论文详情

基于贝叶斯阈值估计的曲波域自适应随机噪声衰减

石油物探

2013年 52卷第No. 2期

阅读：71

Title

The self-adaptive random noise attenuation in curvelet domain based on Bayes estimation

单位

1.中海油田服务股份有限公司物探事业部，天津300451;2.中国石油大学(北京)油气资源与探测国家重点

实验室，北京102249;3.中国石油大学(北京)CNPC物探重点实验室，北京102249

Organization

Liu Wei,Geophysical Department,China Oilfield Services Limited,Tianjin 300451,China

摘要

与小波变换相比，曲波变换可以更好地表达曲线奇异函数的异向性。根据曲波变换对于光滑且二阶

连续可微函数所具有的最优逼近性能，结合贝叶斯理论，给出了基于曲波域的自适应阈值去噪方法。通

过对合成地震记录及实际地震数据的处理，验证了该方法的有效性。结果表明，与传统小波阈值法相比

，基于贝叶斯阈值估计的曲波域自适应去噪方法不仅可以很好地衰减随机噪声，有效提高地震资料的信

噪比，而且能够较好地保持有效信号。

Abstract

The curvelet transform can represent anisotropy of curved singular function better than wavelet transform.

According to the optimal approximation property of the curvelet transform for smoothing and second-order

continuous differentiable singular functions, an adaptive thresholding denoising method for combining the

improved curvelet transform with Bayesian theory is proposed. The processing results of seismogram and real

seismic data verify that curvelet transform for self-adaptive random noise attenuation based on Bayes estimation

can not only attenuate random noise and effectively improve S/N in seismic data but also well preserve effective

signal compared with conventional wavelet transform threshold method.

关键词：

曲波变换; 小波变换; 随机噪声衰减; 贝叶斯估计; 自适应阈值;

Keywords:

curvelet transform; wavelet transform; random noise attenuation; Bayes estimation; self-adaptive

threshold

;

基金项目

DOI

10.3969/j.issn.1000-1441.2013.02.001