露天矿山边坡变形监测的改进型IQR粗差探测方法
|
摘要
在露天矿山边坡变形监测工作中,由于多种因素的影响,监测数据序列中往往不可避免地存在粗差。在变形监测环境下,由于监测数据序列分布较复杂,导致常规粗差探测方法在监测数据序列处理方面效果不理想。为此,将小波分析、3σ法与IQR法相结合,提出了一种改进型IQR粗差探测法,并分别结合仿真模拟数据和金堆城露天矿边坡GPS变形监测数据对改进型IQR法、IQR法以及3σ法的粗差探测效果进行了对比分析。结果表明:改进型IQR法的粗差探测效果优于其余2类方法,对于提高露天矿山边坡变形监测精度有一定的参考价值。
During the slope deformation monitoring of open-pit mine,due to the existence of influential factors,the the monitoring data sequence will inevitably appear gross error.At the same time,the monitoring data sequence in deformation monitoring environment has the characteristics of complex distribution,which leads to poor performance of the conventional gross error detection method in monitoring data sequence processing.Aiming at this problem,based on wavelet analysis and3σ method,the classical IQR method is improved,a new improved IQR method is proposed.Based on the simulaiton data and the GPS deformaiton monitoring data of Jinduicheng open-pit mine,the gross error detection effects of the improved IQR method,IQR method and 3σ method is ahalyzed.The study results show that the performance of the improved IQR method is superior to the other,which is help for impoving the slope deformation monitoring accuracy of open-pit mine.
During the slope deformation monitoring of open-pit mine,due to the existence of influential factors,the the monitoring data sequence will inevitably appear gross error.At the same time,the monitoring data sequence in deformation monitoring environment has the characteristics of complex distribution,which leads to poor performance of the conventional gross error detection method in monitoring data sequence processing.Aiming at this problem,based on wavelet analysis and3σ method,the classical IQR method is improved,a new improved IQR method is proposed.Based on the simulaiton data and the GPS deformaiton monitoring data of Jinduicheng open-pit mine,the gross error detection effects of the improved IQR method,IQR method and 3σ method is ahalyzed.The study results show that the performance of the improved IQR method is superior to the other,which is help for impoving the slope deformation monitoring accuracy of open-pit mine.
引文
[1]苗胜军,蔡美峰,张丽英,等.水厂铁矿边坡变形GPS监测及数据处理[J].金属矿山,2005(4):11-13.Miao Shengjun,Cai Meifeng,Zhang Liying,et al.GPS monitoring and data processing of slope deformation in Shuichang Iron Mine[J].Metal Mine,2005(4):11-13.
[2]杨玉访.GPS在矿山控制测量中的应用[J].金属矿山,2009(6):117-119.Yang Yufang.Application of GPS in mine control survey[J].Metal Mine,2009(6):117-119.
[3]毛亚纯,王恩德,修春华.剔除变形监测粗差数据的新方法--数据跳跃法[J].东北大学学报:自然科学版,2011,32(7):1020-1023.Mao Yachun,Wang Ende,Xiu Chunhua.Data uump method:a new approach to elimination the deformation monitoring data with gross errors[J].Journal of Northeastern University:Natural Science Edition,2011,32(7):1020-1023.
[4]熊艳艳,吴先球.粗大误差四种判别准则的比较和应用[J].大学物理实验,2010,23(1):66-68.Xiong Yanyan,Wu Xianqiu.The generalizing applications of four judging criterions for gross errors[J].Physical Experiment of College,2010,23(1):66-68.
[5]张恒,程鹏飞.基于GPS高程时间序列粗差的抗差探测与插补研究[J].大地测量与地球动力学,2011,31(4):71-75.Zhang Hengjing,Cheng Pengfei.Study on robust detection and interpolation from gross errors of GPS height time series[J].Journal of Geodesy&Geodynamics,2011,31(4):71-75.
[6]张璇,程敏熙,肖凤平.利用Origin对数据异常值的剔除方法进行比较[J].实验科学与技术,2012,10(1):74-76.Zhang Xuan,Cheng Minxi,Xiao Fengping.Origin used in comparison the method of eliminating the excrescent data[J].Experiment Science and Technology,2012,10(1):74-76.
[7]Zhang Z,Chen J.Correntropy based data reconciliation and gross error detection and identification for nonlinear dynamic processes[J].Computers&Chemical Engineering,2015,75:120-134.
