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Classification of Drill Core Textures for Process Simulation in Geometallurgy: Aitik Mine, Sweden
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering. EMerald Program.ORCID iD: 0000-0001-9823-1664
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis study employs textural classification techniques applied to four different data groups: (1) visible light photography, (2) high-resolution drill core line scan imaging (3) scanning electron microscopy backscattered electron (SEM-BSE) images, and (4) 3D data from X-ray microtomography (μXCT). Eleven textural classes from Aitik ores were identified and characterized. The distinguishing characteristics of each class were determined such as modal mineralogy, sulphide occurrence and Bond work indices (BWI). The textural classes served as a basis for machine learning classification using Random Forest classifier and different feature extraction schemes. Trainable Weka Segmentation was utilized to produce mineral maps for the different image datasets. Quantified textural information for each mineral phase such as modal mineralogy, mineral association index and grain size was extracted from each mineral map. 

Efficient line local binary patterns provide the best discriminating features for textural classification of mineral texture images in terms of classification accuracy. Gray Level Co-occurrence Matrix (GLCM) statistics from discrete approximation of Meyer wavelets decomposition with basic image statistical features[PK1]  (e.g. mean, standard deviation, entropy and histogram derived values) give the best classification result in terms of accuracy and feature extraction time. Differences in the extracted modal mineralogy were observed between the drill core photographs and SEM images which can be attributed to different sample size[PK2] . Comparison of SEM images and 2D μXCT image slice shows minimal difference giving confidence to the segmentation process. However, chalcopyrite is highly underestimated in 2D μXCT image slice, with the volume percentage amounting to only half of the calculated value for the whole 3D sample. This is accounted as stereological error.

Textural classification and mineral map production from basic drill core photographs has a huge potential to be used as an inexpensive ore characterization tool. However, it should be noted that this technique requires experienced operators to generate an accurate training data especially for mineral identification and thus, detailed mineralogical studies beforehand is required.

Place, publisher, year, edition, pages
2017. , p. 71
Keywords [en]
ore texture, texture classification, Machine Learning, drill core photography, scanning electron microscope, X-ray microtomography, mineral mapping, stereology
National Category
Geosciences, Multidisciplinary Metallurgy and Metallic Materials
Identifiers
URN: urn:nbn:se:ltu:diva-65207OAI: oai:DiVA.org:ltu-65207DiVA, id: diva2:1134455
External cooperation
Aitik Mine, New Boliden
Subject / course
Student thesis, at least 30 credits
Educational program
Natural Resources Engineering, master's level (120 credits)
Presentation
2017-06-19, F232, Luleå University of Technology, Luleå, 08:50 (English)
Supervisors
Examiners
Projects
Primary Resource Efficiency by Enhanced Prediction (PREP)Center for Advanced Mining and Metallurgy (CAMM)Available from: 2017-08-22 Created: 2017-08-19 Last updated: 2017-11-08Bibliographically approved

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CiteExportLink to record
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Citation style
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