A method for intelligent information extraction of coal fractures based on µCT and deep learning
ObjectiveThe fine-scale characterization of fractures in coal reservoirs is significant for the exploration and exploitation of coalbed methane (CBM) resources. Given that the size, orientation, and density of fractures directly affect the permeability of coal seams, the accurate information identif...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Coal Geology & Exploration
2025-02-01
|
| Series: | Meitian dizhi yu kantan |
| Subjects: | |
| Online Access: | http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.09.0609 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850052285091020800 |
|---|---|
| author | Zhazha HU Xun ZHANG Yi JIN Linxian GONG Wenhui HUANG Jianji REN Norbert Klitzsch |
| author_facet | Zhazha HU Xun ZHANG Yi JIN Linxian GONG Wenhui HUANG Jianji REN Norbert Klitzsch |
| author_sort | Zhazha HU |
| collection | DOAJ |
| description | ObjectiveThe fine-scale characterization of fractures in coal reservoirs is significant for the exploration and exploitation of coalbed methane (CBM) resources. Given that the size, orientation, and density of fractures directly affect the permeability of coal seams, the accurate information identification and extraction of fractures in coal seams plays a key role in revealing the formation and propagation mechanisms of fracture networks during reservoir volume fracturing. Conventional methods for fracture information extraction typically rely on manual labeling and feature extraction based on image processing techniques, exhibiting significantly limited accuracy and efficiency. Methods This study proposed a method for fracture information extraction of coals based on TransUNet and micro-computed tomography (µCT) images. TransUNet, integrating the advantages of both the Transformer modules and convolutional neural network (CNN), is capable of extracting global features and capturing local details in images, significantly enhancing the image segmentation accuracy and network robustness. First, the µCT images of coal samples were preprocessed, including improving the image quality using the difference method and increasing the sample size using data augmentation techniques. Subsequently, image segmentation was conducted using TransUNet to extract fracture features. Additionally, the image segmentation results of varying neural network models were compared. Results and ConclusionsThe results indicate that the proposed method exhibited superior performance on a given dataset. Specifically, the TransUNet model yielded an accuracy of 91.3%, precision of 89.5%, F1 score of 89.8%, and Intersection over Union (IoU) of 84.0%, significantly outperforming other intelligent models like U-Net and U-Net++. Given the characteristics of fine-grained µCT images, applying TransUNet to the fracture information extraction of coals emerges as an efficient and accurate approach. This study provides a novel philosophy for image processing in the field of CBM exploration and production. |
| format | Article |
| id | doaj-art-824c4471ec2d418f92aa8ab773db5afb |
| institution | DOAJ |
| issn | 1001-1986 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | Editorial Office of Coal Geology & Exploration |
| record_format | Article |
| series | Meitian dizhi yu kantan |
| spelling | doaj-art-824c4471ec2d418f92aa8ab773db5afb2025-08-20T02:52:52ZzhoEditorial Office of Coal Geology & ExplorationMeitian dizhi yu kantan1001-19862025-02-01532556610.12363/issn.1001-1986.24.09.060924-09-0609-Hu-ZhazhaA method for intelligent information extraction of coal fractures based on µCT and deep learningZhazha HU0Xun ZHANG1Yi JIN2Linxian GONG3Wenhui HUANG4Jianji REN5Norbert Klitzsch6Science and Technology R&D Platform of Emergency Management Ministry for Deep Well Ground Control and Gas Extraction Technology, School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, ChinaScience and Technology R&D Platform of Emergency Management Ministry for Deep Well Ground Control and Gas Extraction Technology, School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, ChinaSchool of Resources and Environment, Henan Polytechnic University, Jiaozuo 454003, ChinaSchool of Resources and Environment, Henan Polytechnic University, Jiaozuo 454003, ChinaSchool of Energy, China University of Geosciences (Beijing), Beijing 100083, ChinaSchool of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, ChinaInstitute of Applied Geophysics and Geothermal Energy, RWTH Aachen University, Aachen 52074, GermanyObjectiveThe fine-scale characterization of fractures in coal reservoirs is significant for the exploration and exploitation of coalbed methane (CBM) resources. Given that the size, orientation, and density of fractures directly affect the permeability of coal seams, the accurate information identification and extraction of fractures in coal seams plays a key role in revealing the formation and propagation mechanisms of fracture networks during reservoir volume fracturing. Conventional methods for fracture information extraction typically rely on manual labeling and feature extraction based on image processing techniques, exhibiting significantly limited accuracy and efficiency. Methods This study proposed a method for fracture information extraction of coals based on TransUNet and micro-computed tomography (µCT) images. TransUNet, integrating the advantages of both the Transformer modules and convolutional neural network (CNN), is capable of extracting global features and capturing local details in images, significantly enhancing the image segmentation accuracy and network robustness. First, the µCT images of coal samples were preprocessed, including improving the image quality using the difference method and increasing the sample size using data augmentation techniques. Subsequently, image segmentation was conducted using TransUNet to extract fracture features. Additionally, the image segmentation results of varying neural network models were compared. Results and ConclusionsThe results indicate that the proposed method exhibited superior performance on a given dataset. Specifically, the TransUNet model yielded an accuracy of 91.3%, precision of 89.5%, F1 score of 89.8%, and Intersection over Union (IoU) of 84.0%, significantly outperforming other intelligent models like U-Net and U-Net++. Given the characteristics of fine-grained µCT images, applying TransUNet to the fracture information extraction of coals emerges as an efficient and accurate approach. This study provides a novel philosophy for image processing in the field of CBM exploration and production.http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.09.0609trans-unetµct imagescoal fractureimage segmentationdeep learning |
| spellingShingle | Zhazha HU Xun ZHANG Yi JIN Linxian GONG Wenhui HUANG Jianji REN Norbert Klitzsch A method for intelligent information extraction of coal fractures based on µCT and deep learning Meitian dizhi yu kantan trans-unet µct images coal fracture image segmentation deep learning |
| title | A method for intelligent information extraction of coal fractures based on µCT and deep learning |
| title_full | A method for intelligent information extraction of coal fractures based on µCT and deep learning |
| title_fullStr | A method for intelligent information extraction of coal fractures based on µCT and deep learning |
| title_full_unstemmed | A method for intelligent information extraction of coal fractures based on µCT and deep learning |
| title_short | A method for intelligent information extraction of coal fractures based on µCT and deep learning |
| title_sort | method for intelligent information extraction of coal fractures based on µct and deep learning |
| topic | trans-unet µct images coal fracture image segmentation deep learning |
| url | http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.09.0609 |
| work_keys_str_mv | AT zhazhahu amethodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning AT xunzhang amethodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning AT yijin amethodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning AT linxiangong amethodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning AT wenhuihuang amethodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning AT jianjiren amethodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning AT norbertklitzsch amethodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning AT zhazhahu methodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning AT xunzhang methodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning AT yijin methodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning AT linxiangong methodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning AT wenhuihuang methodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning AT jianjiren methodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning AT norbertklitzsch methodforintelligentinformationextractionofcoalfracturesbasedonμctanddeeplearning |