基于混沌时间序列分析方法的矿山塌陷区范围预测
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摘要
地下开采所引发的地表变形对矿山工业生产造成了严重危害,因此准确预测矿山塌陷区范围对于矿山安全生产具有重要意义。将混沌时间序列分析方法应用于矿山塌陷区范围预测中,以相空间重构理论为基础,采用小数据量算法计算得到时间序列的关键指标——最大Lyapunov指数,研究了塌陷区在相空间相点距离的演变规律,建立了塌陷区范围边界预测模型,并应用该模型对红岭铅锌矿塌陷区范围进行了分析预测。结果表明,矿山塌陷区范围变化具有混沌特性,时间序列分析方法能够很好地反映塌陷区范围的内在规律,通过计算得出红岭铅锌矿塌陷区范围时间序列的最大Lyapunov指数大于0,该矿山塌陷区范围的预测值与实际值基本吻合,误差大小不超过0.1%,验证了该方法的可靠性,为矿山塌陷区预测提供了一种新思路。
With the advance of mining to deep in domestic underground metal mines,surface subsidence caused by mining has seriously threatened the industrial production of the mine and the ecological environment of the mining area.Therefore,accurately predicting the extent of mine subsidence area is of great significance to mine safety production and ecological protection of mining areas. Because the mine subsidence area is a complex system,it contains various random factors,and the evolution process of the system is often accompanied by the exchange of energy,showing nonlinear characteristics. This paper demonstrates the mine subsidence area as a nonlinear dissipative dynamic system.Combined with the time series analysis method in chaos theory,the range of mine subsidence area is predicted. The specific method studied in this paper is based on the phase space reconstruction theory,and the phase space reconstruction of the deformation time series of the mine subsidencearea is carried out.A small data amount algorithm is used to calculate the key index of the time series-the largest Lyapunov exponent. According to the calculation results,the chaos of the mine subsidence zone system is identified. The evolution law and energy variation law of the phase distance of the collapse zone in the phase space are studied.The prediction model of the boundary of the subsidence zone is established.The model is used to analyze and predict the subsidence zone of the Hongling lead-zinc mine.The results show that the formation and expansion of the subsidence area of the Hongling lead-zinc mine is the result of the comprehensive effects of all kinds of factors such as geological conditions,mining engineering operations and inherent nonlinear characteristics.The maximum Lyapunov exponent and associated dimension of the time series of the subsidence area of the Hongling lead-zinc mine are calculated. It is verified that the variation of the subsidence area of Hongling lead-zinc mine has chaotic characteristics.The time series analysis method can well reflect the inherent law of the range change of the subsidence area. The chaotic characteristics of the different locations in the collapsed zone are different in the phase space due to different geological conditions and mechanical properties.The established mine subsidence area prediction system can better predict the change of the subsidence area.The predicted value is basically consistent with the actual value,and the error size does not exceed 0.1%,which verifies the reliability of the method.It provides a new idea for the prediction of mine subsidence area and guides mine safety production.
