LAUSR

Abstract With the development of programming technology and high resolution image analysis, automated image analysis and recognition technology is used in a variety of disciplines, including modern sedimentary. On the basis of cathodeluminescence (CL) images and the algorithm and functions in MATLAB, this paper establishes an analysis method of quantitative calculation for the distribution, content and radius of siliceous cement, calcareous cement, kaolinite cement with various occurrence types in sandstone. Taking the Upper Paleozoic sandstone of Ordos Basin as the research object, the CL image was converted to the binary image which contained the color and pixel information of cement by the built-in application “Color Thresholder” in MATLAB. Further, through the functions such as ‘bwarea’, ‘bwlabel’, ‘bwboundaries’, the area in which the cement feature pixels were stored was calculated and analyzed in turn. The results show that this characterization method can automatically distinguish and calculate the content and radius of various cementation, even quartz overgrowth, in different diagenetic periods. The cement radius of quartz overgrowth, calcite and kaolinite are distributed at 0–150 μm, 0–300 μm and 0–200 μm. Comparing the content results measured by X-ray diffraction (X-RD) analysis with CL-MATLAB algorithm, the calculation results with a single CL image are suitable for siliceous cement, grain-replaced calcareous cement and pore-cemented kaolinite with linear coefficient R2 0.841, 0.562 and 0.236. For the radius distribution of cement, the average grain radius and pore radius, corresponding to the grain-replaced cementaion and the porous cementation, are used as the comparison parameters of cement radius. The appropriate objects for the method are grain-replaced and porous calcareous cement and pore-cemented kaolinite cement with R2, 0.7797, 0.4987 and 0.289. In addition, four color modes of image binarization, YCbCr, RGB, L*a*b*, HSV, are explored in this paper. It is clarified that the calcareous cements with orange-red color are most suitable under L*a*b* mode, while the siliceous and kaolinte with blue-purple color are most appropriate under RGB mode. The analysis method of CL image -MATLAB algorithm can not only accurately identify and quantitatively output the distribution, content and pore radius of cements in a single CL image, but also reflect the distribution characteristics of cements of the whole formation with superposed results of all the samples.