基于PSO优化小波变换的测井信号去噪研究  

Research on log signal denoising based on PSO optimized wavelet transform

作　　者：魏振华[1,2,3] 胥越峰 刘志锋 舒志浩[2] Wei Zhenhua;Xu Yuefeng;Liu Zhifeng;Shu Zhihao(Engineering Research Center of Nuclear Technology Application(East China University of Technology),Ministry of Education,Nanchang 330013,China;School of Information Engineering,East China University of Technology,Nanchang 330013,China;Jiangxi Provincial Engineering Laboratory of Radiology Big Data Technology,Nanchang 330013,China)

机构地区：[1]核技术应用教育部工程研究中心,江西南昌330013 [2]东华理工大学信息工程学院,江西南昌330013 [3]江西省放射性地学大数据技术工程实验室,江西南昌330013

出　　处：《电子技术应用》2022年第11期115-120,共6页Application of Electronic Technique

基　　金：国家自然科学基金(11765001);江西省核技术应用教育部工程研究中心开放基金(HJSJYB2021-12)。

摘　　要：小波变换被广大科研工作者用于测井信号去噪研究上,而小波参数的选取直接影响最后的去噪效果,所以需要设计获取测井信号最佳小波变换参数的算法。为应对测井信号处理中多种多样的情况,创新性地提出用粒子群算法来改进小波变换参数的选取,并应用随机惯性权重策略改变粒子群算法权重参数,提升粒子群算法收敛速度,增强搜索寻优能力,引入自然选择机制以增加种群多样性,获得对应测井数据的最佳小波变换参数,将最佳小波变换参数应用到阈值法小波变换去噪中,有效分离了有用信号和无用噪声。该算法有效地提高了测井信号的信噪比,降低了均方根差,实现了对测井信号中噪声的有效去除。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.

关 键 词：测井信号去噪 粒子群算法 小波参数 小波变换去噪 软阈值法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]