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首页> 外文期刊>Interpretation >Attribute selection in seismic facies classification: Application to a Gulf of Mexico 3D seismic survey and the Barnett Shale
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Attribute selection in seismic facies classification: Application to a Gulf of Mexico 3D seismic survey and the Barnett Shale

机译：地震相的属性选择分类：应用于墨西哥湾3D地震调查和Barnett Shale的应用

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摘要

Automated seismic facies classification using machine-learning algorithms is becoming more common in the geophysics industry. Seismic attributes are frequently used as input because they may express geologic patterns or depositional environments better than the original seismic amplitude. Selecting appropriate attributes becomes a crucial part of the seismic facies classification analysis. For unsupervised learning, principal component analysis can reduce the dimensions of the data while maintaining the highest variance possible. For supervised learning, the best attribute subset can be built by selecting input attributes that are relevant to the output class and avoiding using redundant attributes that are similar to each other. Multiple attributes are tested to classify salt diapirs, mass transport deposits (MTDs), and the conformal reflector "background" for a 3D seismic marine survey acquired on the northern Gulf of Mexico shelf. We have analyzed attribute-to-attribute correlation and the correlation between the input attributes to the output classes to understand which attributes are relevant and which attributes are redundant. We found that amplitude and texture attribute families are able to differentiate salt, MTDs, and conformal reflectors. Our attribute selection workflow is also applied to the Barnett Shale play to differentiate limestone and shale facies. Multivariate analysis using filter, wrapper, and embedded algorithms was used to rank attributes by importance, so then the best attribute subset for classification is chosen. We find that attribute selection algorithms for supervised learning not only reduce computational cost but also enhance the performance of the classification.
机译：使用机器学习算法进行自动地震相分类在地球物理行业中变得越来越普遍。地震属性经常用作输入，因为它们可以表达比原始地震幅度更好的地质图案或沉积环境。选择适当的属性成为地震相分析分析的重要部分。对于无监督的学习，主成分分析可以减少数据的尺寸，同时保持最高方差。对于监督学习，可以通过选择与输出类相关的输入属性并避免使用彼此类似的冗余属性来构建最佳属性子集。测试多个属性以对墨西哥北部湾北部的3D地震海洋调查进行分类以分类盐涂抹物，大规模运输沉积物（MTD），以及共形反射器“背景”。我们已经分析了属性到属性的相关性，并将输入属性与输出类之间的相关性，以了解哪些属性是相关的，哪些属性是冗余的。我们发现幅度和纹理属性系列能够区分盐，MTD和保形反射器。我们的属性选择工作流还应用于Barnett Shale Play以区分石灰石和页岩相。使用过滤器，包装器和嵌入算法的多变量分析用于按重要性对属性进行排名，因此选择了分类的最佳属性子集。我们发现用于监督学习的属性选择算法不仅可以降低计算成本，而且还提高了分类的性能。

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  • 来源
    《Interpretation》 |2019年第3期|共17页
  • 作者

    Kim Yuji; Hardisty Robert; Marfurt Kurt J.;

  • 作者单位

    Univ Oklahoma ConocoPhillips Sch Geol &

    Geophys Norman OK 73019 USA;

    Univ Oklahoma ConocoPhillips Sch Geol &

    Geophys Norman OK 73019 USA;

    Univ Oklahoma ConocoPhillips Sch Geol &

    Geophys Norman OK 73019 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 地球物理学;
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