基于图像处理技术的岩体裂隙定量识别方法研究

传统的岩体裂隙特征提取方法较难及时准确地对裂隙特征进行提取,现有数字化处理方式缺乏系统性。笔者提出一种基于图像处理技术的岩体裂隙定量识别与表征方法,在小波分析基础上建立了自动阈值分割算法,对图像裂隙区域进行分割处理。在对图像进行裂隙区域分割处理后,采用区域生长法计算裂隙面积。采用边界跟踪算法提取边缘轮廓,并利用Zhang-Suen细化算法计算裂隙骨架。在此基础上利用动量法拟合骨架切线,并计算切线的法向向量,利用三角函数转化来计算裂隙倾角,结合DDA画线算法对裂隙宽度信息进行提取并计算裂隙宽度。研究结果表明:基于小波分析建立的自动阈值分割算法,在对裂隙区域的分割效果、阈值选取及计算速度上均优于传统的Otsu算法。在裂隙特征值的提取计算上,该方法不仅保证了骨架的平滑度,而且提高骨架的拟合程度,相比于传统方法,该方法计算更加简单,计算量更小。

Study on Quantitative Identification Method of Rock Fracture Based on Image Processing Technology

The traditional rock fracture feature extraction method is difficult to extract the fracture features timely and accurately, and the existing digital processing methods are not systematic. A method of quantitative identification and characterization of rock fractures based on image processing technology is proposed, and an automatic threshold segmentation algorithm is established on the basis of wavelet analysis to segment the fracture area of image. The region growth method is used to calculate the fracture area, after the image is segmented. The boundary tracking algorithm is used to extract the edge contour, and the zhang-Suen algorithm is used to calculate the crack skeleton. The momentum method is used to fit the skeleton tangent line, and the normal vector of the tangent line is calculated. The trigonometric function is transformed to calculate the crack inclination angle, and the crack width information is extracted and calculated with the DDA line drawing algorithm. The results show that the automatic threshold segmentation algorithm based on wavelet analysis is superior to the traditional Otsu algorithm in terms of segmentation effect, threshold selection and calculation speed. In the extraction and calculation of fracture characteristic values, this method not only ensures the smoothness of the skeleton, but also improves the fitting degree of the skeleton. This method is simpler and requires less calculation compared with the traditional method.