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A machine learning approach to find association between imaging features and XRF signatures of rocks in underground mines
conference contributionposted on 2023-05-23, 12:11 authored by Rahman, A, Shahriar, MS, Timms, G, Lindley, C, Davie, AB, Biggins, D, Hellicar, A, Sennersten, C, Smith, G, Coombe, M
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.
Publication title2015 IEEE SENSORS Proceedings
Department/SchoolSchool of Information and Communication Technology
PublisherInstitute of Electrical and Electronics Engineers Inc.
Place of publicationUnited States
Event title14th IEEE SENSORS
Event VenueSeoul, Korea
Date of Event (Start Date)2015-11-01
Date of Event (End Date)2015-11-04
Rights statementCopyright 2015 IEEE