The Classification of Inertinite Macerals in Coal Based on the Multifractal Spectrum Method

Abstract

Considering the heterogeneous nature and non-stationary property of inertinite components, we propose a texture description method with a set of multifractal descriptors to identify different macerals with few but effective features. This method is based on the multifractal spectrum calculated from the method of multifractal detrended fluctuation analysis (MF-DFA). Additionally, microscopic images of inertinite macerals were analyzed, which were verified to possess the property of multifractal. Simultaneously, we made an attempt to assess the influences of noise and blur on multifractal descriptors; the multifractal analysis was proven to be robust and immune to image quality. Finally, a classification model with a support vector machine (SVM) was built to distinguish different inertinite macerals from microscopic images of coal. The performance evaluation proves that the proposed descriptors based on multifractal spectrum can be successfully applied in the classification of inertinite macerals. The average classification precision can reach 95.33%, higher than that of description method with gray level co-occurrence matrix (GLCM; about 7.99%).

Quantitative Study of the Maceral Groups of Laminae Based on Support Vector Machine

“…In addition, the selection of the kernel function is critical for classification accuracy. Relying on previous experience [17,19], we used the radial basis function as the kernel function. Once the kernel function is selected, SVM has two significant parameters to determine: gamma and penalty parameter.…”

“…Previous studies have made considerable attempts to overcome the challenges related to the quantitative study of coal macerals using computer recognition techniques. In recent years, some computer scientists have utilized support vector machine (SVM), neural networks, and random forest plots to identify as well as quantify some maceral groups and mineral components [16][17][18][19][20][21]. These scholars started from the perspective of improving the accuracy of the algorithm and proposed to achieve satisfactory results.…”