Textural Quantification and Classification of Drill Cores for Geometallurgy: Moving Toward 3D with X-ray Microcomputed Tomography (\xb5CT)

Pratama Istiadi Guntoro,Jan Rosenkranz,Jukka Kuva,Alan R. Butcher,Yousef Ghorbani

doi:10.1007/s11053-020-09685-5

Pratama Istiadi Guntoro, Jan Rosenkranz + Show 3 more

Open Access

https://doi.org/10.1007/s11053-020-09685-5

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Abstract

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 (µCT) 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%.

Highlights

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)
The importance of choosing the suitable classifier model is highlighted in this study, in which random forest (RF) classifier is better suited for association indicator matrix (AIM) features, while support vector machines (SVM) classifier is better suited for local binary pattern (LBP) features
The validation results indicated that a trained classifier could classify the drill core samples to their respective textural classes with high accuracy of 84% and 88%, respectively, for AIM and 3D LBP

Summary

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). Geologists have commonly used qualitative textural descriptors to describe different drill core samples. This practice imposes some limitations on the use of such textural descriptors in geometallurgy, as it is subject to human bias (Bonnici et al 2008; Vos 2017). When it comes to geometallurgy, the integration of textural information to predictive process models requires the texture information to be free of any subjectivity, which necessitates the use of computer vision to obtain quantifiable textural descriptors from the ore sample (PerezBarnuevo et al 2018b)

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