Automatic Segmentation and Characterization of Structure Planes From Borehole Images Based on Deep Learning
文献类型：期刊论文
| 作者 | Chen, Shuangyuan1,2; Han, Zengqiang1,2; Cheng, Yi1,2; Wang, Chao2 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2025 |
| 卷号 | 13页码:34789-34801 |
| 关键词 | Image segmentation Decoding Rocks Feature extraction Imaging Image edge detection Probes Geotechnical engineering Deep learning Cameras Borehole image feature extraction fracture detection image segmentation |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2025.3534269 |
| 英文摘要 | Structural characteristics of rock masses are crucial in geotechnical engineering, yet manual identification of structural planes from borehole images is limited by efficiency and reliability. To address this, we developed an improved U-Net-based segmentation network, specifically tailored for structural planes in borehole images, enabling automatic identification and characterization. The model integrates deformable convolutions and channel attention mechanisms to improve structure plane detection, allowing for precise segmentation of geological structures under various challenging conditions. Furthermore, we introduce an automated workflow that couples segmentation results with curve fitting and parameter estimation techniques to precisely quantify critical structural attributes, including dip direction, dip angle, and aperture. The proposed method was evaluated on a borehole image dataset, achieving a mean IoU of 69.53%, with 93.59% for the background class and 45.47% for the structure plane class, demonstrating its effectiveness in segmenting both dominant and complex regions. The dataset is available upon request and will be made publicly available in future studies. Results also validates the effectiveness of estimating structural plane parameters. Compared to exist methods, our approach specifically addresses the unique challenges of borehole images. By providing a reliable and efficient tool for structure plane segmentation and parameter characterization, this study enhances the accuracy and efficiency of rock mass structure detection and analysis. |
| 资助项目 | National Key Research and Development Program of China[2023YFC3007003] ; National Natural Science Foundation of China[42227805] ; Key Research and Development Program of Hubei Province[2021BAA201] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001439878300007 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/36897]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Han, Zengqiang |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Shuangyuan,Han, Zengqiang,Cheng, Yi,et al. Automatic Segmentation and Characterization of Structure Planes From Borehole Images Based on Deep Learning[J]. IEEE ACCESS,2025,13:34789-34801. |
| APA | Chen, Shuangyuan,Han, Zengqiang,Cheng, Yi,&Wang, Chao.(2025).Automatic Segmentation and Characterization of Structure Planes From Borehole Images Based on Deep Learning.IEEE ACCESS,13,34789-34801. |
| MLA | Chen, Shuangyuan,et al."Automatic Segmentation and Characterization of Structure Planes From Borehole Images Based on Deep Learning".IEEE ACCESS 13(2025):34789-34801. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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