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

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%.

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

“…The study [41] used the association indicator matrix (AIM) and local binary pattern (LBP) texture analysis methods to get quantitative textural descriptors of drill core samples with relatively high accuracy of 84% and 88%, respectively, for AIM and 3D LBP. An automatic method for the classification of hematite textures in Brazilian iron ores according to their textural types through applying an AI technique for analyzing the images from a reflected light microscope and a digital camera is described in [78].…”

“…ML algorithms such as artificial neural network (ANN), support vector machine (SVM), regression tree (RT), and random forest (RF) are powerful data driven methods that are becoming extremely popular in such applications as the mapping of mineral prospectivity [26][27][28], mapping geochemical anomalies [29][30][31], geological mapping [32][33][34][35], drill-core mapping [36][37][38], and mineral phase segmentation for X-ray microcomputed tomography data [39][40][41].…”

3D Ore Characterization as a Paradigm Shift for Process Design and Simulation in Mineral Processing

“…Over the last decades, the development of X-ray Microcomputed Tomography (µCT) in geosciences has created a potential for 3D ore characterization free from stereological errors. Several studies have been devoted to evaluating the potential application of µCT for mineral characterization [14][15][16][17].…”

“…The projections are reconstructed to µCT images (slices) which are then stacked to 3D image of the ore sample. The stacked 3D images are often the raw data for further data processing and feature extraction [18][19][20], particle morphology [21], as well as ore texture [14,22]. Furthermore, with the additional depth of information offered by µCT systems, some other ore features such as mineral surface exposure [23,24] can also be extracted.…”

“…The potential application of µCT in drill core texture analysis has been subject of several investigations [14,22,34]. The principles of texture analysis used in these studies are quite commonplace in image processing communities, as such texture analysis is often used in face pattern recognition [35].…”