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Intelligent recognition of shale fracture network images based on transfer learning.

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Title: Intelligent recognition of shale fracture network images based on transfer learning.
Authors: Wang, Qin1 (AUTHOR) wangqin0131@163.com, Hu, Jiangchun1 (AUTHOR), Liu, PengFei1 (AUTHOR), Sun, GuangLin1 (AUTHOR)
Source: Earth Science Informatics. Feb2024, Vol. 17 Issue 1, p797-812. 16p.
Subject Terms: *Shale, *Transfer of training, *Deep learning, *Bridge floors, *Image recognition (Computer vision), *Image processing, *Intelligent tutoring systems, *Intelligent transportation systems
Abstract: This study presents an intelligent identification method for shale crack networks based on transfer learning. Focuses on investigating the main physical parameters of shale similar materials based on the similarity theory and the physical parameters of shale. Fracture network images obtained from shale-like materials. The fracture network images are then preprocessed using image processing technology to generate a high-quality shale image dataset. A deep learning transfer recognition model, based on ResNet-50, is constructed to detect model performance using these shale fracture network images. Experimental results and reliability analyses demonstrate that the ResNet-50 based deep learning transfer model achieves an accuracy of 87% for shale images, indicating high recognition accuracy and fast model convergence. The proposed intelligent shale crack identification method exhibits robustness and generalization ability, making it suitable for efficient fracture identification in geological, road, and bridge deck projects. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 175021582
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Intelligent recognition of shale fracture network images based on transfer learning.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Qin%22">Wang, Qin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wangqin0131@163.com</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Jiangchun%22">Hu, Jiangchun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+PengFei%22">Liu, PengFei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+GuangLin%22">Sun, GuangLin</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Earth+Science+Informatics%22">Earth Science Informatics</searchLink>. Feb2024, Vol. 17 Issue 1, p797-812. 16p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Shale%22">Shale</searchLink><br />*<searchLink fieldCode="DE" term="%22Transfer+of+training%22">Transfer of training</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Bridge+floors%22">Bridge floors</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Intelligent+tutoring+systems%22">Intelligent tutoring systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Intelligent+transportation+systems%22">Intelligent transportation systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study presents an intelligent identification method for shale crack networks based on transfer learning. Focuses on investigating the main physical parameters of shale similar materials based on the similarity theory and the physical parameters of shale. Fracture network images obtained from shale-like materials. The fracture network images are then preprocessed using image processing technology to generate a high-quality shale image dataset. A deep learning transfer recognition model, based on ResNet-50, is constructed to detect model performance using these shale fracture network images. Experimental results and reliability analyses demonstrate that the ResNet-50 based deep learning transfer model achieves an accuracy of 87% for shale images, indicating high recognition accuracy and fast model convergence. The proposed intelligent shale crack identification method exhibits robustness and generalization ability, making it suitable for efficient fracture identification in geological, road, and bridge deck projects. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s12145-023-01202-5
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 797
    Subjects:
      – SubjectFull: Shale
        Type: general
      – SubjectFull: Transfer of training
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Bridge floors
        Type: general
      – SubjectFull: Image recognition (Computer vision)
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Intelligent tutoring systems
        Type: general
      – SubjectFull: Intelligent transportation systems
        Type: general
    Titles:
      – TitleFull: Intelligent recognition of shale fracture network images based on transfer learning.
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            NameFull: Wang, Qin
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            NameFull: Hu, Jiangchun
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            NameFull: Liu, PengFei
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            NameFull: Sun, GuangLin
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          Dates:
            – D: 01
              M: 02
              Text: Feb2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 18650473
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              Value: 17
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              Value: 1
          Titles:
            – TitleFull: Earth Science Informatics
              Type: main
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