
科学技术与工程

中国技术经济学会
Influencing Factors of Coal Seam Gas Content Based on Feature Derivation and Machine Learning
Jian-hui WANG , Fu-jun WANG , Wen CHEN , Xiao-jun WANG , Xuan WU , Ming-jie LI , Han ZHOU , Cheng-deng GAO
Science Technology and Engineering ›› 2025, Vol. 25 ›› Issue (23) : 9748 -9758.
With the increase of mining depth, the underground environment becomes increasingly complex. In order to clarify the impact of various factors on the changes in coal seam gas content. Firstly, the factors affecting coal seam gas content were processed using feature derivation and tree-structured parzen estimator (TPE). Then, 11 new features were derived using polynomial feature derivation, cross combination feature derivation, and grouping statistical feature derivation. The factors affecting gas content were studied using decision tree and random forest algorithm. The results show that the top four influencing factors in the decision tree model were new feature 9 (mudstone thickness × coal mine structure), new feature 5 (sandstone to mudstone ratio × mudstone thickness), new feature 1 (sandstone to mudstone ratio × sandstone to mudstone ratio), and new feature 3 (volatile matter × volatile matter), with a mean square error (MSE) of 0.069 2 and a coefficient of determination (R2) value of 0.942 3. The top four influencing factors in the random forest model are new feature 9 (mudstone thickness × coal mine structure), new feature 1 (sandstone to mudstone ratio × sandstone to mudstone ratio), sandstone to mudstone ratio, and mudstone thickness. The MSE is 0.0458 3 and R2 is 0.961 8. The top four influencing factors in the TPE hyperparameter optimized random forest model are new feature 9 (mudstone thickness × coal mine structure), new feature 6 (sandstone shale ratio × volatile matter), sandstone shale ratio, and new feature 7 (sandstone shale ratio × coal mine structure). The MSE is 0.015 66 and R2 is 0.990 9. The mean square error predicted by the decision tree, random forest, and TPE tuning random forest algorithms gradually decreases, while the R2 value gradually increases. Among the three model algorithms, the new feature 9 has the highest weight and is the most important influencing factor; Mudstone thickness is included in the weight ranking of factors 2-4, indicating that shale thickness is also an important influencing factor on coal seam gas content.
decision tree / random forest / feature derivation / TPE / gas content
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