Research Article: A two-stage classification method for borehole-wall images with support vector machine

Date Published: June 28, 2018

Publisher: Public Library of Science

Author(s): Zhaopeng Deng, Maoyong Cao, Laxmisha Rai, Wei Gao, Zhaoqing Pan.


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.

Partial Text

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]. In geological exploration, 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. To overcome these shortages, Borehole Camera Technology (BCT) was 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). BPC uses photographic film to take static photos of the borehole-wall, which is lack of real-time monitoring capabilities. Currently, Axial View Panoramic Borehole Televiewer (APBT) [6] and Digital Panoramic Borehole Camera System (DPBCS) [7] are the most common techniques for the geological borehole observation. The DPBCS can obtain the section or entire of borehole-wall unrolled image, but the equipment is complex, expensive and only suitable for vertical holes [8, 9]. In contrast, the APBT can generate visualized panoramic images with simple structure, small volume and low cost. Moreover, it can be directly applied to horizontal holes and inclined holes, etc. [6].

In this paper, we mainly concern our study on the classification of the most common three classes of borehole-wall images: border images, fracture images and intact rock mass images. In general, border images are characterized by large portion of bright area and clear contrasts across boundaries corresponding to a high degree of variability in the gray histogram, significantly different from other categories of images. Intact rock mass images are featured by highly homogenous in terms of directionality, granularity, and color. Typically, the differences between fracture images and intact rock mass images are not as clear as the border images. The fracture image is actually the intact rock mass image that exists fracture, abscission layer, and joint, etc. Therefore, the traditional classification methods that the features of all samples are extracted with a unified feature extraction method and then input into a single classifier did not allow us to distinguish the borehole images satisfactorily.

To ensure the validity of the proposed system, this section makes three experiments, which demonstrate the effectiveness of the two-stage classification model and Gabor filter. And moreover, we compare the performances of different classifiers and filtering algorithms in classification of borehole images.

Analyzing images of geological drilling holes is an important and crucial task to explore the geological structure. Several studies have been developed for geological image analysis, but few of them take care about identification and classification of borehole images obtained by Axial View Panoramic Borehole Televiewer (APBT). Therefore, this paper presents a novel two-stage classification approach for the automatic classification of borehole images. It can improve the classification accuracy of borehole images significantly. At the first stage of classification, the border images are recognized by the first level SVM from three types of borehole images based on texture and gray features of original image. Afterwards, in the second-stage classification, the ROI of the fracture images and intact rock mass images are extracted by Gabor filter and image segmentation technology, and then the processed images are well classified by the second SVM.