Coal texture is important for predicting coal seam permeability and selecting favorable blocks for coalbed methane (CBM) exploration. Drilled cores and mining seam observations are the most direct and effective methods of identifying coal texture; however, they are expensive and cannot be used in unexplored coal seams. Geophysical logging has become a common method of coal texture identification, particularly during the CBM mining stage. However, quantitative methods for identifying coal texture based on geophysical logging data require further study. The support vector machine (SVM), a machine-learning method, has received great interest due to its remarkable generalization performance, and it has been used to quantitatively identify hard and soft coal using geophysical logging data. In this study, four well-logging curves, the acoustic time difference (AC), caliper log (CAL), density (DEN), and natural gamma (GR), were used for coal texture analysis. Hard coal (undeformed and cataclastic coal) exhibited higher DEN, GR, lower CAL, and lower AC than soft coal. The accuracy rate of coal texture identification was highest (97%) when the linear kernel function was applied, and the maximum training accuracy rate was achieved when the penalty parameter value of the linear kernel increased to 1. The results of verification with a newly cored CBM exploration well indicated that the SVM-based identification method was effective for coal texture analysis. With the increasing availability of data, this method can be used to distinguish hard and soft coal in a coal-bearing basin under numerous sample learning conditions.

As a crucial component in mining engineering, the instability of waste dumps can lead to severe engineering accidents and significant economic losses. This study focuses on the stability of steep slopes in open-pit waste dumps, particularly under short-duration heavy rainfall conditions, and analyzes the stability performance of slopes with different slope ratios. Using a manganese mine waste dump in Guangxi Province as a case study, a 2D numerical model was developed using GeoStudio software (2022.1). The model incorporated local soil parameters and rainfall data to calculate the safety factors of single-step slopes with heights ranging from 5 to 30 m under the maximum local rainfall, which lasted for 10 h. The slope ratios considered were 1:1.5, 1:1.75, 1:2.0, and 1:2.25. The study found that as the slope ratio and rainfall duration increased, the stability of the slope significantly decreased. For slope ratios of 1:1.5, 1:1.75, and 1:2.0, the safety factors dropped below 1.1 as the step height increased. However, for slopes with a ratio of 1:2.25 and step heights ≤ 30 m, the safety factors remained above 1.1, meeting the stability requirements. This research provides a theoretical basis for addressing the stability issues of waste dumps in rainfall-prone regions and for the implementation of stabilization measures in single-step waste dump slopes.