Acoustic location echo signal extraction of buried non-metallic pipelines based on EMD and wavelet threshold joint denoising  

基于EMD与小波阈值联合去噪的埋地非金属管道声学定位回波信号提取

作　　者：GE Liang YUAN Xuefeng XIAO Xiaoting LUO Ping WANG Tian 葛亮;袁雪峰;肖小汀;骆平;王甜(西南石油大学机电工程学院,四川成都610500;西南石油大学电气信息学院,四川成都610500;中国移动(成都)产业研究院,四川成都610200)

机构地区：[1]School of Mechanical and Electrical Engineering,Southwest Petroleum University,Chengdu 610500,China [2]School of Electrical Engineering and Information,Southwest Petroleum University,Chengdu 610500,China [3]China Mobile Chengdu Institute of Research and Development,Chengdu 610200,China

出　　处：《Journal of Measurement Science and Instrumentation》2024年第4期417-431,共15页测试科学与仪器（英文版）

基　　金：supported by Nanchong Southwest Petroleum University Science and Technology Strategic Cooperation Project(Nos.23XNSYSX0022,23XNSYSX0026);Provincial Science and Technology Plan Project(No.2023ZHCG0020);Southwest Petroleum University Natural Science“Sailing Plan”Project(No.2023QHZ003)。

摘　　要：In the acoustic detection process of buried non-metallic pipelines,the echo signal is often interfered by a large amount of noise,which makes it extremely difficult to effectively extract useful signals.An denoising algorithm based on empirical mode decomposition(EMD)and wavelet thresholding was proposed.This method fully considered the nonlinear and non-stationary characteristics of the echo signal,making the denoising effect more significant.Its feasibility and effectiveness were verified through numerical simulation.When the input SNR(SNRin)is between-10 dB and 10 dB,the output SNR(SNRout)of the combined denoising algorithm increases by 12.0%-34.1%compared to the wavelet thresholding method and by 19.60%-56.8%compared to the EMD denoising method.Additionally,the RMSE of the combined denoising algorithm decreases by 18.1%-48.0%compared to the wavelet thresholding method and by 22.1%-48.8%compared to the EMD denoising method.These results indicated that this joint denoising algorithm could not only effectively reduce noise interference,but also significantly improve the positioning accuracy of acoustic detection.The research results could provide technical support for denoising the echo signals of buried non-metallic pipelines,which was conducive to improving the acoustic detection and positioning accuracy of underground non-metallic pipelines.在埋地非金属管道的声学探测过程中,回波信号往往受到大量噪声的干扰,这使得有效提取有用信号变得极为困难。本文提出了一种结合经验模态分解(EMD)与小波阈值去噪的算法,该方法充分考虑了回波信号的非线性和非平稳性特征,使得去噪效果更为显著。通过数值仿真,验证了该方法的可行性和有效性。从数值结果上看,当SNRin在-10 dB与10 dB之间时,联合去噪算法的SNRout在小波阈值方法的基础上增加了12.0%-34.1%,在EMD去噪方法的基础上增加了19.6%-56.8%;联合去噪算法的RMSE在小波阈值方法基础上降低了18.1%-48.0%,在EMD去噪方法的基础上降低了22.1%-48.8%。实验结果表明,这种联合的去噪算法不仅能有效减少噪声干扰,还能显著提高声学探测的定位精度。研究成果可为埋地非金属管道的回波信号去噪提供技术支撑,有利于提高声学对地下非金属管道的探测定位精度。

关 键 词：buried non-metallic pipeline acoustic positioning signal processing optimal decomposition scale wavelet basis function EMD combined wavelet threshold algorithm 

分 类 号：TU990.3[建筑科学—市政工程]