基于贝叶斯更新的煤矿回采巷道采动变形预测方法  

Prediction method for mining-induced deformation in coal mine roadways based on Bayesian updating

作　　者：张斌 贾海宾 李阿涛 王怀远 陈郁川 于万泉 秦长坤 ZHANG Bin;JIA Haibin;LI Atao;WANG Huaiyuan;CHEN Yuchuan;YU Wanquan;QIN Changkun(ShandongXinjulong Energy Co.,Ltd.,Heze 274918,Shandong,China;Research Center for Rock Burst Prevention and Control of Shandong Energy Group Co.,Ltd.,Jinan 250014,Shandong,China;Shandong Energy Group Luxi Mining Co.,Ltd.,Heze 274700,Shandong,China;State Key Laboratory of Geomechanics and Geotechnical Engineering Safety,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences,Wuhan 430071,Hubei,China)

机构地区：[1]山东新巨龙能源有限公司,山东菏泽274918 [2]山东能源集团有限公司冲击地压防治研究中心,山东济南250014 [3]山东能源集团鲁西矿业有限公司,山东菏泽274700 [4]中国科学院武汉岩土力学研究所岩土力学与工程安全全国重点实验室,湖北武汉430071

出　　处：《隧道与地下工程灾害防治》2025年第1期57-67,共11页Hazard Control in Tunnelling and Underground Engineering

基　　金：山东能源集团有限公司科技计划重大资助项目(LX2022-015)。

摘　　要：回采巷道受强烈采动影响易出现顶板剧烈下沉、大变形和结构破坏等灾害,准确预测其变形趋势对于保障矿井安全生产和实现有效的围岩控制具有重要意义。本研究以新巨龙煤矿回采工作面的围岩变形监测数据为基础,分析了回采巷道采动变形的演化特征,并提出了适用于煤矿回采巷道的经验预测模型。针对传统经验模型难以考虑围岩变形的动态变化及参数不确定性的问题,本研究引入贝叶斯更新算法,构建了动态更新预测模型,通过实时监测数据动态调整模型参数的后验分布,以提高预测精度并降低预测的不确定性。以新巨龙煤矿6305和2305工作面监测数据为例对模型进行验证,结果表明,随着数据累积和更新次数增加,模型参数后验分布趋于稳定,预测精度显著提高,最终预测变形值与实测值高度吻合,预测拟合系数R^(2)达到0.98以上,均方根误差显著降低。此外,以另外一个煤矿某工作面数据进行扩展验证,结果进一步证实了本研究方法在不同地质条件矿井下的泛化能力和适用性。本研究提出的贝叶斯更新预测方法能够有效应对围岩变形的动态变化和参数不确定性,为煤矿回采巷道的围岩稳定性控制提供数据支撑。Mining roadways were found to be susceptible to severe roof subsidence,large deformation,and structural failure under intense mining-induced disturbances.Accurately predicting deformation trends was considered crucial for ensuring mine safety and achieving effective surrounding rock control.Based on deformation monitoring data from mining faces at the Xin Julong Coal Mine,the deformation evolution characteristics of mining roadways were analyzed,and an empirical prediction model suitable for coal mining roadways was proposed.Considering that traditional empirical models were limited in capturing dynamic deformation behavior and parameter uncertainties,a Bayesian updating algorithm was introduced to construct a dynamically updated prediction model.By continuously adjusting the posterior distribution of model parameters with real-time monitoring data,prediction accuracy was improved,and uncertainty was reduced.Model validation was performed using monitoring data from working faces 6305 and 2305 of Xin Julong Coal Mine.It was indicated that,as data accumulated and Bayesian updating iterations proceeded,posterior parameter estimated stabilized,significantly enhancing prediction accuracy.The final predicted deformation values were found to closely match measured values,with determination coefficients(R^(2))exceeding 0.98 and root mean square errors(RMSE)significantly reduced.Additionally,extended validation was conducted using monitoring data from another coal mine's working face,further confirming the generalization capability and applicability of the proposed method under different geological conditions.The Bayesian updating prediction approach proposed in this research was demonstrated to effectively addresses the dynamic variations and parameter uncertainties in surrounding rock deformation,providing reliable data support for surrounding rock stability control in coal mine roadways.

关 键 词：回采巷道 采动变形 预测 经验模型 贝叶斯 动态更新 

分 类 号：U455[建筑科学—桥梁与隧道工程]