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近几年,小波分析技术在测井信号去噪处理方面得到了较为广泛的应用,但是传统的小波变换依赖于傅立叶变换,因而有大量的褶积运算,占用内存大,运算速度较慢。为此,采用基于提升算法的第二代小波变换来进行测井信号的去噪处理,提升算法是构造第二代小波的关键技术。通过分析提升算法的基本原理,尝试使用第二代小波对测井信号进行不同分辨率下的分解处理,并使用软限幅函数处理小波分解的细节系数,将处理过的细节系数和近似系数一起重构信号。第二代小波变换的结果与传统小波变换的结果对比表明第二代小波变换的处理效果要优于传统的小波变换,而且运算速度快,占用内存小。
In recent years, wavelet analysis has been widely used in de-noising of logging signals. However, the traditional wavelet transform relies on Fourier transform, resulting in a large number of convolution operations, large memory usage and slow computation speed. Therefore, the second generation wavelet transform based on lifting algorithm is used to denoise the logging signal. The lifting algorithm is the key technology to construct the second generation wavelet. By analyzing the basic principle of lifting algorithm, we try to use the second generation wavelet to decompose the logging signal under different resolutions, and use the soft limiting function to process the detail coefficients of the wavelet decomposition. The processed detail coefficients are combined with the approximate coefficients Reconstruction signal. Comparison between the results of the second generation wavelet transform and the traditional wavelet transform shows that the second-generation wavelet transform is superior to the traditional wavelet transform in the processing efficiency and the memory is small.