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Segmentation and quantitative analysis of geological fracture: a deep transfer learning approach based on borehole televiewer image

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Abstract

Rock quality evaluation is an important step for preliminary design and survey in hydraulic engineering. In current practice, borehole televiewer (BHTV) images are manually reviewed and analyzed by domain experts to evaluate rock quality, which is time-consuming, laborious, and prone to the subjectivity of human evaluation. Emerging techniques, such as deep learning and image processing, can potentially address the limitations by automating the process of BHTV image analysis. In this research, we propose an intelligent image segmentation model based on ResNet and Unet, which is called RUnet. In the model, ResNet is used as the pre-trained model to extract image features. The optimizer and the loss function of the RUNet are improved by incorporating the domain knowledge of geology. Through the comparison of the Unet and RUnet with different optimizers, the effectiveness of the model has been validated. Based on the features extracted by RUnet, the binary image skeleton can be obtained and the relative coordinates of all the pixels can be calculated, which can be applied in dip azimuth, dip angle, and mean thickness calculation of the fracture. The generalized least squares method is also employed in fracture occurrence quantification. The intelligent fracture information quantification can be realized through the whole analysis process, which provides an automated and reliable approach to quantitatively evaluate rock quality via BHTV images.

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Code availability

Source codes can be found at GitHub https://github.com/ye3010205121/Quantitative-Analysis-of-Geological-Fracture.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (NSFC) (Grant No. 52009109, 51979224) and the PhD Research Startup Foundation of Xi’an University of Technology (Grant No. 104-451120005).

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Authors and Affiliations

  1. State Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an, Shaanxi, 710048, People’s Republic of China

    Ye Zhang, Jinqiao Chen & Yanlong Li

Authors
  1. Ye Zhang
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  2. Jinqiao Chen
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  3. Yanlong Li
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Contributions

Ye Zhang and Yanlong Li: idea, framework design, coding, and writing; Jinqiao Chen: data processing and calculating.

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Correspondence to Yanlong Li.

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The authors declare no competing interests.

Additional information

Communicated by Zeynal Abiddin Erguler.

Responsible Editor: Zeynal Abiddin Erguler

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Zhang, Y., Chen, J. & Li, Y. Segmentation and quantitative analysis of geological fracture: a deep transfer learning approach based on borehole televiewer image. Arab J Geosci 15, 300 (2022). https://doi.org/10.1007/s12517-022-09536-y

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