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

conference contribution
posted 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.

History

Publication title

2015 IEEE SENSORS Proceedings

Pagination

1-4

ISBN

978-147998202-8

Department/School

School of Information and Communication Technology

Publisher

Institute of Electrical and Electronics Engineers Inc.

Place of publication

United States

Event title

14th IEEE SENSORS

Event Venue

Seoul, Korea

Date of Event (Start Date)

2015-11-01

Date of Event (End Date)

2015-11-04

Rights statement

Copyright 2015 IEEE

Repository Status

  • Restricted

Socio-economic Objectives

Mining and extraction of energy resources not elsewhere classified

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