With the advance of mining to deep in domestic underground metal mines,surface subsidence caused by mining has seriously threatened the industrial production of the mine and the ecological environment of the mining area.Therefore,accurately predicting the extent of mine subsidence area is of great significance to mine safety production and ecological protection of mining areas. Because the mine subsidence area is a complex system,it contains various random factors,and the evolution process of the system is often accompanied by the exchange of energy,showing nonlinear characteristics. This paper demonstrates the mine subsidence area as a nonlinear dissipative dynamic system.Combined with the time series analysis method in chaos theory,the range of mine subsidence area is predicted. The specific method studied in this paper is based on the phase space reconstruction theory,and the phase space reconstruction of the deformation time series of the mine subsidencearea is carried out.A small data amount algorithm is used to calculate the key index of the time series-the largest Lyapunov exponent. According to the calculation results,the chaos of the mine subsidence zone system is identified. The evolution law and energy variation law of the phase distance of the collapse zone in the phase space are studied.The prediction model of the boundary of the subsidence zone is established.The model is used to analyze and predict the subsidence zone of the Hongling lead-zinc mine.The results show that the formation and expansion of the subsidence area of the Hongling lead-zinc mine is the result of the comprehensive effects of all kinds of factors such as geological conditions,mining engineering operations and inherent nonlinear characteristics.The maximum Lyapunov exponent and associated dimension of the time series of the subsidence area of the Hongling lead-zinc mine are calculated. It is verified that the variation of the subsidence area of Hongling lead-zinc mine has chaotic characteristics.The time series analysis method can well reflect the inherent law of the range change of the subsidence area. The chaotic characteristics of the different locations in the collapsed zone are different in the phase space due to different geological conditions and mechanical properties.The established mine subsidence area prediction system can better predict the change of the subsidence area.The predicted value is basically consistent with the actual value,and the error size does not exceed 0.1%,which verifies the reliability of the method.It provides a new idea for the prediction of mine subsidence area and guides mine safety production.
引文
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[17]Orimi M G,Farid A,Amiri R,et al.Cprecip parameter for checking snow entry for forecasting weekly discharge of the Haraz River flow by artificial neural network[J].Wa‐ter Resources,2015,42(5):607-615.
[18]Tiwari R K,Rao K N N.Phase space structure,attractor di‐mension,lyapunov exponent and nonlinear prediction from earth’s atmospheric angular momentum time series[J].Pure andApplied Geophysics,1999,156(4):719-736.
[19]刘勍,温志贤.MATLAB基础及其应用[M].南京:东南大学出版社,2011.Liu Qing,Wen Zhixian.MATLAB Foundation and Its Ap‐plication[M].Nanjing:Southeast University Press,2011.
[20]杨智勇.基于混沌与人工神经网络的滑坡位移预测研究[D].武汉:长江科学院,2007.Yang Zhiyong.Prediction of Landslide Displacement Based on Chaos and Artificial Neural Network[D]. Wuhan:Changjiang River Scientific Research Institute,2007.
[21]陈小亮.基于混沌非线性时间序列的滑坡预测预报研究[D].南宁:广西大学,2008.Chen Xiaoliang. Study of Landslide Prediction Based on Chaotic Nonlinear Time Series[D].Nanning:Guangxi Uni‐versity,2008.
[2]吕金虎.混沌时间序列分析及其应用[M].武汉:武汉大学出版社,2002.LüJinhu.Chaotic Time Series Analysis and Its Application[M].Wuhan:Wuhan University Press,2002.
[3]刘志祥,龚永超,李夕兵.基于分形理论和BP神经网络的充填料性能研究[J].黄金科学技术,2017,25(2):38-44.Liu Zhixiang,Gong Yongchao,Li Xibing. Study on the backfilling material properties based on fractal theory and BP neural network[J]. Gold Science and Technolo‐gy,2017,25(2):38-44.
[4]叶培.一种新的量子遗传算法在阳山金矿GPS卫星信号去噪处理中的应用探讨[J].黄金科学技术,2015,23(2):83-87.Ye Pei. Application of a new quantum genetic algorithm on denoising processing of GPS satellite signal at the Yangshan gold deposit[J].Gold Science and Technology,2015,23(2):83-87.
[5]余凤鸣,王磊,余虹剑,等.基于遥感技术的武当区域构造分析及找矿意义[J].黄金科学技术,2015,23(1):1-9.Yu Fengming,Wang Lei,Yu Hongjian,et al.Regional tec‐tonic analysis and prospecting significance of Wudang ar‐ea based on remote sensing technology[J]. Gold Science and Technology,2015,23(1):1-9.
[6]刘树勇,朱石坚,俞翔.相空间重构的一种新方法研究[J].系统仿真学报,2007,19(21):4990-4993.Liu Shuyong,Zhu Shijian,Yu Xiang. New method for phase space reconstruction[J].Journal of System Simula‐tion,2007,19(21):4990-4993.
