Abstract
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.
Highlights
The structural feature and mechanical property of fractures, absciss layers and other structural planes are significant to study the geological stability, engineering design and construction safety [1,2]
With analyzing the characteristics of borehole images obtained by Axial View Panoramic Borehole Televiewer (APBT), we propose an automatic two-stage classification system to classify three dominant types of borehole-wall images, namely, border images, fracture images and intact rock mass images by using support vector machines (SVM) [22], which replaces traditional classification method to improve the classification accuracy
We directly extract the features of all three types of images for classification without the method of two-stage, and choose three different feature extraction methods: Algorithm (1): In this method, five texture features of original images extracted by Gray level co-occurrence matrix (GLCM) combined with two gray features constitute the feature vector, which is selected as an input for the SVM classifier
Summary
The structural feature and mechanical property of fractures, absciss layers and other structural planes are significant to study the geological stability, engineering design and construction safety [1,2]. The core boring method [3] is a traditional way to analyze the geological condition, which is characterized by heavy workload, low efficiency and difficulty in obtaining the cores of weak layers such as broken mudded intercalation and weathered interlayer A two-stage classification method for borehole-wall images introduced into the geological exploration in 1950s to directly observe the internal structure of geological bodies [4,5] Thereafter, this technique has experienced about 3 phases, namely, Borehole Photo Camera(BPC), Borehole Televiewer (BTV) and Digital Borehole Optical Televiewer (DBOT). It can be directly applied to horizontal holes and inclined holes, etc. [6]
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