013; Aasly and Ellefmo 2014). Such predictive models require the establishment of a link between the ore properties (mineralogy and textures) to the process outputs, which can be done by geometallurgical tests (Mwanga et al. 2017; Lishchuk et al. 2019) or predictive process models (Koch et al. 2019). The importance of mineral textures in relation to the mineral processing behavior of different ore samples has been underlined by several researchers (Lund et al. 2015; Tungpalan et al. 2015; Pe´rezBarnuevo et al. 2018a). For example, the information about grain size can be used to predict target liberation size of the minerals of interest (Vizcarra et al. 2010; Evans et al. 2015). Textural patt
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