Analysis of linkage relationship between longwall face support resistance and water inflow time series data
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摘要: 长壁工作面矿压与涌水量间的数据联动关系是实现基于矿山多源信息融合进行开采参数动态决策和灾害智能预警的基础内容之一。基于工作面支架阻力与涌水量现场数据，建立了时间序列GARCH模型，从静态和动态角度挖掘出矿压与涌水量之间的联动性和数据结构特征，提出了基于Almon分布模型的支架工作阻力与涌水量的滞后联动时间计算方法。研究结果表明：工作面回采期间工作面涌水量峰值和支架阻力之间具有显著的时变关联性；工作面支架阻力变动率和涌水量变动率均呈现非正态分布且具有波动集聚性和自相关性；在平稳时间序列ADF单位根检验、格兰杰因果检验和自相关检验的基础上，构建了支架阻力与涌水量数据的ARMA-GARCH-t-Copula边缘分布组合模型，确定了采用Clayton-Copula函数来描述两者数据的非对称相关结构特征和数据联动程度，可以捕捉到工作面支架阻力处于下降趋势或者剧烈波动时数据联动性更强，即顶板发生来压时，工作面涌水量也将出现峰值拐点；从动态角度建立了DCC-GARCH分析模型，案例工作面支架工作阻力和涌水量动态系数在-0.264 9~0.264 9波动，总体波动具有正联动性和长期协整关系；通过PDL估计模型对Almon分布滞后模型的参数进行了分析，提出了工作面支架阻力和涌水量数据滞后期的计算方法。研究成果可为长壁开采涌水量动态预测与智能预警提供基础数据分析模型。Abstract: The data linkage relationship between mine pressure and water yield in longwall face is one of the basic contents to realize the dynamic decision-making of mining technical parameters and the intelligent early-warning of mine disasters on the basis of multi-source information fusion. Based on the field data of support resistance and water yield in longwall face, a time series GARCH model was established, and the linkage relationship and data structure characteristics between mine pressure and water inflow were mined from both static and dynamic points of view. A calculation method of lag linkage time between mine pressure and water yield based on the Almon distributed model was developed. The results revealed that the relation between peak water yield and support resistance is highly time-dependent during longwall mining operation. The variation rates of support resistance and water yield in working face presented a non-normal distribution, volatility clustering and self-correlation. An AR-MA-GARCH-t-Copula edge distribution combined model on the field data of support resistance and water yield was established from a static point of view, based on the ADF unit root test for time series stationarity, the Granger causality test, and autocorrelation test. The Clayton-Copula function was employed to describe the asymmetric data structure and data linkage, which can capture that the data linkage is stronger when the support resistance presents a downward trend or violent fluctuation, that is, the peak water yield occurred along with the roof weighting. A DCC-GARCH model, which was established from a dynamic point of view, shows that the dynamic coefficients of the data of support resistance and water yield fluctuates between -0.264 9 and 0.264 9. There is a positive linkage and a long-term cointegration relationship between the fluctuations of support resistance and water yield. A method for calculating the time lag between support resistance and water yield was developed based on the parameter determination of the Almon distributed model through PDL estimation model. The results can provide a basic data analysis model for the dynamic prediction and intelligent early warning of water yield in longwall mining.
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Keywords:
- longwall mining /
- support resistance /
- water yield /
- data linkage /
- edge distribution combined model /
- lag model
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[1] 钱鸣高,许家林. 煤炭开采与岩层运动[J]. 煤炭学报,2019, 44(4):973-84. QIAN Minggao,XU Jialin. Behaviors of strata movement in coal mining [J]. Journal of China Coal Society,2019,44(4):973-84.
[2] PENG S. Advances in coal mine ground control [M]. Sowstion Combridge:Wood Head Publishing,2017.
[3] 张东升,范钢伟,张世忠,等. 保水开采覆岩等效阻水厚度的内涵、算法与应用[J]. 煤炭学报,2022,47(1):128-136. ZHANG Dongsheng,FAN Gangwei,ZHANG Shizhong,et al. Equivalent water-resisting overburden thickness for wter-conservation mining:Conception,method and application[J]. Journal of China Coal Society,2022,47(1):128-36. [4] 范钢伟,张东升,马立强. 神东矿区浅埋煤层开采覆岩移动与裂隙分布特征[J]. 中国矿业大学学报,2011,40(2):196-201. FAN Gangwei,ZHANG Dongsheng,MA Liqiang. Overburden movement and fracture distribution induced by longwall mining of the shallow coal seam in the Shendong coalfield [J]. Journal of China University of Mining & Technology,2011,40(2):196-201.
[5] 彭苏萍,张博,王佟,等. 煤炭资源与水资源[M]. 北京:科学出版社,2014. [6] 王家臣,王蕾,郭尧. 基于顶板与煤壁控制的支架阻力的确定[J]. 煤炭学报,2014,39(8):1619-1624. WANG Jiachen,WANG Lei,GUO Yao. Determining the support capacity based on roof and coal wall control [J]. Journal of China Coal Society,2014,39(8):1619-1624.
[7] 李化敏,蒋东杰,李东印. 特厚煤层大采高综放工作面矿压及顶板破断特征[J]. 煤炭学报,2014,39(10):1956-1960. LI Huamin,JIANG Dongjie,LI Dongyin. Analysis of ground pressure and roof movement in fully-mechanized top coal caving with large mining height in ultra-thick seam [J]. Journal of China Coal Society,2014,39(10):1956-1960.
