中圖分類號(hào)： TP301.6 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.223028 中文引用格式： 魏振華，胥越峰，劉志鋒，等. 基于PSO優(yōu)化小波變換的測(cè)井信號(hào)去噪研究[J].電子技術(shù)應(yīng)用，2022，48(11)：115-120. 英文引用格式： Wei Zhenhua，Xu Yuefeng，Liu Zhifeng，et al. Research on log signal denoising based on PSO optimized wavelet transform[J]. Application of Electronic Technique，2022，48(11)：115-120.
Research on log signal denoising based on PSO optimized wavelet transform
1.Engineering Research Center of Nuclear Technology Application(East China University of Technology)， Ministry of Education，Nanchang 330013，China； 2.School of Information Engineering，East China University of Technology，Nanchang 330013，China； 3.Jiangxi Provincial Engineering Laboratory of Radiology Big Data Technology，Nanchang 330013，China
Abstract： Wavelet transform is widely used in the research of logging signal denoising, and the selection of wavelet parameters directly affects the final denoising effect, so it is necessary to design an algorithm to obtain the best wavelet transform parameters of logging signal. In this paper, the random inertia weight strategy is innovatively proposed to change the weight parameters of particle swarm optimization algorithm, which improves the convergence speed of particle swarm optimization algorithm, enhances the ability of searching for optimization, and obtains the optimal wavelet transform parameters. The optimal wavelet transform parameters are applied to the wavelet denoising of soft threshold method, which can effectively separate the useful signal and useless noise. This algorithm can effectively improve the signal-to-noise ratio of logging signal, reduce the root mean square difference, and realize the effective removal of noise in logging signal.
Key words : logging signal denoising；particle swarm optimization；the wavelet parameters；wavelet transform denoising；soft threshold method
