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

Abstract

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 (SNR_in) is between -10 dB and 10 dB, the output SNR (SNR_out) 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.

Keywords

buried non-metallic pipeline / acoustic positioning / signal processing / optimal decomposition scale / wavelet basis function / EMD combined wavelet threshold algorithm

References

[1] TAO X R, HE R Y, SHEN G T. Current status and development of in-use buried pipeline inspection technology. China Special Equipment Safety, 2005, 21: 41-45.

[2] LIANG L W. Experimental research on complex underground pipeline detection in urban districts. Xi'an: Northwest University, 2018.

[3] XU C. Research on buried non-metallic pipeline detection technology based on acoustic wave propagation characteristics. Anhui: Anhui University of Science and Technology, 2019.

[4] ZHAO H S. Research on the acoustic wave type underground pipe detector design. Shanghai: Shanghai University of Engineering Science, 2019.

[5] LI H M, FU Y R, ZHENG G J. Research on material suitability of Shenzhen metro water supply and drainage pipelines. Green Environmental Protection Building Materials, 2021(4): 25-26.

[6] LIU H, CHEN B, XIE M H, et al. Design of locator for buried non-metallic pipeline. Computer Measurement and Control, 2021, 29(5): 251-255.

[7] GE L, WANG T, FANG X, et al. Buried pipeline detection device and identification method based on capacitive tomography imaging. China Patent, 113945983A,2022-01-18.

[8] MUGGLETON J M, RUSTIGHI E. Mapping the Underworld: recent developments in vibro-acoustic techniques to locate buried infrastructure. Géotechnique Letters, 2013, 3(3): 137-141.

[9] MUGGLETON J M, BRENNAN M J. The design and instrumentation of an experimental rig to investigate acoustic methods for the detection and location of underground piping systems. Applied Acoustics, 2008, 69(11): 1101-1107.

[10] MUGGLETON J M, BRENNAN M J, GAO Y. Determining the location of buried plastic water pipes from measurements of ground surface vibration. Journal of Applied Geophysics, 2011, 75(1): 54-61.

[11] ZHANG Y H, L S, WANG J Q. On the similarities and differences between the positioning of acoustic positioning instruments and the positioning of detectable warning strips for gas buried PE pipelines. Consumer Electronics Magazine, 2021(9): 53-55.

[12] GAO Y, CUI X W. A device and method for detecting buried pipelines using acoustic reflection. China Patent, 201810083685.9, 2019-07-09.

[13] GE L, HE J, GAO Y, et al. Localisation of buried PE pipes by acoustic imaging method using time-domain superposition. Acta Acustica, 2024, 49(3): 569-576.

[14] GONG Y, JIA R S, LU X M, et al. Suppression of random noise in microseismic signals using empirical modal decomposition and wavelet transform. Journal of China Coal Society, 2018, 43(11): 3247-3256.

[15] GE W Q, ZHANG K, LIU C Z. Signal preprocessing of gas ultrasonic flowmeter based on wavelet threshold algorithm. Acta Metrologica Sinica, 2024, 45(10): 1502-1511.

[16] ARTINA M, FORNASIER M, SOLOMBRINO F. Linearly constrained nonsmooth and nonconvex minimization. SIAM Journal on Optimization, 2013, 23(3): 1904-1937.

[17] HUI T, ZHAO G C, SU J, et al. Improved empirical modal decomposition for noise reduction of atomic clock data. Journal of Henan University of Science & Technology (Natural Science), 2021, 42(5): 45-50.

[18] WANG T. Research on EMD algorithm and its application in signal denoising. Harbin: Harbin Engineering University, 2010.

[19] SHI Z Y, XU W M, ZHOU B, et al. Adaptive noise reduction method based on empirical modal decomposition and wavelet thresholding. Hydrographic Surveying and Charting, 2021, 41(6): 54-57.

[20] FEI H L, LIU M, QU G J, et al. Noise reduction method for blast vibration signal based on ensemble empirical modal decomposition-wavelet thresholding method. Explosion and Shock Waves, 2018, 38(1): 112-148.

[21] GUO F, WANG P. Joint denoising algorithm for MEMS vector hydrophones based on ensemble empirical mode decomposition. Journal of Test and Measurement Technology, 2022, 36(2): 117-121.

[22] SHAO R P, HU W T, LI J. Multi-fault feature extraction and diagnosis of gear transmission system using time-frequency analysis and wavelet threshold de-noising based on EMD. Shock and Vibration, 2013, 20(4): 763-780.

[23] LU Q, PANG L X, HUANG H Q, et al. High-G calibration denoising method for high-G MEMS accelerometer based on EMD and wavelet threshold. Micromachines, 2019, 10(2): 134.

[24] DING H B. Research on comparative analysis of denoising methods for LiDAR signals. Hefei: University of Science and Technology of China, 2022.

[25] XU F H, WANG Z W, LIU J H, et al. Application of de-noising method on electrical imaging logging data based on joint EMD and wavelet threshold. Journal of China University of Petroleum, 2020, 44(3): 56-65.

[26] ZHANG Y Y, YANG Z X, DU X L, et al. A new method for denoising underwater acoustic signals based on EEMD, correlation coefficient, permutation entropy, and wavelet threshold denoising. Journal of Marine Science and Application, 2024, 23(1): 222-237.

[27] LI L Z, PAN C R, REN Y K, et al. Application of EMD and wavelet thresholding combined denoising method on magnetic induction sorter. Nonferrous Metals (Mineral Processing Section), 2023(6): 140-148.

[28] SUN G, ZHANG R, LIU Z, et al. EMD-based noise reduction study of steel cored conveyor belt containing slag signal. Alexandria Engineering Journal, 2024, 98: 56-67.

[29] GE L, HE J, GAO Y, et al. Localisation of buried PE pipes by acoustic imaging method using time-domain superposition. Acta Acustica, 2024, 49(3): 569-576.

[30] GE L, WANG T, XIAO X T, et al. A new acoustic positioning method for buried PE pipeline based on time-domain superposition. Natural Gas Industry, 2022, 42(9): 111-121.

[31] PAPANDREOU B, RUSTIGHI E, BRENNAN M J. On the detection of objects buried at a shallow depth using seismic wave reflections. The Journal of the Acoustical Society of America, 2011, 129(3): 1366-1374.

[32] XU L L, ZHANG J, ZHAO G Y. Design method of nonlinear scanning signal for controllable seismic source based on damped Rayleigh waves. Oil Geophysical Prospecting, 2022, 57(3): 540-549.

[33] YANG R H. Analysis of the influence of inverse time migration source wavelet. Progress in Geophysics, 2021, 36(5): 2102-2108.

[34] ZHANG X, WANG H L. Static boosted wavelet denoising method based on optimal decomposition scale. High Voltage Engineering, 2009, 35(3): 501-508.

[35] XIE H J, LI J R, DONG Y, et al. Influence of wavelet base and threshold parameters on denoising effect of transient electromagnetic signals. Journal of Xi’an University of Science and Technology, 2020, 40(4): 682-690.