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Borehole Structural Plane Segmentation and Parameter Visualization Method with Dual-Encoder Attention Network
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Yiru SHI1, 2, Dong DUAN1, 2, Jie LI1, 2, Xiaojing FENG1, 2, Jiaxiong HE1, 2, Weidong LU3, 4, Yuanping GAN3, 4
Mining Research and Development | 2026, 46(2) : 288 - 302
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Mining Research and Development | 2026, 46(2): 288-302
Borehole Structural Plane Segmentation and Parameter Visualization Method with Dual-Encoder Attention Network
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Yiru SHI1, 2, Dong DUAN1, 2, Jie LI1, 2, Xiaojing FENG1, 2, Jiaxiong HE1, 2, Weidong LU3, 4, Yuanping GAN3, 4
Affiliations
  • 1.College of Mining Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China
  • 2.Key Laboratory of In-situ Property-improving Mining of Ministry of Education, Taiyuan University of Technology, Taiyuan, Shanxi, 030024, China
  • 3.College of Safety Science and Engineering, Xinjiang Institute of Engineering, Urumqi, Xinjiang 830023, China
  • 4.Xinjiang Key Laboratory of Coal Mine Disaster Intelligent Prevention and Emergency Response, Xinjiang Institute of Engineering, Urumqi, Xinjiang 830023, China
Published: 2026-02-25
Outline
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Aiming at the problems of uneven illumination, complex background and large difference in structural plane morphology of borehole images, a dual-encoder interactive attention enhanced fusion network ( DISA-FuseNet ) was proposed. Based on the UNet encoder-decoder architecture, the network adopted the dual-encoder structure of Swin Transformer and ResNet50 to capture global context information and local detail features simultaneously. The attention was weighted by the Statistical Aggregation Channel Attention ( SACA ) module to suppress the background noise and irrelevant information of the rock mass. The underlying features were inputted into the Adaptive Hierarchical Enhancement module ( AHE ) after splicing to enhance the network's ability to capture multi-scale structural plane features. Experimental verification was conducted based on the self-constructed borehole structural plane dataset, and DISA-FuseNet was compared with five models, namely UNet, Deeplabv3+, Trans-UNet, DeepCrack and CrackFormer. The results show that DISA-FuseNet outperforms all the other models in terms of mean intersection over union, mean pixel accuracy, recall, precision and F1-score, these indexes are increased by 8.44, 9.51, 8.52, 10.60 and 9.62 percentage points respectively compared with those of the baseline model UNet. Furthermore, a method for structural plane parameter extraction and 3D visualization is proposed, which enables the accurate extraction of structural plane parameters and the construction of an interactive 3D visualization model. The actual engineering case verifies that the method has good effectiveness and engineering applicability.

Structural plane identification  /  Borehole image  /  Dual-encoder  /  Interactive attention enhanced fusion network  /  3D visualization
时乙入, 段东, 李杰, 冯小静, 何家雄, 陆卫东, 甘元平. 融合双编码器注意力网络的钻孔结构面分割与参数可视化方法. 矿业研究与开发, 2026 , 46 (2) : 288 -302 .
Yiru SHI, Dong DUAN, Jie LI, Xiaojing FENG, Jiaxiong HE, Weidong LU, Yuanping GAN. Borehole Structural Plane Segmentation and Parameter Visualization Method with Dual-Encoder Attention Network[J]. Mining Research and Development, 2026 , 46 (2) : 288 -302 .
Year 2026 volume 46 Issue 2
47
12
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  • Receive Date:2025-01-22
  • Online Date:2026-10-29
  • Published:2026-02-25
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  • Received:2025-01-22
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Affiliations
    1.College of Mining Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China
    2.Key Laboratory of In-situ Property-improving Mining of Ministry of Education, Taiyuan University of Technology, Taiyuan, Shanxi, 030024, China
    3.College of Safety Science and Engineering, Xinjiang Institute of Engineering, Urumqi, Xinjiang 830023, China
    4.Xinjiang Key Laboratory of Coal Mine Disaster Intelligent Prevention and Emergency Response, Xinjiang Institute of Engineering, Urumqi, Xinjiang 830023, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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