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Improved Automatic Crack Identification for Electrical Imaging Logging Using PSPNet
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Ke SHEN1, Xiao-ling XIAO1, 2, Xiang ZHANG2, *, Mao-shan LIN3
Science Technology and Engineering | 2025, 25(7) : 2691 - 2702
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Science Technology and Engineering | 2025, 25(7): 2691-2702
Papers·Astronomy and Geosciences
Improved Automatic Crack Identification for Electrical Imaging Logging Using PSPNet
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Ke SHEN1, Xiao-ling XIAO1, 2, Xiang ZHANG2, *, Mao-shan LIN3
Affiliations
  • 1 School of Computer Science, Yangtze University, Jingzhou 434023, China
  • 2 Oil and Gas Resources and Exploration Technology, Ministry of Education, Yangtze University, Wuhan 430100, China
  • 3 Tuha Branch, China National Logging Corporation, Hami 839000, China
Published: 2025-03-08 doi: 10.12404/j.issn.1671-1815.2309833
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An improved PSPNet(pyramid scene parseing network) network was proposed to automatically identify fractures in electrical imaging logging images, which was difficult to extract fracture features and led to low segmentation accuracy and large calculation of network parameters. Firstly, the backbone network in PSPNet was replaced with the optimized MobileNetV3 network, which could significantly reduce the number of network parameters and the amount of computation. Secondly, the asymptotic feature pyramid network(AFPN) was introduced to increase the interaction of multi-scale information and enhance the recognition ability of small cracks. Then, multi-depthwise Conv head transposed attention(MDTA) was introduced to extract global features and improve the extraction ability of key information. Finally, the combination of Focal Loss and Dice Loss were used as a loss function to solve the problem of unbalanced proportion of data sets. The experimental results show that the improved PSPNet network has a good segmentation effect on the fracture in the electrical imaging logging. Compared with the PSPNet network, mIoU(mean intersection over union) improved by 3.17% and mPA(mean pixel accuracy) improved by 6.38%. In addition, the number of parameters, calculation amount and weight of the proposed algorithm are reduced by 94.3%, 95.7% and 93.8% respectively compared with the original model. At the same time, the crack identification system based on CIFLog is developed, which can meet the practical needs of the electrical imaging logging.

PSPNet  /  fracture identification  /  electrical imaging logging image  /  MobileNetV3  /  asymptotic feature pyramid network(AFPN)
Ke SHEN, Xiao-ling XIAO, Xiang ZHANG, Mao-shan LIN. Improved Automatic Crack Identification for Electrical Imaging Logging Using PSPNet[J]. Science Technology and Engineering, 2025 , 25 (7) : 2691 -2702 . DOI: 10.12404/j.issn.1671-1815.2309833
Year 2025 volume 25 Issue 7
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Article Info
doi: 10.12404/j.issn.1671-1815.2309833
  • Receive Date:2023-12-13
  • Online Date:2026-03-30
  • Published:2025-03-08
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  • Received:2023-12-13
  • Revised:2024-07-09
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Affiliations
    1 School of Computer Science, Yangtze University, Jingzhou 434023, China
    2 Oil and Gas Resources and Exploration Technology, Ministry of Education, Yangtze University, Wuhan 430100, China
    3 Tuha Branch, China National Logging Corporation, Hami 839000, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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