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

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 (<italic>SNR</italic><sub>in</sub>) is between <inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mo>-</mo></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M001.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M001c.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic></alternatives></inline-formula>10 dB and 10 dB, the output SNR (<italic>SNR</italic><sub>out</sub>) of the combined denoising algorithm increases by 12.0%<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mo>-</mo></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M002.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M002c.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic></alternatives></inline-formula>34.1% compared to the wavelet thresholding method and by 19.60%<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mo>-</mo></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M002.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M002c.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic></alternatives></inline-formula>56.8% compared to the EMD denoising method. Additionally, the RMSE of the combined denoising algorithm decreases by 18.1%<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mo>-</mo></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M004.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M004c.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic></alternatives></inline-formula>48.0% compared to the wavelet thresholding method and by 22.1%<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mo>-</mo></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M002.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M002c.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic></alternatives></inline-formula>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.

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SHILAP Revista de lepidopterología
Published
2024-12-01
Primary Topic
Water Systems and Optimization
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article
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article

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

Ge Liang, Wang Tian, LUO Ping, XIAO Xiaoting et al.
SHILAP Revista de lepidopterología
Water Systems and Optimization
article

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

Ge Liang, Wang Tian, LUO Ping, XIAO Xiaoting, YUAN Xuefeng
article en

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 (<italic>SNR</italic><sub>in</sub>) is between <inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mo>-</mo></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M001.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M001c.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic></alternatives></inline-formula>10 dB and 10 dB, the output SNR (<italic>SNR</italic><sub>out</sub>) of the combined denoising algorithm increases by 12.0%<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mo>-</mo></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M002.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M002c.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic></alternatives></inline-formula>34.1% compared to the wavelet thresholding method and by 19.60%<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mo>-</mo></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M002.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M002c.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic></alternatives></inline-formula>56.8% compared to the EMD denoising method. Additionally, the RMSE of the combined denoising algorithm decreases by 18.1%<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mo>-</mo></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M004.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M004c.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic></alternatives></inline-formula>48.0% compared to the wavelet thresholding method and by 22.1%<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mo>-</mo></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M002.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/4693728D-C5C9-4a2c-9A1F-2B6E267A7E5F-M002c.jpg"><?fx-imagestate width="2.53999996" height="2.37066650"?></graphic></alternatives></inline-formula>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.

SHILAP Revista de lepidopterología
Openalex Percentile: Top 52%
Water Systems and Optimization
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