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Neural network approach for shape-based euhedral pyrite identification in X-ray CT data with adversarial unsupervised domain adaptation
Örebro University, School of Science and Technology. (MPI Lab (AASS))ORCID iD: 0000-0003-0901-5973
Orexplore AB, Kista, Stockholm, Sweden.
Orexplore AB, Kista, Stockholm, Sweden.
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-0579-7181
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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. Vol. 21, article id 100153
Keywords [en]
Mineral identification, Unsupervised domain adaptation, Deep convolutional neural network, Semantic segmentation, Euhedral pyrites
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-111189DOI: 10.1016/j.acags.2023.100153ISI: 001155327400001Scopus ID: 2-s2.0-85182272087OAI: oai:DiVA.org:oru-111189DiVA, id: diva2:1832249
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

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Neelakantan, SurajLängkvist, MartinLoutfi, Amy

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