Loading [MathJax]/extensions/MathZoom.js
Advanced Search
Volume 14 Issue 4
. 
Turn off MathJax
Article Contents
Abstract
References
Article Navigation >  Acta Sedimentologica Sinica  > 1996  >  14(4): 154-160

Wang Shouru, Fan Dejiang, Wang Bingzhu. Facies Recognition Using the Neural Networks[J]. Acta Sedimentologica Sinica, 1996, 14(4): 154-160.
Citation: Wang Shouru, Fan Dejiang, Wang Bingzhu. Facies Recognition Using the Neural Networks[J]. Acta Sedimentologica Sinica, 1996, 14(4): 154-160.
shu

Facies Recognition Using the Neural Networks

  • Ocean University of Qingdao 26603

  • Received Date: 1995-01-28
  • Pattern recognition methods are a powerful means in studying facies quantitatively. The neural network is a new method and has many improvements such as parallel processing and plasticity imitating the human brain compared with other pattern recognition methods. Permian carbonate rocks in the central Hubei basin have been identified as carbonate platform facies after traditional facies analysis. It includes five subfacies, i.e. sub-facies of districted depression, slope B, depression, slope A and shallow out. They are different from each other in rock color, mineral components, paleobiology components, rock structures and so on. The application of pattern recognition on the basis of the BP neural network for Permian carbonate rock facies studies, particularly comparison with fuzzy pattern recognition is very successful and inspiring. The correct identification ratios of fuzzy sets and the neural network are both about 75%. And the corret identification ratio of the combination of fuzzy sets and neural networks is 100%.
  • [1] (1) 王硕儒, 刘伸衡, 范德江. 海相碳酸盐岩岩相的模糊模式识别. 沉积学报, 1992, 10 (4).

    (2) MeCormack M C. Seismic trace editing and First break Picking using neural netwo rks. Ex tended Abstracts, 60thSEG Annual Meating, 1990, 321-324.

    (3) 包约翰 (美 ). 自适应模式识别与神经网络. 科学出版社, 1992.

    (4) Lippman R P. IEEE Assp Magazine. 1987, 4 :4-22.

    (5) 王硕儒等. 模糊模式识别在成矿预测中的应用. 地质与勘探, 1990, 26 (4).
  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-040123456
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 9.0 %FULLTEXT: 9.0 %META: 86.6 %META: 86.6 %PDF: 4.4 %PDF: 4.4 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 3.9 %其他: 3.9 %其他: 3.6 %其他: 3.6 %Bruderheim: 0.8 %Bruderheim: 0.8 %Canton: 0.8 %Canton: 0.8 %China: 1.3 %China: 1.3 %Dallas: 1.6 %Dallas: 1.6 %Rochester: 0.5 %Rochester: 0.5 %United States: 1.6 %United States: 1.6 %[]: 0.3 %[]: 0.3 %临沂: 0.3 %临沂: 0.3 %丽水: 0.3 %丽水: 0.3 %兰州: 0.3 %兰州: 0.3 %凉山: 0.3 %凉山: 0.3 %凤凰城: 1.3 %凤凰城: 1.3 %北京: 8.5 %北京: 8.5 %台州: 0.5 %台州: 0.5 %哥伦布: 0.3 %哥伦布: 0.3 %圣彼得堡: 0.8 %圣彼得堡: 0.8 %城南: 0.3 %城南: 0.3 %大庆: 0.3 %大庆: 0.3 %天津: 0.5 %天津: 0.5 %密蘇里城: 1.6 %密蘇里城: 1.6 %山景城: 0.3 %山景城: 0.3 %张家口: 1.8 %张家口: 1.8 %成都: 1.0 %成都: 1.0 %晋中: 0.3 %晋中: 0.3 %杭州: 1.8 %杭州: 1.8 %格里利: 1.6 %格里利: 1.6 %法拉盛: 0.8 %法拉盛: 0.8 %洛杉矶: 1.6 %洛杉矶: 1.6 %济宁: 0.5 %济宁: 0.5 %深圳: 0.3 %深圳: 0.3 %湖州: 1.0 %湖州: 1.0 %牡丹江: 0.3 %牡丹江: 0.3 %纽约: 1.8 %纽约: 1.8 %芒廷维尤: 10.3 %芒廷维尤: 10.3 %芝加哥: 0.5 %芝加哥: 0.5 %苏州: 0.5 %苏州: 0.5 %萨斯卡通: 1.6 %萨斯卡通: 1.6 %衢州: 0.3 %衢州: 0.3 %西宁: 44.4 %西宁: 44.4 %郑州: 0.3 %郑州: 0.3 %其他其他BruderheimCantonChinaDallasRochesterUnited States[]临沂丽水兰州凉山凤凰城北京台州哥伦布圣彼得堡城南大庆天津密蘇里城山景城张家口成都晋中杭州格里利法拉盛洛杉矶济宁深圳湖州牡丹江纽约芒廷维尤芝加哥苏州萨斯卡通衢州西宁郑州
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Get Citation
PDF
XML

Article Metrics

Article views(601) PDF downloads(257) Cited by()

Proportional views
Related
Publishing history
  • Received:  1995-01-28

Facies Recognition Using the Neural Networks

  • Ocean University of Qingdao 26603
  • Received Date: 1995-01-28

Abstract: Pattern recognition methods are a powerful means in studying facies quantitatively. The neural network is a new method and has many improvements such as parallel processing and plasticity imitating the human brain compared with other pattern recognition methods. Permian carbonate rocks in the central Hubei basin have been identified as carbonate platform facies after traditional facies analysis. It includes five subfacies, i.e. sub-facies of districted depression, slope B, depression, slope A and shallow out. They are different from each other in rock color, mineral components, paleobiology components, rock structures and so on. The application of pattern recognition on the basis of the BP neural network for Permian carbonate rock facies studies, particularly comparison with fuzzy pattern recognition is very successful and inspiring. The correct identification ratios of fuzzy sets and the neural network are both about 75%. And the corret identification ratio of the combination of fuzzy sets and neural networks is 100%.

Wang Shouru, Fan Dejiang, Wang Bingzhu. Facies Recognition Using the Neural Networks[J]. Acta Sedimentologica Sinica, 1996, 14(4): 154-160.
Citation: Wang Shouru, Fan Dejiang, Wang Bingzhu. Facies Recognition Using the Neural Networks[J]. Acta Sedimentologica Sinica, 1996, 14(4): 154-160.
shu

    HTML

Reference (1)

Catalog

    Links

    Journal Online

    Contact Us

    • Website Copyright © 2011 Editorial Office of Acta Sedimentologica Sinica
    • Address: 8 Tianshui Middle Road, Lanzhou
    • Post code: 730000   Tel:（0931）8264231
    • 陇ICP备2021001824号-28
    • 甘公网安备 62010202000673号
    Wechat

    Email alert

    RSS

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return