[8]李敏,易国庆.四分位数稳健统计方法与传统统计方法在实验室能力验证结果评价中的比较分析[J].中国卫生统计,2010,27(4):431-433.Li Min,Yi Guoqing.Comparative analysis of four quantile robust statistical method and traditional statistical method in laboratory proficiency testing results evaluation[J].Chinese Journal of Health Statistics,2010,27(4):431-433.
[9]史玉峰,孙保琪.时间序列分析及其在变形数据分析中的应用[J].金属矿山,2004(8):13-15.Shi Yufeng,Sun Baoqi.Time series analysis and its application in analysis of deformation data[J].Metal Mine,2004(8):13-15.
[10]蒋晨,张书毕,文小勇.基于中位数回归分析的矿区变形监测数据处理[J].金属矿山,2016(5):192-195.Jiang Chen,Zhang Shubi,Wen Xiaoyong.Deformation monitoring data processing method of mining area based on median regression analysis method[J].Metal Mine,2016(5):192-195.
[11]Bretas N G,Piereti S A,Bretas A S,et al.A geometrical view for multiple gross errors detection,identificationand correction in power system state estimation[J].IEEE Transactions on Power Systems,2013,28(3):2128-2135.
[12]李喜盼,扈静,李海刚.基于小波分析的GPS动态变形数据粗差识别模型研究[J].测绘通报,2011(4):7-9.Li Xipan,Hu Jing,Li Haigang.Research on model of gross error identification for GPS dynamic deformation data based on wavelet analysis[J].Bulletin of Surveying&Mapping,2011(4):7-9.
[13]邢小茹,马小爽,田文,等.实验室间比对能力验证中的两种稳健统计技术探讨[J].中国环境监测,2011,27(4):4-8.Xing Xiaoru,Ma Xiaoshuang,Tian Wen,et al.Two robust statistic techniques in proficiency testing by inter laboratory comparisons[J].Environmental Monitoring in China,2011,27(4):4-8.
[14]蒋晨,于瑞鹏,鲍国,等.副井振动信号处理的提升小波变换方法[J].金属矿山,2015(4):233-237.Jiang Chen,Yu Ruipeng,Bao Guo,et al.Auxiliary shaft vibration signals processing based on lifting wavelet transform[J].Metal Mine,2015(4):233-237.
[15]李宗春,邓勇,张冠宇,等.变形测量异常数据处理中小波变换最佳级数的确定[J].武汉大学学报:信息科学版,2011,36(3):285-288.Li Zongchun,Deng Yong,Zhang Guanyu,et al.Determination of best grading of wavelet transform in deformation measurement data filtering[J].Geomatics&Information Science of Wuhan University,2011,36(3):285-288.
[16]任超,沙磊,卢献健.一种改进小波阀值算法的变形监测数据滤波方法[J].武汉大学学报:信息科学版,2012,37(7):873-875.Ren Chao,Sha Lei,Lu Xianjian.An adaptive wavelet threshold denosing both in low and high frequency domains[J].Geomatics&Information Science of Wuhan University,2012,37(7):873-875.
[17]陈晓鹏,张强勇,刘大文,等.边坡变形统计回归分析模型及应用[J].岩石力学与工程学报,2008,27(2):3673-3679.Chen Xiaopeng,Zhang Qiangyong,Liu Dawen,et al.Deformation statistical regression analysis model of slope and its application[J].Chinese Journal of Rock Mechanics and Engineering,2008,27(2):3673-3679.
[2]杨玉访.GPS在矿山控制测量中的应用[J].金属矿山,2009(6):117-119.Yang Yufang.Application of GPS in mine control survey[J].Metal Mine,2009(6):117-119.
[3]毛亚纯,王恩德,修春华.剔除变形监测粗差数据的新方法--数据跳跃法[J].东北大学学报:自然科学版,2011,32(7):1020-1023.Mao Yachun,Wang Ende,Xiu Chunhua.Data uump method:a new approach to elimination the deformation monitoring data with gross errors[J].Journal of Northeastern University:Natural Science Edition,2011,32(7):1020-1023.
[4]熊艳艳,吴先球.粗大误差四种判别准则的比较和应用[J].大学物理实验,2010,23(1):66-68.Xiong Yanyan,Wu Xianqiu.The generalizing applications of four judging criterions for gross errors[J].Physical Experiment of College,2010,23(1):66-68.
