Evidence map›Paper›PMID 40914423›Full record

ArticleJournal of advanced research2026

Reconstruction of three-dimensional fractures in coal based on multidirection slicing and U-Net segmentation.

Yufeng Bian, Jiangtao Zheng, Tian Tian, Chaodong Xi, Zhangyu Ren, Yang Ju

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Article in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Yufeng BianState Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology at Beijing, Beijing 100083, China; School of Mechanics and Civil Engineering, China University of Mining and Technology at Beijing, Beijing 100083, China.
Jiangtao ZhengState Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology at Beijing, Beijing 100083, China; School of Mechanics and Civil Engineering, China University of Mining and Technology at Beijing, Beijing 100083, China. Electronic address: zhengjt@cumtb.edu.cn.
Tian TianState Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology at Beijing, Beijing 100083, China; School of Mechanics and Civil Engineering, China University of Mining and Technology at Beijing, Beijing 100083, China.
Chaodong XiCCTEG Coal Mining Research Institute, Beijing 100013, China.
Zhangyu RenSchool of Emergency Management and Safe Engineering, China University of Mining and Technology at Beijing, Beijing 100083, China.
Yang JuState Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology at Beijing, Beijing 100083, China. Electronic address: juy@cumtb.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAccurate characterization of multi-size fractures in coal is crucial for estimating its transport properties. However, the extraction of narrow microfractures in 3D voxel-type CT images is difficult, which causes the loss of connectivity in the extracted fracture network and reduces the accuracy of the predicted transport properties.

objectivesImproving the image quality and optimizing the segmentation process to deal with the inaccuracy of fracture extraction from coal CT images.

methodsIn this study, the super-resolution image reconstruction and image noise filtering, were used to double the CT image size and enhance the recognition of narrow microfractures. The 3D image data were then sliced normal to the center vertical axis and around the center vertical axis to create two datasets. A convolution network, U-Net, was used to segment the two datasets, and a 3D binary fracture model was reconstructed by combining the segmented results. The permeability of the model was calculated using the lattice Boltzmann method.

resultsCompared with other widely used segmentation methods, multidirectional slicing and U-Net segmentation significantly improved the recognition of narrow microfractures. The segmented model had a higher effective porosity and permeability, indicating the good connectivity of the fracture network.

conclusionThese results suggest that the accurate extraction of narrow microfractures is critical to improve the estimation of fluid transport in a broad range of subsurface applications.

Indexed as

Fracture extractionLattice Boltzmann methodMulti-direction slicingU-net segmentationX-ray computed tomography

Identifiers

PMID40914423
PMCPMC13154622

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