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Application of machine learning techniques in mineral phase segmentation for X-ray microcomputed tomography (µCT) data
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.ORCID-id: 0000-0002-8693-1054
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Geovetenskap och miljöteknik.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.ORCID-id: 0000-0002-5228-3888
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
Vise andre og tillknytning
2019 (engelsk)Inngår i: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 142, artikkel-id 105882Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

X-ray microcomputed tomography (µCT) offers a non-destructive three-dimensional analysis of ores but its application in mineralogical analysis and mineral segmentation is relatively limited. In this study, the application of machine learning techniques for segmenting mineral phases in a µCT dataset is presented. Various techniques were implemented, including unsupervised classification as well as grayscale-based and feature-based supervised classification. A feature matching method was used to register the back-scattered electron (BSE) mineral map to its corresponding µCT slice, allowing automatic annotation of minerals in the µCT slice to create training data for the classifiers. Unsupervised classification produced satisfactory results in terms of segmenting between amphibole, plagioclase, and sulfide phases. However, the technique was not able to differentiate between sulfide phases in the case of chalcopyrite and pyrite. Using supervised classification, around 50–60% of the chalcopyrite and 97–99% of pyrite were correctly identified. Feature based classification was found to have a poorer sensitivity to chalcopyrite, but produced a better result in segmenting between the mineral grains, as it operates based on voxel regions instead of individual voxels. The mineralogical results from the 3D µCT data showed considerable difference compared to the BSE mineral map, indicating stereological error exhibited in the latter analysis. The main limitation of this approach lies in the dataset itself, in which there was a significant overlap in grayscale values between chalcopyrite and pyrite, therefore highly limiting the classifier accuracy.

sted, utgiver, år, opplag, sider
Elsevier, 2019. Vol. 142, artikkel-id 105882
Emneord [en]
X-ray micro-tomography (µCT), Machine learning, Mineral segmentation, Feature-based classification, Feature matching
HSV kategori
Forskningsprogram
Mineralteknik; Malmgeologi
Identifikatorer
URN: urn:nbn:se:ltu:diva-75703DOI: 10.1016/j.mineng.2019.105882ISI: 000488141400014Scopus ID: 2-s2.0-85070948239OAI: oai:DiVA.org:ltu-75703DiVA, id: diva2:1346176
Merknad

Validerad;2019;Nivå 2;2019-08-27 (svasva)

Tilgjengelig fra: 2019-08-27 Laget: 2019-08-27 Sist oppdatert: 2019-10-30bibliografisk kontrollert
Inngår i avhandling
1. X-ray microcomputed tomography (µCT) as a potential tool in Geometallurgy
Åpne denne publikasjonen i ny fane eller vindu >>X-ray microcomputed tomography (µCT) as a potential tool in Geometallurgy
2019 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

In recent years, automated mineralogy has become an essential tool in geometallurgy. Automated mineralogical tools allow the acquisition of mineralogical and liberation data of ore particles in a sample. These particle data can then be used further for particle-based mineral processing simulation in the context of geometallurgy. However, most automated mineralogical tools currently in application are based on two-dimensional (2D) microscopy analysis, which are subject to stereological error when analyzing three-dimensional(3D) object such as ore particles. Recent advancements in X-ray microcomputed tomography (µCT) have indicated great potential of such system to be the next automated mineralogical tool. µCT's main advantage lies on its ability in monitoring 3D internal structure of the ore at resolutions down to few microns, eliminating stereological error obtained from 2D analysis. Aided with the continuous developments of computing capability of 3D data, it is only the question of time that µCT system becomes an interesting alternative in automated mineralogy system.

This study aims to evaluate the potential of implementing µCT as an automated mineralogical tool in the context of geometallurgy. First, a brief introduction about the role of automated mineralogy in geometallurgy is presented. Then, the development of µCT system to become an automated mineralogical tool in the context of geometallurgy andprocess mineralogy is discussed (Paper 1). The discussion also reviews the available data analysis methods in extracting ore properties (size, mineralogy, texture) from the 3D µCT image (Paper 2). Based on the review, it was found that the main challenge inperforming µCT analysis of ore samples is the difficulties associated to the segmentation of the mineral phases in the dataset. This challenge is adressed through the implementation of machine learning techniques using Scanning Electron Microscope (SEM) data as a reference to differentiate the mineral phases in the µCT dataset (Paper 3).

sted, utgiver, år, opplag, sider
Luleå: Luleå University of Technology, 2019. s. 74
Serie
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Emneord
X-ray microcomputed tomography, geometallurgy, automated mineralogy, ore characterization
HSV kategori
Forskningsprogram
Mineralteknik
Identifikatorer
urn:nbn:se:ltu:diva-76576 (URN)978-91-7790-492-2 (ISBN)978-91-7790-493-9 (ISBN)
Presentation
2019-12-13, F531, Luleå University of Technology, Luleå, 10:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
EU, Horizon 2020, 722677
Tilgjengelig fra: 2019-10-30 Laget: 2019-10-30 Sist oppdatert: 2019-11-27bibliografisk kontrollert

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