[5]张恒,程鹏飞.基于GPS高程时间序列粗差的抗差探测与插补研究[J].大地测量与地球动力学,2011,31(4):71-75.Zhang Hengjing,Cheng Pengfei.Study on robust detection and interpolation from gross errors of GPS height time series[J].Journal of Geodesy&Geodynamics,2011,31(4):71-75.
[6]张璇,程敏熙,肖凤平.利用Origin对数据异常值的剔除方法进行比较[J].实验科学与技术,2012,10(1):74-76.Zhang Xuan,Cheng Minxi,Xiao Fengping.Origin used in comparison the method of eliminating the excrescent data[J].Experiment Science and Technology,2012,10(1):74-76.
[7]Zhang Z,Chen J.Correntropy based data reconciliation and gross error detection and identification for nonlinear dynamic processes[J].Computers&Chemical Engineering,2015,75:120-134.
[8]李敏,易国庆.四分位数稳健统计方法与传统统计方法在实验室能力验证结果评价中的比较分析[J].中国卫生统计,2010,27(4):431-433.Li Min,Yi Guoqing.Comparative analysis of four quantile robust statistical method and traditional statistical method in laboratory proficiency testing results evaluation[J].Chinese Journal of Health Statistics,2010,27(4):431-433.
[9]史玉峰,孙保琪.时间序列分析及其在变形数据分析中的应用[J].金属矿山,2004(8):13-15.Shi Yufeng,Sun Baoqi.Time series analysis and its application in analysis of deformation data[J].Metal Mine,2004(8):13-15.
[10]蒋晨,张书毕,文小勇.基于中位数回归分析的矿区变形监测数据处理[J].金属矿山,2016(5):192-195.Jiang Chen,Zhang Shubi,Wen Xiaoyong.Deformation monitoring data processing method of mining area based on median regression analysis method[J].Metal Mine,2016(5):192-195.
[11]Bretas N G,Piereti S A,Bretas A S,et al.A geometrical view for multiple gross errors detection,identificationand correction in power system state estimation[J].IEEE Transactions on Power Systems,2013,28(3):2128-2135.
[12]李喜盼,扈静,李海刚.基于小波分析的GPS动态变形数据粗差识别模型研究[J].测绘通报,2011(4):7-9.Li Xipan,Hu Jing,Li Haigang.Research on model of gross error identification for GPS dynamic deformation data based on wavelet analysis[J].Bulletin of Surveying&Mapping,2011(4):7-9.
[13]邢小茹,马小爽,田文,等.实验室间比对能力验证中的两种稳健统计技术探讨[J].中国环境监测,2011,27(4):4-8.Xing Xiaoru,Ma Xiaoshuang,Tian Wen,et al.Two robust statistic techniques in proficiency testing by inter laboratory comparisons[J].Environmental Monitoring in China,2011,27(4):4-8.
[14]蒋晨,于瑞鹏,鲍国,等.副井振动信号处理的提升小波变换方法[J].金属矿山,2015(4):233-237.Jiang Chen,Yu Ruipeng,Bao Guo,et al.Auxiliary shaft vibration signals processing based on lifting wavelet transform[J].Metal Mine,2015(4):233-237.
[15]李宗春,邓勇,张冠宇,等.变形测量异常数据处理中小波变换最佳级数的确定[J].武汉大学学报:信息科学版,2011,36(3):285-288.Li Zongchun,Deng Yong,Zhang Guanyu,et al.Determination of best grading of wavelet transform in deformation measurement data filtering[J].Geomatics&Information Science of Wuhan University,2011,36(3):285-288.
[16]任超,沙磊,卢献健.一种改进小波阀值算法的变形监测数据滤波方法[J].武汉大学学报:信息科学版,2012,37(7):873-875.Ren Chao,Sha Lei,Lu Xianjian.An adaptive wavelet threshold denosing both in low and high frequency domains[J].Geomatics&Information Science of Wuhan University,2012,37(7):873-875.
[17]陈晓鹏,张强勇,刘大文,等.边坡变形统计回归分析模型及应用[J].岩石力学与工程学报,2008,27(2):3673-3679.Chen Xiaopeng,Zhang Qiangyong,Liu Dawen,et al.Deformation statistical regression analysis model of slope and its application[J].Chinese Journal of Rock Mechanics and Engineering,2008,27(2):3673-3679.