[7]Ruelle D. Locating resonances for Axiom A dynamical systems[J]. Journal of Statistical Physics,1986,44(3/4):281-292.
[8]陆君安,吕金虎.Chen’s混沌吸引子及其特征量[J].控制理论与应用,2002,19(2):308-310.Lu Jun’an,LüJinhu.Chen’s chaotic attractor and its char‐acteristic quantity[J]. Control Theory and Applications,2002,19(2):308-310.
[9]简相超,郑君里.混沌和神经网络相结合预测短波通信频率参数[J].清华大学学报(自然科学版),2001,41(1):16-19.Jian Xiangchao,Zheng Junli. Combination of chaos and neural network to predict the frequency parameters of short wave communication[J]. Journal of Tsinghua Uni‐versity(Natural Science Edition),2001,41(1):16-19.
[10]李夕兵,刘志祥.基于重构相空间充填体变形规律的灰色预测研究[J].安全与环境学报,2004,4(6):54-57.Li Xibing,Liu Zhixiang. Research on grey prediction of deformation laws in backfill based on phase space recon‐struction[J].Journal of Safety and Environment,2004,4(6):54-57.
[11]唐璐,齐欢.混沌和神经网络结合的滑坡预测方法[J].岩石力学与工程学报,2003,22(12):1984-1987.Tang Lu,Qi Huan.Prediction of landslide based on chaos and neural networks[J]. Chinese Journal of Rock Me‐chanics and Engineering,2003,22(12):1984-1987.
[12]何玉彬,李新忠.神经网络控制技术及其应用[M].北京:科学出版社,2000.He Yubin,Li Xinzhong. Neural Network Control Tech‐nology and Its Application[M]. Beijing:Science Press,2000.
[13]Albano A M,Muench J,Schwartz C,et al.Singular-value decomposition and the Grassberger-Procaccia algorithm[J].Physical Review A,1988,38(6):3017-3026.
[14]安鸿志.非线性时间序列分析[M].上海:上海科学技术出版社,1998.An Hongzhi. Analysis of Nonlinear Time Series[M].Shanghai:Shanghai Science and Technology Press,1998.
[15]Takens F. Detecting strange attractors in turbulence[M]//Dynamical Systems and Turbulence,Warwick 1980. New York:Springer-Verlag,1981.
[16]潘文杰.傅里叶分析及其应用[M].北京:北京大学出版社,2000.Pan Wenjie.Fourier Analysis and Its Application[M].Bei‐jing:Peking University Press,2000.
[17]Orimi M G,Farid A,Amiri R,et al.Cprecip parameter for checking snow entry for forecasting weekly discharge of the Haraz River flow by artificial neural network[J].Wa‐ter Resources,2015,42(5):607-615.
[18]Tiwari R K,Rao K N N.Phase space structure,attractor di‐mension,lyapunov exponent and nonlinear prediction from earth’s atmospheric angular momentum time series[J].Pure andApplied Geophysics,1999,156(4):719-736.
[19]刘勍,温志贤.MATLAB基础及其应用[M].南京:东南大学出版社,2011.Liu Qing,Wen Zhixian.MATLAB Foundation and Its Ap‐plication[M].Nanjing:Southeast University Press,2011.
[20]杨智勇.基于混沌与人工神经网络的滑坡位移预测研究[D].武汉:长江科学院,2007.Yang Zhiyong.Prediction of Landslide Displacement Based on Chaos and Artificial Neural Network[D]. Wuhan:Changjiang River Scientific Research Institute,2007.
[21]陈小亮.基于混沌非线性时间序列的滑坡预测预报研究[D].南宁:广西大学,2008.Chen Xiaoliang. Study of Landslide Prediction Based on Chaotic Nonlinear Time Series[D].Nanning:Guangxi Uni‐versity,2008.