基于小波变换的地震资料去噪处理研究

常规小波阈值去噪方法未能充分利用地震信号相关性的特点进行去噪,为此在多层小波变换中引入了双变量概率分布模型。基于贝叶斯估计理论,得到了相应的双变量收缩函数;基于层内局域方差估计,得到了一种局域自适应去噪算法。在实验中,将该算法分别应用于实值离散小波变换域和复数小波变换域,并和隐马尔科夫模型的去噪方法进行了比较。图像处理和地震模型测试结果表明,复数小波变换的局域自适应收缩算法去噪效果最好。

Seismic data denoising research based on wavelet Transform

Conventional denoising methods by threshold filter in wavelet domain do not utilize the correlations of seismic data to remove noises, so a locally adaptive denoising algorithm was presented. The new algorithm assumed the statistical dependence among wavelet coefficients. First in this paper, a bivariate probability distribution model was introduced to model the statistics of wavelet coefficients, and corresponding nonlinear threshold function (bivariate shrinkage function) was derived from the model using the Bayesian estimation theory. Secondly, using locally variance estimation, a locally adaptive image-denoising algorithm was presented. Also this algorithm could be applied to the complex wavelet domain. Experimental results and comparison analysis are given to illustrate the effectiveness of this denoising algorithm.