Aslan, B. and Nitschke, G. and Ly, C. and Knapp, J. (2024) New tools for Automated Particle Deagglomeration: Machine-Learning from Mineralogy Data, Proceedings of Process Mineralogy 2024, Cape Town, South Africa, MEI Conferences.
Full text not available from this repository. (Use alternate locations listed below)Abstract
Rock classification depends on mineral composition and morphology, such as size, angularity, or mineral associations. Traditionally, optical petrography by skilled and experienced professionals was used for this purpose. Many tools have been developed to provide data on bulk mineralogy, such as X-Ray Fluorescence (XRF), Short-Wave Infrared (SWIR), and Fourier Transform InfraRed (FTIR). However, automated mineralogy provides insightful mineralogy and textural information, but is limited in its ability to recognize individual particles in a granular specimen, as it relies on programmatic and rules-based methods to deagglomerate particles, defined as a mineral area surrounded by background phases. This is especially noticeable for fine particle-size specimens, where traditional deagglomeration techniques are limited in recognizing an irregularly shaped particle compared to multiple touching particles, which a trained analyst could recognize. We describe a new automated mineralogy computational tool for particle classification and analysis, leveraging the general classification capacity of large neural networks (deep-learning), multi-label classification, and established computer-vision (machine-learning) techniques to improve particle deagglomeration across various granular specimens.
| Item Type: | Conference paper |
|---|---|
| Subjects: | Computing methodologies > Artificial intelligence > Computer vision > Computer vision problems > Image segmentation Computing methodologies > Machine learning |
| Date Deposited: | 26 Jun 2025 08:55 |
| Last Modified: | 26 Jun 2025 08:55 |
| URI: | https://pubs.cs.uct.ac.za/id/eprint/1727 |
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