Intelligent recognition of shale fracture network images based on transfer learning.
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| Title: | Intelligent recognition of shale fracture network images based on transfer learning. |
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| Authors: | Wang, Qin1 (AUTHOR) wangqin0131@163.com, Hu, Jiangchun1 (AUTHOR), Liu, PengFei1 (AUTHOR), Sun, GuangLin1 (AUTHOR) |
| Source: | Earth Science Informatics. Feb2024, Vol. 17 Issue 1, p797-812. 16p. |
| Subject Terms: | *Shale, *Transfer of training, *Deep learning, *Bridge floors, *Image recognition (Computer vision), *Image processing, *Intelligent tutoring systems, *Intelligent transportation systems |
| Abstract: | This study presents an intelligent identification method for shale crack networks based on transfer learning. Focuses on investigating the main physical parameters of shale similar materials based on the similarity theory and the physical parameters of shale. Fracture network images obtained from shale-like materials. The fracture network images are then preprocessed using image processing technology to generate a high-quality shale image dataset. A deep learning transfer recognition model, based on ResNet-50, is constructed to detect model performance using these shale fracture network images. Experimental results and reliability analyses demonstrate that the ResNet-50 based deep learning transfer model achieves an accuracy of 87% for shale images, indicating high recognition accuracy and fast model convergence. The proposed intelligent shale crack identification method exhibits robustness and generalization ability, making it suitable for efficient fracture identification in geological, road, and bridge deck projects. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | This study presents an intelligent identification method for shale crack networks based on transfer learning. Focuses on investigating the main physical parameters of shale similar materials based on the similarity theory and the physical parameters of shale. Fracture network images obtained from shale-like materials. The fracture network images are then preprocessed using image processing technology to generate a high-quality shale image dataset. A deep learning transfer recognition model, based on ResNet-50, is constructed to detect model performance using these shale fracture network images. Experimental results and reliability analyses demonstrate that the ResNet-50 based deep learning transfer model achieves an accuracy of 87% for shale images, indicating high recognition accuracy and fast model convergence. The proposed intelligent shale crack identification method exhibits robustness and generalization ability, making it suitable for efficient fracture identification in geological, road, and bridge deck projects. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 18650473 |
| DOI: | 10.1007/s12145-023-01202-5 |
