煤田地质与勘探2026，Vol.54Issue(3)：126-138,13.DOI:10.12363/issn.1001-1986.25.09.0713
基于小样本数据集的煤层顶板突水溃砂危险性预测
Predicting the risk of water-sand inrushes from coal seam roofs based on a small-size sample dataset
摘要
Abstract
[Objective]The eastern and northern regions of China exhibit thick unconsolidated layers and thin bedrocks,with water-sand inrush disasters occurring frequently.Therefore,accurately predicting the risk of water-sand inrushes from coal seam roofs holds great significance for safe coal mining.However,the water-sand inrushes in these regions exhibit complex disaster-causing mechanisms,which involve the coupling effects of multiple factors.Accordingly,the real-time prediction of the water-sand inrush risk in the field poses challenges including high risk and cost.These issues lead to difficult data acquisition and severely insufficient samples,limiting the accuracy and performance of traditional prediction models.Therefore,there is an urgent need to explore effective prediction methods suitable for a small sample size.[Methods]Based on a review and analysis of the measured field data and historical cases of mining faces adjacent to unconsolidated layers,this study determined 11 factors influencing water-sand inrushes(e.g.,the thickness of aquifers at the unconsolidated-layer bottom and bedrock thickness)and constructed the original sample dataset.Subsequently,the intrinsic relationships and correlations among various influencing factors were discovered using Spearman correlation.A risk prediction model for water-sand inrushes,termed CTGAN-DPSO-RF,was developed based on conditional tabular generative adversarial networks(CTGAN),detecting particle swarm optimization(DPSO)algorithm,and random forest(RF)algorithm.The quality of data synthesized using CTGAN was explored.Furthermore,the effectiveness of the CT-GAN-DPSO-RF model was validated through comparison with the DPSO-SVM and DPSO-XGBoost models,along with two engineering cases.[Results and Conclusions]Among the 11 influencing factors,the caving zone height exhib-ited the strongest correlation with mining height(correlation coefficient:0.93),while the water pressure of aquifers at the unconsolidated-layer bottom presented the weakest correlation with the height of the hydraulically conductive fracture zone.The data synthesized using CTGAN highly resembled the original data,with a comprehensive quality score reach-ing up to 85.03％.The DPSO algorithm yielded an optimal fitness of 0.926 5 after the hyperparameter tuning,outper-forming the particle swarm optimization(PSO)algorithm.The CTGAN-DPSO-RF model yielded accuracy(Ac),weighted precision(Pw),weighted recall(Rw),and weighted F1-score(F1w)consistently reaching 1.0 on the test set,out-performing its counterparts.The risk prediction results of two mining faces derived using the proposed model were con-sistent with actual mining conditions.By synthesizing high-quality data to expand the sample dataset and optimizing hy-perparameters,the proposed model effectively overcomes the low accuracy and poor performance of traditional models under a small sample size,providing a new method for predicting the risk of water-sand inrushes from coal seam roofs under conditions of thick unconsolidated layers and thin bedrocks.关键词
煤层顶板/厚松散层薄基岩/突水溃砂/小样本数据/条件表格生成对抗网络/探测粒子群优化算法/危险性预测Key words
coal seam roof/thick unconsolidated layer and thin bedrock/water-sand inrush/small-size sample data/con-ditional tabular generative adversarial network(CTGAN)/detecting particle swarm optimization(DPSO)algorithm/risk prediction分类
矿业与冶金引用本文复制引用
张文泉,李子旭,朱先祥,邱伟,张承杰..基于小样本数据集的煤层顶板突水溃砂危险性预测[J].煤田地质与勘探,2026,54(3):126-138,13.基金项目
国家自然科学基金区域创新发展联合基金重点项目(U23A20600) （U23A20600）
山东省自然科学基金面上项目(ZR2023ME066) （ZR2023ME066）
