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Intelligent recognition of shale fracture network images based on transfer learning

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Publicado en:Earth Science Informatics vol. 17, no. 1 (Feb 2024), p. 797
Autor principal: Wang, Qin
Otros Autores: Hu, Jiangchun, Liu, PengFei, Sun, GuangLin
Publicado:
Springer Nature B.V.
Materias:
Reliability analysis
Model accuracy
Shales
Similarity theory
Mathematical models
Physical properties
Bridge decks
Image quality
Deep learning
Identification methods
Image processing
Parameters
Mechanical properties
Principles
Accuracy
Datasets
Vision systems
Identification
Concrete
Civil engineering
Asphalt pavements
Measurement techniques
Cracks
Machine learning
Oil shale
Oil reserves
Artificial intelligence
Neural networks
Classification
Earth science
Algorithms
Informatics
Natural gas reserves
Economic
Environmental
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1865-0473
1865-0481
DOI:10.1007/s12145-023-01202-5
Fuente:Science Database