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Open Access

Research on magnetic anomaly signal extraction of underground pipelines based on improved wavelet threshold denoising

Pengbo JIANG1,2, Minghui WEI1,2( ), Bin WANG3, Lixia JIANG1,2, Fengmiao TU1,2
School of Mechanical and Electrical Engineering, Southwest Petroleum University, Chengdu 610500, China
Oil and Gas Equipment Technology Sichuan Province Science and Technology Resource Sharing Service Platform, Chengdu 610500, China
Bohai Sea Drilling, Tianjin 300280, China
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Abstract

The collection of magnetic anomaly signal underground pipelines is disturbed by environmental noise. The wavelet threshold method is used as the basic denoising model of the magnetic anomaly signal of underground pipelines, and a composite normalization index is added on the basis of the traditional evaluation index to optimize the wavelet basis functions, optimal decomposition scale and threshold function. An improved threshold function with tunable variable genes is determined to make up for the lack of breakpoint oscillations and loss details of traditional soft and hard threshold function estimation methods. The denoising and effect evaluation of the noisy magnetic anomaly signal is carried out through the simulation signal and experiment. The simulation and experimental results show that the improved threshold function can effectively eliminate the interference of the surrounding noise on the magnetic anomaly signal, and then accurately restore the original magnetic anomaly signal compared with conventional methods, the extracted signal has less distortion and higher SNR, which can provide more accurate data support for subsequent magnetic anomaly inversion.

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Journal of Measurement Science and Instrumentation
Pages 189-199

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Cite this article:
JIANG P, WEI M, WANG B, et al. Research on magnetic anomaly signal extraction of underground pipelines based on improved wavelet threshold denoising. Journal of Measurement Science and Instrumentation, 2023, 14(2): 189-199. https://doi.org/10.62756/jmsi.1674-8042.2023022

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Received: 27 November 2022
Published: 01 June 2023
© The Author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.