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 Type:	Refereed Conference Paper
Keywords:	image processing, machine learning, mining, regression, XRF signatures
Research Division:	Information and Computing Sciences
Research Group:	Artificial Intelligence and Image Processing
Research Field:	Artificial Intelligence and Image Processing 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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