基于小波变换的坝体GPS形变监测分析
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摘要
为了对水库坝体在蓄水期进行更好的GNSS形变监测,探讨其真实的形变趋势信息,需对原始观测数据进行降噪处理。文中借助db4小波变换方法与C语言编程工具,实现了对GPS原始观测数据的小波分解与降噪处理,并与传统经典HHT变换法进行了对比。结果表明:(1)db4小波更适用于水库坝体的形变监测;(2)小波分解至第3阶可提取其高频干扰信息,分解至第4阶可判断其低频部分整体走势。经过降噪后的趋势低频曲线会更加平滑,趋势上更有规律可循,并有一定的趋势预测性,且结果可靠。
In order to better monitor the GNSS deformation of the reservoir dam during the impoundment period and to explore its true deformation trend information,it is necessary to reduce the noise of the original observation data.With the help of the basic theory of db4 wavelet transform and C language programming tool,the wavelet decomposition and noise reduction of GPS raw observation data are realized.The results show that:(1) db4 wavelet basis is more suitable for deformation monitoring of reservoir dams;(2) The high frequency interference information can be extracted with the wavelet decomposition to the third order,and the whole trend of the low frequency part can be judged by the decomposition to the fourth order.The trend low frequency curve after the noise reduction will be more smooth,more regular,and there is a certain trend predictability,and the results are reliable.
In order to better monitor the GNSS deformation of the reservoir dam during the impoundment period and to explore its true deformation trend information,it is necessary to reduce the noise of the original observation data.With the help of the basic theory of db4 wavelet transform and C language programming tool,the wavelet decomposition and noise reduction of GPS raw observation data are realized.The results show that:(1) db4 wavelet basis is more suitable for deformation monitoring of reservoir dams;(2) The high frequency interference information can be extracted with the wavelet decomposition to the third order,and the whole trend of the low frequency part can be judged by the decomposition to the fourth order.The trend low frequency curve after the noise reduction will be more smooth,more regular,and there is a certain trend predictability,and the results are reliable.
引文
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[13] 罗德河,郑东健.大坝变形的小波分析与ARMA预测模型[J].水利水运工程学报,2016(3):70-75.
[2] 黄丁发,陈永奇,丁晓利,等.GPS高层建筑物常荷载振动测试的小波分析[J].振动与冲击,2001,20(1):12-15.
[3] 黄凯,陈渠森,鞠博晓.GNSS自动化监测系统的大坝变形预测方法研究[J].测绘通报,2018(1):147-150.
[4] 张永光,王兰锋,吕开云.小浪底水利枢纽大坝变形的灰色预测模型[J].测绘科学,2006,31(6):80-81.
[5] 王新洲,范千,许承权,等.基于小波变换和支持向量机的大坝变形预测[J].武汉大学学报(信息科学版),2008,33(5):469-471.
[6] 范千,王新洲,许承权.大坝变形预测的支持向量机模型[J].测绘工程,2007,16(6):1-4.
[7] 王波,周仪邦,梁海波,等.基于小波分析的GPS大坝变形监测数据处理[J].青海大学学报(自然科学版),2013,31(1):48-53.
[8] 程正兴,杨守志,冯晓霞.小波分析的理论、算法、进展和应用[M].北京:国防工业出版社,2007:10-15.
[9] RAN Q W.Theory and application of wavelet transform and fractional fourier transforms[M].Harbin:Publishing House of Harbin Institute of Technology,2001:36-37.
[10] 胡昌华,张军波,夏军,等.基于MATLAB的系统分析与设计——小波分析[M].西安:西安电子科技大学出版社,2004:49-64.
[11] 方菲.希尔伯特—黄变换及其在混凝土无损检测中的应用[J].工程地球物理学报,2018,15(6):733-741.
[12] 杨可明,汪国平,付萍杰,等.HHT时频分析土壤光谱的重金属铜离子污染信息提取模型[J].光谱学与光谱分析,2018,38(2):564-569.
[13] 罗德河,郑东健.大坝变形的小波分析与ARMA预测模型[J].水利水运工程学报,2016(3):70-75.