Classification of Mineral Phases in Unpolished Samples Using a Transformer Neural Network

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0199984" target="_blank" >RIV/00216305:26220/26:0199984 - isvavai.cz</a>
Alternative codes found
RIV/00007064:K01__/25:N0000098
Result on the web
<a href="http://dx.doi.org/10.1109/ICUMT67815.2025.11268630" target="_blank" >http://dx.doi.org/10.1109/ICUMT67815.2025.11268630</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICUMT67815.2025.11268630" target="_blank" >10.1109/ICUMT67815.2025.11268630</a>

Result language
angličtina
Original language name
Classification of Mineral Phases in Unpolished Samples Using a Transformer Neural Network
Original language description
Scanning electron microscopy (SEM) combined with energy dispersive spectroscopy (EDS) is widely used in geosciences for mineral phase classification. However, the lack of large, labeled datasets—especially for unpolished samples common in forensic pedology—limits the direct application of traditional supervised machine learning methods. In this study, we investigate the use of synthetic EDS data, generated using DTSA-II, to train advanced neural network architectures and evaluate their performance on real SEM–EDS measurements. We compare a U-Net baseline with a proposed 3D ResNet and a Transformer model. All models were trained on synthetic data and tested on real measurements of ten selected mineral phases. U-Net achieved accuracy of 63.7%, 3D ResNet reached 92.3%, and the Transformer model achieved the highest accuracy of 97.0%. These findings demonstrate that Transformer architectures can effectively generalize from synthetic to real EDS data, offering a promising way for accurate mineral phase classification in forensic and geological applications without the need for extensive labeled real datasets.
Czech name
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Czech description
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Project
<a href="/en/project/VK01010107" target="_blank" >VK01010107: Application of artificial intelligence for forensic identification of soil phases</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 17th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
ISBN
979-8-3315-7675-2
ISSN
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e-ISSN
2157-023X
Number of pages
6
Pages from-to
170-175
Publisher name
—
Place of publication
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Event location
Florence, Italy
Event date
Nov 3, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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