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Earth Science Informatics

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27-12-2023 | RESEARCH

Intelligent recognition of shale fracture network images based on transfer learning

Authors: Qin Wang, Jiangchun Hu, PengFei Liu, GuangLin Sun

Published in: Earth Science Informatics | Issue 1/2024

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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.

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Title: Intelligent recognition of shale fracture network images based on transfer learning
Authors: Qin Wang
Jiangchun Hu
PengFei Liu
GuangLin Sun
Publication date: 27-12-2023
Publisher: Springer Berlin Heidelberg
Published in: Earth Science Informatics / Issue 1/2024
Print ISSN: 1865-0473
Electronic ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-023-01202-5

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