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

Content Provider	IEEE Xplore Digital Library
Author	Rahman, A. Shahriar, M.S. Timms, G. Lindley, C. Davie, A.B. Biggins, D. Hellicar, A. Sennersten, C. Smith, G. Coombe, M.
Copyright Year	2015
Description	Author affiliation: Autonomous Syst. Program, CSIRO, Sandy Bay, TAS, Australia (Rahman, A.; Shahriar, M.S.; Timms, G.; Lindley, C.; Davie, A.B.; Biggins, D.; Hellicar, A.; Sennersten, C.; Smith, G.; Coombe, M.)
Abstract	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.
Sponsorship	IEEE
Starting Page	1
Ending Page	4
File Size	1730099
Page Count	4
File Format	PDF
e-ISBN	9781479982035
DOI	10.1109/ICSENS.2015.7370680
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-01
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Rocks Image color analysis Imaging Machine learning algorithms Image edge detection Surface texture Histograms mining machine learning XRF signatures image processing regression
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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