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A machine learning approach to find association between imaging features and XRF signatures of rocks in underground mines


Rahman, A and Shahriar, MS and Timms, G and Lindley, C and Davie, AB and Biggins, D and Hellicar, A and Sennersten, C and Smith, G and Coombe, M, A machine learning approach to find association between imaging features and XRF signatures of rocks in underground mines, 2015 IEEE SENSORS Proceedings, 1-4 November 2015, Seoul, Korea, pp. 1-4. ISBN 978-147998202-8 (2015) [Refereed Conference Paper]

Copyright Statement

Copyright 2015 IEEE

DOI: doi:10.1109/ICSENS.2015.7370680


This study investigated the applicability of machine learning algorithms to detect the presence of elements in underground mines from rock surface images, which is proposed as a heuristic classification method inspired by the ability of human geologists to make judgments about the location of ore veins by eye. A regression algorithm was investigated to find associations between image features and X-Ray Fluorescence (XRF) signatures indicating elemental content of the surface and near-surface region of the rocks. A set of image processing algorithms was used to extract color distribution, edge orientation statistics, and texture of the rock surfaces. XRF signatures were obtained from the same samples, providing a semi-quantitative measure of element concentration. The process was performed on a set of 20 rock samples. The regression algorithm was then trained to find a mapping between image features and the semi-quantitative element concentrations (corresponding with XRF peaks). Experimental results demonstrate the potential effectiveness of the proposed approach in the context of a specific ore body.

Item Details

Item Type:Refereed Conference Paper
Keywords:image processing, machine learning, mining, regression, XRF signatures
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial intelligence not elsewhere classified
Objective Division:Energy
Objective Group:Mining and extraction of energy resources
Objective Field:Mining and extraction of energy resources not elsewhere classified
UTAS Author:Shahriar, MS (Dr Sumon Shahriar)
UTAS Author:Lindley, C (Dr Craig Lindley)
ID Code:118003
Year Published:2015
Deposited By:Information and Communication Technology
Deposited On:2017-06-30
Last Modified:2017-10-17

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