矿业科学学报2025，Vol.10Issue(5)：821-833,13.DOI:10.19606/j.cnki.jmst.2025075
基于频谱声学方法的深部掘进工作面瓦斯浓度预测研究
Prediction of gas concentration in deep heading face based on the frequency spectrum acoustic method
摘要
Abstract
Accurate gas concentration prediction is crucial for preventing dynamic disasters in deep coal mines.To address the limitations of current prediction methods that rely heavily on historical data and dynamic-static parameters,the frequency spectrum acoustic method employed in Russian coal mines was introduced.Indicators closely related to gas concentration were screened from artificial acoustic sig-nal indicators of the frequency spectrum acoustic method using Grey Relational Analysis(GRA)and Hierarchical Cluster Analysis(HCA).The iterative annealing strategy of the Simulated Annealing Al-gorithm(SAA)was applied to determine the number of modal components(K)and the penalty coeffi-cient(α)in Variational Mode Decomposition(VMD).The optimized VMD was then used to decom-pose noisy gas concentration signals into several relatively stable intrinsic mode functions with different frequencies.The optimal smoothing factor(σ)of the Generalized Regression Neural Network(GRNN)was identified through the stochastic perturbation strategy of SAA.The refined GRNN model was uti-lized to effectively predict each modal component and reconstruct the prediction results.The findings demonstrate that the"decomposition-prediction-reconstruction"mechanism effectively suppresses noise interference and significantly reduces nonlinear complexity,thereby enhancing prediction accuracy.Compared with four alternative models,the VMD-SAA-GRNN model based on the frequency spectrum acoustic method exhibits superior generalization capability and higher precision in dynamic gas concen-tration prediction,providing a valuable reference for gas control in deep tunneling faces.关键词
瓦斯浓度预测/频谱声学方法/变分模态分解/模拟退火算法/广义回归神经网络Key words
gas concentration prediction/the frequency spectrum acoustic method/variational mode decomposition/simulated annealing algorithm/generalized regression neural network分类
矿山工程引用本文复制引用
张建国,王文唱,魏风清,冯志成,闵瑞..基于频谱声学方法的深部掘进工作面瓦斯浓度预测研究[J].矿业科学学报,2025,10(5):821-833,13.基金项目
国家自然科学基金联合基金(U23A20600) （U23A20600）
河南省科技攻关(252102320335) （252102320335）
