Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks
The integration of image analysis through deep learning (DL) into rock classification represents a significant leap forward in geological research. While traditional methods remain invaluable for their expertise and historical context, DL offers a powerful complement by enhancing the speed, objectiv...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Journal of Rock Mechanics and Geotechnical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S167477552400177X |
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| author | Afshin Tatar Manouchehr Haghighi Abbas Zeinijahromi |
| author_facet | Afshin Tatar Manouchehr Haghighi Abbas Zeinijahromi |
| author_sort | Afshin Tatar |
| collection | DOAJ |
| description | The integration of image analysis through deep learning (DL) into rock classification represents a significant leap forward in geological research. While traditional methods remain invaluable for their expertise and historical context, DL offers a powerful complement by enhancing the speed, objectivity, and precision of the classification process. This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks (CNNs) for geological image analysis, particularly in the classification of igneous, metamorphic, and sedimentary rock types from rock thin section (RTS) images. This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision. Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities, achieving an F1-Score of 0.9869 for igneous rocks, 0.9884 for metamorphic rocks, and 0.9929 for sedimentary rocks, representing improvements compared to the baseline original results. Moreover, the weighted average F1-Score across all classes and techniques is 0.9886, indicating an enhancement. Conversely, methods like Distort lead to decreased accuracy and F1-Score, with an F1-Score of 0.949 for igneous rocks, 0.954 for metamorphic rocks, and 0.9416 for sedimentary rocks, exacerbating the performance compared to the baseline. The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results. The findings of this study can benefit various fields, including remote sensing, mineral exploration, and environmental monitoring, by enhancing the accuracy of geological image analysis both for scientific research and industrial applications. |
| format | Article |
| id | doaj-art-faa8075b239949658985b6a941c0537c |
| institution | Kabale University |
| issn | 1674-7755 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Rock Mechanics and Geotechnical Engineering |
| spelling | doaj-art-faa8075b239949658985b6a941c0537c2025-01-17T04:49:08ZengElsevierJournal of Rock Mechanics and Geotechnical Engineering1674-77552025-01-01171106125Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networksAfshin Tatar0Manouchehr Haghighi1Abbas Zeinijahromi2Corresponding author.; School of Chemical Engineering, Discipline of Mining and Petroleum Engineering, The University of Adelaide, Adelaide, SA 5005, AustraliaSchool of Chemical Engineering, Discipline of Mining and Petroleum Engineering, The University of Adelaide, Adelaide, SA 5005, AustraliaSchool of Chemical Engineering, Discipline of Mining and Petroleum Engineering, The University of Adelaide, Adelaide, SA 5005, AustraliaThe integration of image analysis through deep learning (DL) into rock classification represents a significant leap forward in geological research. While traditional methods remain invaluable for their expertise and historical context, DL offers a powerful complement by enhancing the speed, objectivity, and precision of the classification process. This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks (CNNs) for geological image analysis, particularly in the classification of igneous, metamorphic, and sedimentary rock types from rock thin section (RTS) images. This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision. Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities, achieving an F1-Score of 0.9869 for igneous rocks, 0.9884 for metamorphic rocks, and 0.9929 for sedimentary rocks, representing improvements compared to the baseline original results. Moreover, the weighted average F1-Score across all classes and techniques is 0.9886, indicating an enhancement. Conversely, methods like Distort lead to decreased accuracy and F1-Score, with an F1-Score of 0.949 for igneous rocks, 0.954 for metamorphic rocks, and 0.9416 for sedimentary rocks, exacerbating the performance compared to the baseline. The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results. The findings of this study can benefit various fields, including remote sensing, mineral exploration, and environmental monitoring, by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.http://www.sciencedirect.com/science/article/pii/S167477552400177XDeep learning (DL)Image analysisImage data augmentationConvolutional neural networks (CNNs)Geological image analysisRock classification |
| spellingShingle | Afshin Tatar Manouchehr Haghighi Abbas Zeinijahromi Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks Journal of Rock Mechanics and Geotechnical Engineering Deep learning (DL) Image analysis Image data augmentation Convolutional neural networks (CNNs) Geological image analysis Rock classification |
| title | Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks |
| title_full | Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks |
| title_fullStr | Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks |
| title_full_unstemmed | Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks |
| title_short | Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks |
| title_sort | experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks |
| topic | Deep learning (DL) Image analysis Image data augmentation Convolutional neural networks (CNNs) Geological image analysis Rock classification |
| url | http://www.sciencedirect.com/science/article/pii/S167477552400177X |
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