A Robust Image-Based Framework for Borehole Fracture Detection and Quantitative Characterization
DOI:
https://doi.org/10.3991/itdaf.v4i1.59291Keywords:
Fracture detection, Borehole imaging, Canny algorithm, HDBSCAN clustering, RANSAC fitting, Intelligent underground engineeringAbstract
Ensuring the safety and intelligence of underground coal mining has become a crucial task in the context of developing new productive forces. Fractures within surrounding rock layers are key factors affecting the stability of underground engineering, yet traditional manual interpretation of borehole images is inefficient and subjective. To address this issue, this study proposes an automated framework for fracture identification and quantitative characterization based on image processing and clustering analysis. First, an improved Canny edge detection algorithm is applied to borehole wall images after grayscale conversion and Gaussian filtering, effectively suppressing geological noise such as natural textures, drilling traces, and mud residues. Morphological dilation and binarization are then used to enhance fracture connectivity and extract clear fracture boundaries. Subsequently, an unsupervised clustering algorithm, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), is introduced to automatically classify fracture points and separate multiple intersecting fractures. On this basis, a robust sine-model fitting approach is developed using a combination of random sample consensus (RANSAC) and Levenberg-Marquardt optimization, enabling accurate estimation of geometric parameters including amplitude, period, phase, and offset of each fracture. Experimental results demonstrate that the proposed method achieves high recognition accuracy and strong noise resistance. The detected fractures exhibit excellent agreement with manual observations, with the best-fitting models yielding coefficients of determination (R²) above 0.94. Compared with conventional manual interpretation, the proposed approach significantly improves automation, consistency, and computational efficiency. This research provides a reliable and interpretable framework for intelligent fracture detection and quantitative analysis in underground engineering, offering valuable technical support for the intelligent and safe development of coal mines.
References
[1] 康红普, 姜鹏飞, 王子越, 等, “煤巷钻锚一体化快速掘进技术与装备及应用,” 煤炭学报, vol. 49, no. 1, pp. 131–151, 2024. https://doi.org/10.13225/j.cnki.jccs.2023.1675.
[2] 袁亮, 张平松, “煤矿透明地质模型动态重构的关键技术与路径思考,” 煤炭学报, vol. 48, no. 1, pp. 1–14, 2023. https://doi.org/10.13225/j.cnki.jccs.yg22.1012.
[3] X. Ma, F. Qu, W. He, L, Wang and X, Liu, “Intelligent detection method for internal fractures in mine rock masses based on borehole camera images,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 17, pp. 4802-4814, 2025. https://doi.org/10.1016/j.jrmge.2024.10.027.
[4] Stephen E. Prensky, “Advances in borehole imaging technology and applications,” Geological Society, vol. 159, no. 4, pp. 1-43, 1999. https://doi.org/10.1144/GSL.SP.1999.159.01.01.
[5] 刘灿灿, 郑西贵, 王术龙, 等, “基于钻孔成像的结构面智能化识别系统,” 采矿与安全工程学报, vol. 41, no. 4, pp. 720–729, 2024. https://doi.org/10.13545/j.cnki.jmse.2023.0090.
[6] 汪进超, 王川婴, 杜琦, 等, “基于光声组合测量的地质钻孔三维可视化研究,” 岩石力学与工程学报, vol. 42, no. 3, pp. 649–660, 2023. https://doi.org/10.13722/j.cnki.jrme.2022.0528.
[7] L. O. Dias, “Automatic detection of fractures and breakouts patterns in acoustic borehole image logs using fast-region convolutional neural networks,” Journal of Petroleum Science and Engineering, vol. 191, p. 107099, 2020. https://doi.org/10.1016/j.petrol.2020.107099.
[8] S. K. Sinha, P. W. Fieguth, and M. A. Polak, “Computer Vision Techniques for Automatic Structural Assessment of Underground Pipes,” Computer-Aided Civil and Infrastructure Engineering, vol. 18, pp. 95-112, 2003. https://doi.org/10.1111/1467-8667.00302.
[9] V. Chitale, “Borehole imaging in reservoir characterization: implementation of a standard interpretation workflow for the clastic- and carbonate reservoirs.” SPWLA 46th Annual Logging Symposium, vol. 114, pp. 223–238, 2018. https://onepetro.org/SPWLAALS/proceedings/SPWLA-2005/All-SPWLA-2005/SPWLA-2005-CC/27519.
[10] Z. Wang, “Differential confocal optical probes with optimized detection efficiency and pearson correlation coefficient strategy based on the peak-clustering algorithm” Micromachines, Vol. 14, P. 1163, 2023. https://doi.org/10.3390/mi14061163.
[11] N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979. https://doi.org/10.1109/TSMC.1979.4310076.
[12] L. Zhang, “Object-level change detection with a dual correlation attention-guided detector,” Isprs Journal of Photogrammetry and Remote Sensing, vol. 178, pp. 78–92, 2021. https://doi.org/10.1016/j.isprsjprs.2021.05.002.
[13] J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986. https://doi.org/10.1109/TPAMI.1986.4767851.
[14] Y. Cao, D. Wu, and Y. Duan, “A new image edge detection algorithm based on improved Canny,” Journal of Computational Methods in Sciences and Engineering, vol. 20, p. 105141, 2020. https://doi.org/10.3233/JCM-193963.
[15] R. J. G. B. Campello, D. Moulavi, and J. Sander, “Density-based clustering based on hierarchical density estimates,” Advances in Knowledge Discovery and Data Mining, pp. 160–172, 2013. https://doi.org/10.1007/978-3-642-37456-2_14.
[16] M. Ester, H. P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD), pp. 226–231, 1996.
[17] M. A. Fischler and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM, vol. 24, no. 6, pp. 381–395, 1981. https://doi.org/10.1145/358669.358692.
[18] Y. Li, J. Peng, and L. Zhang, “Quantitative evaluation of impact cracks near the borehole based on 2D image analysis and fractal theory,” Geothermics, vol. 100, p. 102335, 2022. https://doi.org/10.1016/j.geothermics.2021.102335.
[19] C. Wang, X. Zou, and Z. Han, “An automatic recognition and parameter extraction method for structural planes in borehole image,” Journal of Applied Geophysics, vol. 135, pp. 135-143, 2016. https://doi.org/ 10.1016/j.jappgeo.2016.10.005.
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