基于分峰思想的煤岩显微组分识别与统计分析

针对现有方法识别煤岩显微组分准确率低的问题,文中提出了一种基于分峰思想的煤岩显微组分识别与统计分析方法。文中从单颗粒角度确定各煤种的镜质组峰值偏移范围,并提出自适应寻峰算法选取煤岩颗粒的有效峰值点。在煤岩显微组分识别阶段设计多策略的分峰峰位识别算法将煤岩颗粒分类为需要分峰聚类的活惰结合颗粒和无需分峰的纯镜质组颗粒、惰质组颗粒和壳质组颗粒,确定需要分峰聚类煤岩颗粒的分峰峰位,然后基于分峰规则和统计学方法进行高斯拟合,分别确定壳质组阈值、镜质组阈值和惰质组阈值,完成各煤岩颗粒的聚类分割。实验结果表明,文中方法能够有效识别单个煤岩颗粒并实现显微组分含量的定量统计,准确率达到 96.85%,熵值最小低至 0.615 3,与传统方法相比准确性更高,具有较好的现实应用意义。

Identification and Statistical Analysis of Coal Macerals Based on the Idea of Peak Splitting

In view of the low accuracy of the existing methods to identify coal macerals, a method for identifying and statistical analysis of coal macerals based on the idea of peak splitting is proposed in this study. The peak offset range of vitrinite of each coal type is determined from the point of view of individual particle, and an adaptive peak finding algorithm is proposed to select the effective peak point of coal and rock particles. In the coal macerals identification stage, the multi-strategy peak position identification algorithm is designed to classify the coal and rock particles into active-inert particles requiring peak clustering and pure vitrinite particles, inertinite particles and exinite particles without peak clustering, and the peak positions of coal and rock particles requiring peak clustering are selected. Then, Gaussian fitting is carried out based on peak splitting rules and statistical methods to determine the threshold values of exinite, vitrinite, and inertinite respectively, and complete the clustering and segmentation of coal and rock particles. The experimental results show that the proposed method can effectively identify single coal particles and realize quantitative statistics of maceral content, with an accuracy of 96.85% and the minimum entropy of 0.615 3. Compared with the traditional method, the proposed method has higher accuracy and has better practical application significance.