@article{JIANG2023, 
author = {Pengbo JIANG and Minghui WEI and Bin WANG and Lixia JIANG and Fengmiao TU},
title = {Research on magnetic anomaly signal extraction of underground pipelines based on improved wavelet threshold denoising},
year = {2023},
journal = {Journal of Measurement Science and Instrumentation},
volume = {14},
number = {2},
pages = {189-199},
keywords = {magnetic anomaly signal, wavelet threshold denoising, wavelet basis function, optimum decomposition scale, threshold function},
url = {https://www.sciopen.com/article/10.62756/jmsi.1674-8042.2023022},
doi = {10.62756/jmsi.1674-8042.2023022},
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.}
}