[8] 程敬义,万志军,PENG SYD,等. 基于海量矿压监测数据的采场支架与顶板状态智能感知技术[J]. 煤炭学报,2020,45(6):2090-2103. CHENG Jingyi,WAN Zhijun,PENG Syd,et al. Technology of intelligent sensing of longwall shield supports status and roof strata based on massive shield pressure monitoring data [J]. Journal of China Coal Society,2020,45(6):2090-2103.
[9] 闫少宏,尹希文,许红杰,等. 大采高综采顶板短悬臂梁-铰接岩梁结构与支架工作阻力的确定[J]. 煤炭学报,2011,36(11):1816-1820. YAN Shaohong, YIN Xiwen, XU Hongjie, et al. Roof structure of short cantilever-articulated rock beam and calculation of support resistance in full-mechanized face with large mining height [J]. Journal of China Coal Society,2011,36(11):1816-1820.
[10] ZHANG Q,ZHANG J X,KANG T,et al. Mining pressure monitoring and analysis in fully mechanized backfilling coal mining faceA case study in Zhai Zhen Coal Mine [J]. J Cent South Univ, 2015,22(5):1965-1972.
[11] YUN D F,LIU Z,CHENG W D,et al. Monitoring strata behavior due to multi-slicing top coal caving longwall mining in steeply dipping extra thick coal seam [J]. International Journal of Mining Science and Technology,2017,27(1):179-184.
[12] 孔令海,姜福兴,王存文. 特厚煤层综放采场支架合理工作阻力研究[J]. 岩石力学与工程学报,2010,29(11):2312-2318. KONG Linghai, JIANG Fuxing, WANG Cunwen. Study of reasonable working resistance of support in fully-mechanized sublevel caving face in extra-thick coal seam [J]. Chinese Journal of Rock Mechanics and Engineering,2010,29(11):2312-2318.
[13] 黄欢. 矿井涌水量预测方法及发展趋势[J]. 煤炭科学技术, 2016,44(S1):127-130. HUANG Huan. Prediction method of mine inflow and its development [J]. Coal Science and Technology,2016,44(S1):127-130.
[14] 虎维岳,闫丽. 对矿井涌水量预测问题的分析与思考[J]. 煤炭科学技术,2016,44(1):13-18,38 . HU Weiyue, YAN Li. Analysis and consideration on prediction problems of mine water inflow volume [J]. Coal Science and Technology,2016,44(1):13-18,38
[15] 黄存捍,冯涛,王卫军,等. 基于分形和支持向量机矿井涌水量的预测[J]. 煤炭学报,2010,35(5):806-810. HUANG Cunhan,FENG Tao,WANG Weijun,et al. Mine water inrush prediction based on fractal and support vector machines [J]. Journal of China Coal Society,2010,35(5):806-810.
[16] 凌成鹏,孙亚军,杨兰和,等. 基于BP神经网络的孔隙充水矿井涌水量预测[J]. 水文地质工程地质,2007,34(5):55-58. LING Chengpeng,SUN Yajun,YANG Lanhe,et al. Prediction of inrush water of mine with pore water yield based on BP artificial neural network [J]. Hydrogeology & Engineering Geology, 2007,34(5):55-58.
[17] 钱家忠,朱学愚,吴剑锋,等. 矿井涌水量的灰色马尔可夫预报模型[J]. 煤炭学报,2000,25(1):73-77. QIAN Jiazhong, ZHU Xueyu, WU Jianfeng, et al. Grey Markov Model for predicting mine discharge [J]. Journal of China Coal Society,2000,25(1):73-77.
[18] 徐龙,葛晓光. 水文地质比拟法的系统处理模型[J]. 煤炭学报,1995,20(4):346-350. XU Long, GE Xiaoguang. A system analyzing model of analogic hydrogeology[J].Journal of China Coal Society,1995,20(4):346-350.
[19] CHEN T,YIN H Y,ZHAI Y T,et al. Numerical simulation of mine water inflow with an embedded discrete fracture model:application to the 16112 working face in the Binhu coal mine, China [J]. Mine Water and the Environment, 2022, 41(1):156-167.
[20] HU W Y,ZHAO C H. Evolution of water hazard control technology in China's coal mines [J]. Mine Water and the Environment, 2021,40(2):334-344.
[21] WANG W H,LI W P. Research on trend analysis method of multiseries economic data based on correlation enhancement of deep learning [J]. Neural Comput Appl,2021,33(10):4815-4831.
[22] XIE H T,LI D F,XIONG L H. Exploring the regional variance using ARMA-GARCH models [J]. Water Resour Manag,2016, 30(10):3507-3518.
[23] FRANCQ C, ZAKOIAN J M. Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes [J]. Bernoulli, 2004,10(4):605-637.
[24] 付凌晖. 我国产业结构高级化与经济增长关系的实证研究[J]. 统计研究,2010,27(8):79-81. FU Linghui. An empirical research on industry structure and economic growth [J]. Statistical Research, 2010,27(8):79-81.
[25] 韦艳华,张世英. 金融市场的相关性分析——Copula-GARCH模型及其应用[J]. 系统工程,2004,22(4):7-12. WEI Yanhua, ZHANG Shiying. Dependence analysis of finance markets:Copula-GARCH model and its application [J]. Systems Engineering,2004,22(4):7-12.
[26] 徐有俊,王小霞,贾金金. 中国股市与国际股市联动性分析——基于DCC-GARCH模型研究[J]. 经济经纬,2010, 27(5):124-128. XU Youjun,WANG Xiaoxia,JIA Jinjin. An analysis of the linkage between chinese and international stock markets——A Research Based on DCC-GARCH Model [J]. Economic Survey, 2010, 27(5):124-128.
[27] ENGLE R. Dynamic conditional correlation:A simple class of multivariate generalized autoregressive conditional heteroskedasticity models [J]. J Bus Econ Stat,2002,20(3):339-350.
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