基于小波降噪的隧道围岩监测数据分析
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摘要
隧道围岩监测数据中含有大量的随机误差,为了消除或削弱随机误差的干扰,通常对观测数据进行降噪处理。基于小波分析理论,利用小波降噪技术,以某隧道的围岩监测数据为例,选择了db3小波函数和heursure软阈值对围岩接触压力进行降噪处理,并用5-15-1BP神经网络对降噪前后的结果进行了预测比较,训练步数分别为2 448步和450步,未降噪的围岩压力预测的误差总体上要比降噪后的误差大。实际计算结果表明,小波去噪合理有效,能够敏感识别观测噪声和有用信息,适合于隧道围岩监测的数据分析。
There are many random errors in the monitoring data of tunnel surrounding rock.The monitoring data is usually denoised for reducing or eliminating the disturbance of the random errors.Based on the theory of wavelet transform,as an example,the monitoring data of a tunnel surrounding rock is processed by a technique of wavelet denoising with db3 wavelet function and heursure soft threshold.The result of pressure prediction is given by using 5-15-1 BP neural network for the original data and the de-noised data,and the training steps are 2 448 and 450 respectively.The error of the pressure prediction for the original data is larger than that for the de-noised data.The results show that the method of wavelet denoising is efficient and reliable,is sensitive to distinguish noise and useful information,is particularly suitable to analyze monitoring data of surrounding rock.
There are many random errors in the monitoring data of tunnel surrounding rock.The monitoring data is usually denoised for reducing or eliminating the disturbance of the random errors.Based on the theory of wavelet transform,as an example,the monitoring data of a tunnel surrounding rock is processed by a technique of wavelet denoising with db3 wavelet function and heursure soft threshold.The result of pressure prediction is given by using 5-15-1 BP neural network for the original data and the de-noised data,and the training steps are 2 448 and 450 respectively.The error of the pressure prediction for the original data is larger than that for the de-noised data.The results show that the method of wavelet denoising is efficient and reliable,is sensitive to distinguish noise and useful information,is particularly suitable to analyze monitoring data of surrounding rock.
引文
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[9]李英,张淑贞,许康生.小波降噪方法在地震信号处理中的应用[J].西北地震学报,2006,28(2):159-162.LI Ying,ZHANG Shu-zhen,XU Kang-sheng.Appli-cation of wavelet transfer in seismic signal denoise[J].Northwestern Seismological Journal,2006,28(2):159-162.
[10]田胜利,周拥军,葛修润,等.基于小波分解的建筑物变形监测数据处理[J].岩石力学与工程学报,2004,23(15):2639-2642.TIAN Sheng-li,ZHOU Yong-jun,GE Xiu-run,etal.Processing of monitoring data of building defor-mation based on wavelet transform[J].ChineseJournal of Rock Mechanics and Engineering,2004,23(15):2639-2642.
[11]田胜利,徐东强,葛修润.大坝水平位移监测数据的小波变换去噪处理[J].水电自动化与大坝监测,2004,28(1):49-53.TIAN Sheng-li,XU Dong-qiang,GE Xiu-run.Wave-let transform analysis and denoising of monitoringdata of horizontal dam displacement[J].HydropowerAutomation and Dam Monitoring,2004,28(1):19-53.
[12]董永生,羿旭明.基于四种改进阈值的小波去噪方法[J].数学杂志,2006,26(5):473-477.DONG Yong-sheng,YI Xu-ming.Wavelet denosingbased on four improved functions for threshold esti-mation[J].Journal of Mathematics,2006,26(5):473-477.
[13]付燕.改进的小波域阈值去噪方法[J]西安科技大学学报,2004,24(4):467-469.FU Yan.A modified denoising method by thresholdfilter in wavelet domain[J].Journal of Xi’an Univer-sity of Science and Technology,2004,24(4):467-469.
[2]Karmen F B,Borut P.Displacement analysis of tunnelsupport in soft rock around a shallow highway tunnelat Golovec[J].Engineering Geology,2004,75:89-106.
[3]袁勇,王胜辉,杜国平,等.双连拱隧道支护体系现场监测试验研究[J].岩石力学与工程学报,2005,24(3):480-484.YUAN Yong,WANG Sheng-hui,DU Guo-ping,et al.In-situ testing study on lining system of double-archedtunnel[J].Chinese Journal of Rock Mechanics and En-gineering,2005,24(3):480-484.
[4]崔天麟.超浅埋暗挖隧道初期支护结构内力监测及稳定性分析[J].现代隧道技术,2001,38(2):29-34.CUI Tian-lin.Structural internal force monitoring andstability analysis on primary lining bored tunnel with ashallow depth[J].Modern Tunneling Technology,2001,38(2):29-34.
[5]刘庆仁.三车道公路隧道监测分析研究[J].北京建筑工程学院学报,2002,18(3):34-39.LIU Qing-ren.Measurement and analysis of 3-lanecarriageway tunnel[J].Journal of Beijing Institute ofCivil Engineering and Architecture,2002,18(3):34-39.
[6]李建平,唐远炎.小波分析方法的应用[M].重庆:重庆大学出版社,1999.LI Jian-ping,TANG Yuan-yan.Application of waveletanalysis[M].Chongqing:Chongqing UniversityPress,1999.
[7]郑治真,沈萍,杨选辉,等.小波变换及其MATLAB工具的应用[M].北京:地震出版社,2002.ZHENG Zhi-zhen,SHEN Ping,YANG Xuan-hui,etal.Application of wavelet analysis and the MATLABtool box[M].Beijing:Seismological PublishingHouse,2002.
[8]聂文滨,阙沛文.桶基平台贯沉深度测量中的小波去噪方法[J].测控技术,2002,21(7):51-59.NIE Wen-bin,QUE Pei-wen.Denoise using wavelettransform during the measurements of the penetrabledepth of the jacket with bucket foundation[J].Me-asurement&Control Technology,2002,21(7):51-59.
[9]李英,张淑贞,许康生.小波降噪方法在地震信号处理中的应用[J].西北地震学报,2006,28(2):159-162.LI Ying,ZHANG Shu-zhen,XU Kang-sheng.Appli-cation of wavelet transfer in seismic signal denoise[J].Northwestern Seismological Journal,2006,28(2):159-162.
[10]田胜利,周拥军,葛修润,等.基于小波分解的建筑物变形监测数据处理[J].岩石力学与工程学报,2004,23(15):2639-2642.TIAN Sheng-li,ZHOU Yong-jun,GE Xiu-run,etal.Processing of monitoring data of building defor-mation based on wavelet transform[J].ChineseJournal of Rock Mechanics and Engineering,2004,23(15):2639-2642.
[11]田胜利,徐东强,葛修润.大坝水平位移监测数据的小波变换去噪处理[J].水电自动化与大坝监测,2004,28(1):49-53.TIAN Sheng-li,XU Dong-qiang,GE Xiu-run.Wave-let transform analysis and denoising of monitoringdata of horizontal dam displacement[J].HydropowerAutomation and Dam Monitoring,2004,28(1):19-53.
[12]董永生,羿旭明.基于四种改进阈值的小波去噪方法[J].数学杂志,2006,26(5):473-477.DONG Yong-sheng,YI Xu-ming.Wavelet denosingbased on four improved functions for threshold esti-mation[J].Journal of Mathematics,2006,26(5):473-477.
[13]付燕.改进的小波域阈值去噪方法[J]西安科技大学学报,2004,24(4):467-469.FU Yan.A modified denoising method by thresholdfilter in wavelet domain[J].Journal of Xi’an Univer-sity of Science and Technology,2004,24(4):467-469.
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