Logging response prediction of high-lithium coal seam based on K-means clustering algorithm - Mu, Xiwei; Zhu, Yanming; Dou, Kailong; Shi, Ying; Huang, Manli (Frontiers in Earth Science, 2024)
Lithium in coal, as a new type of associated mineral resource, has considerable potential for exploration. Exploration of high-lithium coal seams is essential for developing and using the associated lithium resources in coal. To explore the distribution of lithium resources in coal seams in the early stages of development, this study analyzed the relationship between coal seam logging data and lithium content. By analyzing the correlation between the different logging curves and the lithium content in coal and combining the K-means algorithm to identify the logging characteristics of different lithium-containing coal seams, we finally obtained the logging identification characteristics of high-lithium coal seams. The results reveal differences in the logging curves of coal seams with different lithium contents. The natural gamma and lateral resistivity of high-lithium coal seams are approximately 80 API and 100 Ω.M, respectively. This study proposes a method for the preliminary identification of high-lithium coal seams based on well-logging curve parameters through cluster analysis of borehole logging data to achieve accurate predictions of high-lithium coal seams.