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Neelakantan, S., Hansson, A., Norell, J., Schött, J., Längkvist, M. & Loutfi, A. (2024). Machine Learning for Lithology Analysis using a Multi-Modal Approach of Integrating XRF and XCT data. In: 14th Scandinavian Conference on Artificial Intelligence SCAI 2024, June 10-11, 2024, Jönköping, Sweden: . Paper presented at 14th Scandinavian Conference on Artificial Intelligence (SCAI 2024), Jönköping, Sweden, June 10-11, 2024.
Open this publication in new window or tab >>Machine Learning for Lithology Analysis using a Multi-Modal Approach of Integrating XRF and XCT data
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2024 (English)In: 14th Scandinavian Conference on Artificial Intelligence SCAI 2024, June 10-11, 2024, Jönköping, Sweden, 2024Conference paper, Published paper (Refereed)
Abstract [en]

We explore the use of various machine learning (ML) models for classifying lithologies utilizing data from X-ray fluorescence (XRF) and X-ray computed tomography (XCT). Typically, lithologies are identified over several meters, which restricts the use of ML models due to limited training data. To address this issue, we augment the original interval dataset, where lithologies are marked over extensive sections, into finer segments of 10cm, to produce a high resolution dataset with vastly increased sample size. Additionally, we examine the impact of adjacent lithologies on building a more generalized ML model. We also demonstrate that combining XRF and XCT data leads to an improved classification accuracy compared to using only XRF data, which is the common practice in current studies, or solely relying on XCT data.

National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-115219 (URN)10.3384/ecp208021 (DOI)
Conference
14th Scandinavian Conference on Artificial Intelligence (SCAI 2024), Jönköping, Sweden, June 10-11, 2024
Funder
Knowledge Foundation, Dnr:20190128
Available from: 2024-08-06 Created: 2024-08-06 Last updated: 2024-08-12Bibliographically approved
Neelakantan, S., Norell, J., Hansson, A., Längkvist, M. & Loutfi, A. (2024). Neural network approach for shape-based euhedral pyrite identification in X-ray CT data with adversarial unsupervised domain adaptation. Applied Computing and Geosciences, 21, Article ID 100153.
Open this publication in new window or tab >>Neural network approach for shape-based euhedral pyrite identification in X-ray CT data with adversarial unsupervised domain adaptation
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2024 (English)In: Applied Computing and Geosciences, E-ISSN 2590-1974, Vol. 21, article id 100153Article in journal (Refereed) Published
Abstract [en]

We explore an attenuation and shape-based identification of euhedral pyrites in high-resolution X-ray Computed Tomography (XCT) data using deep neural networks. To deal with the scarcity of annotated data we generate a complementary training set of synthetic images. To investigate and address the domain gap between the synthetic and XCT data, several deep learning models, with and without domain adaption, are trained and compared. We find that a model trained on a small set of human annotations, while displaying over-fitting, can rival the human annotators. The unsupervised domain adaptation approaches are successful in bridging the domain gap, which significantly improves their performance. A domain-adapted model, trained on a dataset that fuses synthetic and real data, is the overall best-performing model. This highlights the possibility of using synthetic datasets for the application of deep learning in mineralogy.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Mineral identification, Unsupervised domain adaptation, Deep convolutional neural network, Semantic segmentation, Euhedral pyrites
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-111189 (URN)10.1016/j.acags.2023.100153 (DOI)001155327400001 ()2-s2.0-85182272087 (Scopus ID)
Funder
Knowledge Foundation, 20190128
Note

This work has been supported by the Industrial Graduate School Collaborative AI and Robotics funded by the Swedish Knowledge Foundation Dnr:20190128 and in collaboration with the industrial partner Orexplore Technologies.

Available from: 2024-01-29 Created: 2024-01-29 Last updated: 2024-02-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0901-5973

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