JIANG Pengbo1,2, WEI Minghui1,2, WANG Bin3, JIANG Lixia1,2, TU Fengmiao1,2
(1. School of Mechanical and Electrical Engineering, Southwest Petroleum University, Chengdu 610500, China;2. Oil and Gas Equipment Technology Sichuan Province Science and Technology Resource Sharing Service Platform, Chengdu 610500, China;3. Bohai Sea Drilling, Tianjin 300280, China)
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.
Key words: magnetic anomaly signal; wavelet threshold denoising; wavelet basis function; optimum decomposition scale; threshold function
References
[1]WANG Y. Research and application of detection technology and method of urban underground pipeline. Jilin: Jilin University, 2012.
[2]ZHANG H. Research on magnetic anomaly signal detection and source location methods. Chengdu: University of Electronic Science and Technology of China, 2015.
[3]ZHOU W. Noise reduction processing of weak magnetic anomaly signal based on wavelet entropy. Optical Instruments, 2013, 35(4): 12-16.
[4]DAI Z H, ZHOU S H, SHAN S. Magnetic anomaly detection algorithm based on minimum entropy filter. Mine Warfare and Ship Protection. 2017, 25(2): 16-19.
[5]YANG Y, CHEN Z X. Airborne magnetic exploration method based on orthogonal basis decomposition algorithm. Electronic Technology, 2014, 27(7): 36-39.
[6]DAUBECHIES I. Ten lectures on wavelet. Beijing: National Defense Industry Press, 2004.
[7]BAYER F M, KOZAKEVICIUS A J, CINTRA R J. An iterative wavelet threshold for signal denoising. Signal Processing, 2019, 162(SEP.): 10-12.
[8]ZHAO L H, XU X Q. Detection of weak magnetic anomaly signal using EMD. Advanced Materials Research, 2014, 3181: 926-930.
[9]ZHU J J, ZHANG Z T, KUANG C L, et al. A reliable evaluation index of wavelet denoising quality. Journal of Wuhan University (Information Science Edition), 2015, 40(5): 688-694.
[10]ZHANG M, WANG X Z, TENG Y T, et al. Denoising analysis of geomagnetic data based on emd wavelet threshold filtering. Seismic and Geomagnetic Observation and Research, 2012, 33(Z1): 171-175.
[11]HU J F, CHEN D X, PAN M C, et al. Research on interference elimination method in underwater geomagnetic survey. Metrology Technology, 2008(11): 29-32.
[12]GE L, FAN W, HE Y, et al. Research on denoising of electromagnetic flowmeter signal based on wavelet transform. Journal of Xuzhou Institute of Technology (Natural Science Edition), 2018, 33(3): 68-75.
[13]BALLESTEROS L, MORENO A. Wavelet-denoising on hardware devices with perfect reconstruction, low latency and adaptive thresholding. Computers Electrical Engineering, 2013, 39(4): 1300-1311.
[14]MUKHOPADHYAY S, MANDAL J K. Wavelet based denoising of medical images using sub-band adaptive thresholding through genetic algorithm. Procedia Technology, 2013, 10: 680-689.
[15]AZZALINI A, FARGE M, KAI S. Nonlinear wavelet thresholding: A recursive method to determine the optimal denoising threshold. Applied Computational Harmonic Analysis, 2005, 18(2): 177-185.
[16]NIE X H, PAN Z, ZHANG D S, et al. Wavelet-based adaptive detection of magnetic anomaly signal contaminated by 1/f noise. Applied Mechanics Materials, 2014, 3360: 599-601.
[17]XIE B, XIONG Z, WANG Z, et al. Gamma spectrum denoising method based on improved wavelet threshold. Nuclear Engineering Technology, 2020, 52(8): 1771-1776.
[18]LU Q, XING X C, LI X J, et al. Interference analysis and denoising processing of digital geomagnetic signal from Jing balcony. South China of Seismology, 2013, 33(1): 49-54.
[19]ZHANG J W, FENG Y, LI W. Research on wavelet denoising method based on an improved threshold function. Electronic Design Engineering, 2017, 25(9): 137-140+144.
[20]CAO X W, SUN S Q, XUAN L M, et al. Optimization research of wavelet analysis in denoising of pipeline leakage signal. Petrochemical Automation, 2019, 55(1): 29-34.
[21]CHEN Z, WANG S. An improved method of wavelet threshold denoising for satellite signal//8th International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC). Jul. 19-21, 2018, Harbin, China. New York: IEEE, 2018: 1774-1777.
[22]LI B, ZHANG L, ZHANG Q, et al. An EEMD-based denoising method for seismic signal of high arch dam combining wavelet with singular spectrum analysis. Shock & Vibration, 2019, 2019: 1-9.
[23]XU C F, LI G K. Practical wavelet method. Beijing: National Defense Industry Press, 2001.
[24]KIM K I, HWAN R U, PIL C B. An appropriate thresholding method of wavelet denoising for dropping ambient noise. International Journal of Wavelets, Multiresolution and Information Processing, 2018, 16: 1850012-1850016.
[25]ZHANG N, LIN P, XU L, et al. Application of weak signal denoising based on improved wavelet threshold//5th International Conference on Mechanical and Aeronautical Engineering: ICMAE 2019, Dec. 12-15, 2019, Sanya, China. New York: Materials Science and Engineering, 2019: 507-512.
基于改进小波阈值去噪的地下管线磁异常信号提取研究
姜蓬勃1,2, 韦明辉1,2, 王彬3, 江丽霞1,2, 涂凤秒1,2
(1. 西南石油大学 机电工程学院, 四川 成都 610500; 2. 石油天然气装备技术四川省科技资源共享服务平台, 四川 成都 610500;3. 渤海钻探, 天津 300280)
摘要:为解决地下管道磁异常信号采集受环境噪声干扰的问题, 本文采用小波阈值法作为地下管道磁异常信号的基本去噪模型, 并加入复合归一化指标。 在传统评价指标的基础上, 优化小波基函数、 最优分解尺度和阈值函数, 确定具有可调变量基因的改进阈值函数, 以弥补传统软硬阈值函数估计方法的断点振荡和丢失细节的不足。 通过仿真信号和实验, 对含噪磁异常信号进行去噪及效果评价。 仿真和实验结果表明, 改进后的阈值函数能有效消除周围噪声对磁异常信号的干扰, 进而准确恢复原始磁异常信号, 与常规方法相比, 提取的信号失真较小, 信噪比较高, 可为后续的磁异常反演提供更准确的数据支持。
关键词:磁异常信号; 小波阈值去噪; 小波基函数; 最佳分解尺度; 阈值函数
引用格式:JIANG Pengbo, WEI Minghui, WANG Bin, 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. DOI: 10.3969/j.issn.1674-8042.2023.02.008
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