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Correlation methods of base-level cycle based on wavelet neural network

机译:基于小波神经网络的基级循环相关方法

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摘要

The authors discussed the method of wavelet neural network (WNN) for correlation of base-level cycle. A new vectored method of well log data was proposed. Through the training with the known data set, the WNN can remenber the cycle pattern characteristic of the well log curves. By the trained WNN to identify the cycle pattern in the vectored log data, the ocrrelation process among the well cycles was completed. The application indicates that it is highly efficient and reliable in base-level cycle correlation.
机译:作者讨论了用于基级循环相关性的小波神经网络(WNN)方法。提出了一种新的矢量测井数据方法。通过使用已知数据集进行训练,WNN可以修正测井曲线的循环模式特征。通过训练后的WNN识别矢量测井数据中的循环模式,完成了井眼循环之间的关联过程。该应用表明,它在基本级循环关联中是高效且可靠的。

著录项

  • 来源
    《世界地质(英文版)》 |2007年第1期|25-28|共4页
  • 作者

  • 作者单位

    College of Earth Sciences, Jilin University, Changchun 130061, China;

    College of Earth Sciences, Jilin University, Changchun 130061, China;

    College of Earth Sciences, Jilin University, Changchun 130061, China;

    College of Earth Sciences, Jilin University, Changchun 130061, China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 chi
  • 中图分类 地质学;
  • 关键词

    wavelet neural network; stratigraphic correlation; base-level cycle; vector;

    机译:小波神经网络地层相关性基层周期矢量;
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