| Citation: | Zhang Ye, Chen Jinqiao, Li Yanlong. Intelligent recognition and quantitative analysis of borehole hydraulic geological images utilizing multiple deep learning models[J]. Bulletin of Geological Science and Technology, 2023, 42(6): 31-41. doi: 10.19509/j.cnki.dzkq.tb20220091
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Borehole Televiewer (BHTV) imaging serves as an effective tool for analyzing deep rock formations in geological engineering, offering crucial insights into surface discontinuity extensions and the distribution of buried discontinuities, especially within complex geological environments associated with hydraulic engineering projects. Recognizing the current issues of subjectivity and low efficiency, prevalent in the manual calculation of rock discontinuity morphological characteristics within rock masses, we propose an intelligent approach for segmenting geometric data from digital borehole images. Leveraging the segmentation outcomes, an image thinning technique is employed to facilitate precise quantitative analysis of borehole data.
In this research, we employ deep learning models to intelligently identify fractures within BHTV images, utilizing various network structures such as Unet, SegNet, and DeepLabV3. The recognition results are compared with traditional image processing methods, demonstrating the advantages of deep models in accurately segmenting complex geological images. Furthermore, we enhance the model's performance by incorporating an attention mechanism into the encoder-decoder process.Once precise segmentation of rock discontinuities is achieved, the fracture skeleton is extracted using the image thinning method, representing fractures as one-pixel-width curves. Ultimately, automated calculations is completed for dip strike, dip angle, and fracture thickness.
This method is applied to segment and calculate borehole televiewer images in hydraulic engineering. Comparing the results of manual extraction and automatic extraction of fracture information, the error of dip strike and dip angle is less than 3°, and the fracture thickness error is less than 0.65 mm.
The results verify that the intelligent calculation method of fracture information proposed in this paper. The proposed method has wide application prospects in hydraulic engineering.
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