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Macroscopic Rock Texture Image Classification Using a Hierarchical Neuro-Fuzzy Class Method

机译：层次神经模糊类方法对岩石宏观图像进行分类

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摘要

We used a Hierarchical Neuro-Fuzzy Class Method based on binary space partitioning (NFHB-Class Method) for macroscopic rock texture classification. The relevance of this study is in helping Geologists in the diagnosis and planning of oil reservoir exploration. The proposed method is capable of generating its own decision structure, with automatic extraction of fuzzy rules. These rules are linguistically interpretable, thus explaining the obtained data structure. The presented image classification for macroscopic rocks is based on texture descriptors, such as spatial variation coefficient, Hurst coefficient, entropy, and cooccurrence matrix. Four rock classes have been evaluated by the NFHB-Class Method: gneiss (two subclasses), basalt (four subclasses), diabase (five subclasses), and rhyolite (five subclasses). These four rock classes are of great interest in the evaluation of oil boreholes, which is considered a complex task by geologists. We present a computer method to solve this problem. In order to evaluate system performance, we used 50 RGB images for each rock classes and subclasses, thus producing a total of 800 images. For all rock classes, the NFHB-Class Method achieved a percentage of correct hits over 73%. The proposed method converged for all tests presented in the case study.
机译：我们使用基于二进制空间划分的分层神经模糊类方法（NFHB类方法）对宏观岩石纹理进行分类。这项研究的意义在于帮助地质学家诊断和规划油藏勘探。该方法能够自动提取模糊规则，从而生成自己的决策结构。这些规则在语言上是可解释的，从而解释了获得的数据结构。提出的宏观岩石图像分类基于纹理描述符，例如空间变化系数，赫斯特系数，熵和共生矩阵。通过NFHB-Class方法评估了四个岩石类别：片麻岩（两个子类别），玄武岩（四个子类别），辉绿岩（五个子类别）和流纹岩（五个子类别）。这四个类别的岩石对油井的评估非常感兴趣，地质学家认为这是一项复杂的任务。我们提出一种解决该问题的计算机方法。为了评估系统性能，我们为每个岩石类和子类使用了50张RGB图像，因此总共产生了800张图像。对于所有岩石类别，NFHB类方法的正确命中百分比均超过73％。所提出的方法适用于案例研究中提出的所有测试。

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  • 来源
    《Mathematical Problems in Engineering》 |2010年第2期|p.27.1-27.23|共23页
  • 作者

    Laercio B. Goncalves; Fabiana R. Leta;

  • 作者单位

    Computational and Dimensional Metrology Laboratory (LMDC), Mechanical Engineering Department (PGMEC), Universidade Federal Fluminense (UFF), R. Passo da Pdtria, 156, Niteroi, Rio de Janeiro,24210-240, Brazil,Computer Department, Centro Federal de Educacao Tecnologica Celso Suckow da Fonseca (CEFET-RJ),Av. Maracana, 229, Rio de Janeiro, 20271-110, Brazil;

    Computational and Dimensional Metrology Laboratory (LMDC), Mechanical Engineering Department (PGMEC), Universidade Federal Fluminense (UFF), R. Passo da Pdtria, 156, Niteroi, Rio de Janeiro,24210-240, Brazil;

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