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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Salt-Dome Detection Using a Codebook-Based Learning Model
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Salt-Dome Detection Using a Codebook-Based Learning Model

机译：使用基于码本的学习模型进行盐球检测

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摘要

In this letter, we present a novel supervised codebook-based learning model for salt-dome detection in seismic imaging using texture-based attributes. The proposed algorithm is data driven and overcomes the limitations of existing texture-attributes-based salt-dome detection techniques which are heavily dependent upon the relevance of attributes to the geological nature of salt domes and the number of attributes used for classification. The algorithm works by combining the attributes from the gray-level cooccurrence matrix (GLCM) and those from the Gabor filter, with a codebook-based learning approach to delineate salt boundaries in seismic data. The combination of GLCM- and Gabor-filter-based attributes ensures that the algorithm works well even in the absence of strong reflectors along the salt boundary. Contrary to existing salt-dome detection techniques, our algorithm works with a codebook of small size and is shown to be robust and computationally efficient. The learning properties of the codebook-based model make the algorithm flexible and adaptable to the nature of time-scale varying data acquired in seismic surveys. We used the Netherlands F3 block to evaluate the performance of the proposed algorithm. Our experimental results show that the proposed codebook-based workflow can detect salt domes with good accuracy, superior to existing salt-dome detection techniques.
机译：在这封信中，我们提出了一种新的基于监督码本的学习模型，用于使用基于纹理的属性在地震成像中检测盐球。所提出的算法是数据驱动的，并且克服了现有的基于纹理属性的盐球检测技术的局限性，后者在很大程度上取决于属性与盐穹顶的地质性质的相关性以及用于分类的属性的数量。该算法的工作原理是将灰度共生矩阵（GLCM）和Gabor滤波器的属性与基于密码本的学习方法相结合，以描述地震数据中的盐分边界。基于GLCM和Gabor滤波器的属性的组合确保了算法即使在沿盐边界没有强反射器的情况下也能很好地工作。与现有的盐球检测技术相反，我们的算法与小码本一起工作，并且显示出鲁棒性和计算效率。基于密码本的模型的学习特性使该算法灵活且适应于地震勘测中获取的时标变化数据的性质。我们使用荷兰F3块来评估所提出算法的性能。我们的实验结果表明，所提出的基于密码本的工作流程可以以较高的精度检测盐丘，优于现有的盐丘检测技术。

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