Department of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, Shandong, China.
Department of Electronics, Communication and Physics, Shandong University of Science and Technology, Qingdao, Shandong, China.
PLoS One. 2018 Jun 28;13(6):e0199749. doi: 10.1371/journal.pone.0199749. eCollection 2018.
Analyzing geological drilling hole images acquired by Axial View Panoramic Borehole Televiewer (APBT) is a key step to explore the geological structure in a geological exploration. Conventionally, the borehole images are examined by technicians, which is inefficient and subjective. In this paper, three dominant types of borehole-wall images on coal-rock mass structure, namely, border images, fracture images and intact rock mass images are mainly studied. The traditional image classification methods based on unified feature extraction algorithm and single classifier is not effect for the borehole images. Therefore, this paper proposes a novel two-stage classification approach to improve the classification performance of borehole images. In the first-stage classification, the border images are identified from three kinds of images based on texture features and gray-scale histograms features. For the remaining two types of images, in the second-stage classification, Gabor filter is first applied to segment the region of interest (ROI) (such as microfracture, absciss layer and horizontal cracks, etc.) and the central interference region. Then, using the same feature vector after eliminating the central interference region, fracture images are separated from intact rock mass images. We test our two-stage classification system with real borehole images. The results of experimental show that the two-stage classification method can effectively classify three major borehole-wall images with the correction rate of 95.55% in the first stage and 95% in the second stage.
分析轴向全景钻孔电视（APBT）获取的地质钻孔图像是地质勘探中探索地质结构的关键步骤。传统上，由技术人员检查钻孔图像，这种方法既低效又主观。在本文中，主要研究了煤岩体结构的三种主要类型的钻孔壁图像，即边界图像、裂缝图像和完整岩体图像。基于统一特征提取算法和单一分类器的传统图像分类方法不适用于钻孔图像。因此，本文提出了一种新的两阶段分类方法来提高钻孔图像的分类性能。在第一阶段分类中，基于纹理特征和灰度直方图特征，从三种图像中识别边界图像。对于其余两种类型的图像，在第二阶段分类中，首先应用 Gabor 滤波器来分割感兴趣区域（ROI）（如微裂缝、层理和水平裂缝等）和中心干扰区域。然后，使用消除中心干扰区域后的相同特征向量，将裂缝图像从完整岩体图像中分离出来。我们使用真实的钻孔图像对我们的两阶段分类系统进行了测试。实验结果表明，该两阶段分类方法可以有效地对三种主要的钻孔壁图像进行分类，第一阶段的校正率为 95.55%，第二阶段的校正率为 95%。
