Quantifying the Texture of Coal Images with Different Lithotypes through Gray-Level Co-Occurrence Matrix - SME Annual Meeting 2024

The Coal Pillar Rib Rating (CPRR) technique has been developed to assist in rib support design in underground coal mines. One major challenge of the data collection process is the measurement of coal strengths in the field. Schmidt hammer has been verified as a useful tool to determine coal strength. An alternative approach is to obtain the representative strength of coal mass by determining the coal lithotypes in the field based on the coal brightness profile by experienced geologists or mining engineers. In this paper, image processing techniques have been used to quantify the texture of coal images of different lithotypes with the purpose of classifying coal lithotypes. The coal images were collected from the pillar ribs with exposed surfaces in underground coal mines, and the coal lithotypes were identified when taking the images. The method of Gray-Level Co-Occurrence Matrix (GLCM) was used to analyze the textures of coal images of different lithotypes, and the texture parameters, such as contrast, homogeneity, energy, and entropy, were compared. The results show that the images of coal with different lithotypes have different textures, which can be quantified through the image processing. The results from this study demonstrate the potential of classifying coal lithotypes using rib photos and easing the data collection process of CPRR.