基于Contourlet变换的K-L变换地震随机噪声自适应衰减方法

发布时间：2018-05-14 06:17

本文选题：Contourlet变换 + K-L变换　；参考：《石油物探》2017年05期

【摘要】：地震资料中有效反射信号具有丰富的纹理及边缘特性,在Contourlet变换域系数较大并具有相关性,而随机噪声均匀分布于Contourlet变换域且系数较小。考虑K-L变换具有分类特征提取的优势,在Contourlet变换基础上应用K-L变换,采用最大似然估计法和多尺度噪声估计法估算地震记录中有效信号及随机噪声的Contourlet系数方差,并将其应用到K-L变换域能量百分比阈值函数的定义中,自适应地确定用于K-L反变换的特征向量,修改Contourlet变换的系数,再进行Contourlet反变换压制随机噪声。数值模拟及实际地震资料去噪效果表明,基于Contourlet变换的K-L变换去噪方法不仅可以有效地压制地震资料中的随机噪声,提高地震资料信噪比,而且具有较好的保真性。
[Abstract]:The effective reflected signals in seismic data have rich texture and edge characteristics, and the coefficients in the Contourlet transform domain are large and correlated, while the random noise is uniformly distributed in the Contourlet transform domain and the coefficients are small. Considering K-L transform has the advantage of classification feature extraction, K-L transform is applied on the basis of Contourlet transform, maximum likelihood estimation method and multi-scale noise estimation method are used to estimate the variance of Contourlet coefficient of effective signal and random noise in seismic records. It is applied to the definition of energy percentage threshold function in K-L transform domain. The eigenvector for K-L inverse transformation is determined adaptively, the coefficients of Contourlet transform are modified, and then Contourlet inverse transform is used to suppress random noise. Numerical simulation and actual seismic data denoising effect show that K-L transform denoising method based on Contourlet transform can not only effectively suppress random noise in seismic data and improve signal-to-noise ratio of seismic data, but also has good fidelity.
【作者单位】：中国石油化工股份有限公司石油物探技术研究院;
【分类号】：P631.44

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