横向连续性保持的自适应经验小波变换算法研究

Research on Adaptive Empirical Wavelet Transform for Transverse Continuity Retention

时频分析方法作为分析与处理非平稳信号的有力工具,一直是现代信号处理领域研究的热点。经验小波变换(Empirical Wavelet Transform,EWT)结合了经验模态分解与小波变换的优势,能够自适应地提取信号中调频-调幅分量。本文对经验小波变换原理进行了研究,为了解决经验小波变换在处理复杂信号时存在的问题,将数学形态学与经验小波变换结合,进一步建立了横向连续性保持的经验小波算法,并用实际的地震资料对效果进行了验证。

As a powerful tool for analyzing and processing non-stationary signals,time-frequency analysis has always been the focus of modern signal processing research.Empirical Wavelet Transform(EWT)combines the advantages of empirical mode decomposition and wavelet transform,and adaptively extracts FM-AM components in the signal.In this paper,the principle of empirical wavelet transform is deeply studied.In order to overcome the problem of EWT in dealing with complex signals,the mathematical morphology is combined with empirical wavelet transform.On this basis,an empirical wavelet algorithm for maintaining transverse continuity is established.The effect of the method is verified by actual seismic data.