Research on recognition algorithm of fractures, veins and kavst in digital borehole images based on deep learning

Abstract

[Objective] The precise acquisition of rock mass structural plane parameters is crucial for the stability evaluation of deep underground engineering. Existing manual recognition method for digital borehole images suffer from subjectivity and low efficiency, while image processing-based automatic recognition method still face challenges in accuracy due to the diversity of structural types and insufficient recognition robustness. Therefore, this paper proposes a deep learning-based algorithm for the identification of fractures, veins, and karst, aiming to achieve efficient recognition of structural planes and high-precision parameter extraction. [Methods] In response to the fine segmentation requirements for fractures, veins, and karst, a multi-target semantic segmentation label system is constructed. The SpectDA-ResU-Net segmentation model is designed, integrating spectral gating modules, dynamic channel attention mechanisms, attention gating units, and deep supervision mechanisms, significantly enhancing the accuracy of complex structure recognition. Additionally, an automatic 3D geometric parameter extraction method based on segmentation masks is proposed. [Results] Ablation experiments on an enhanced dataset of 2 148 borehole images show that after integrating all modules, the proposed model achieves an F1-score of 94.46%(an improvement of 4.65%) and an mIoU of 89.59%(an improvement of 8.69%). In comparison with the U-Net model, the mIoU improves by 15.77%, and other evaluation metrics are significantly better than those of existing mainstream segmentation networks. Case studies in the Guizhou karst region show that the automatic extraction error of structural attitudes is controlled within 4%. [Conclusion] The research demonstrates that the proposed algorithm has significant advantages in improving the accuracy of digital borehole image structural plane recognition and the efficiency of parameter extraction.

Keywords

digital borehole images / deep learning / structural plane / orientation / intelligent recognition / influencing factors / multi-target semantic segmentation / Guizhou karst region