物探与化探2017，Vol.41Issue(5)：907-913,7.DOI:10.11720/wtyht.2017.5.17
基于高精度字典学习算法的地震随机噪声压制
Seismic random noise suppression based on the high-precision dictionary learning algorithm
郭奇 1曾昭发 2于晨霞 1张思萌1
作者信息
- 1. 吉林大学 地球探测科学与技术学院,吉林长春 130026
- 2. 中水东北勘测设计研究有限责任公司,吉林长春130062
- 折叠
摘要
Abstract
In seismic exploration,the random noise severely distorts and interferes with seismic signals,and hence the denoising process is very important.In order to meet the high-precision requirement,the authors,based on the sparse and redundant representation theory,improve the dictionary update stage and the sparse coding stage in the conventional dictionary learning algorithm.While keeping the supports intact,the dictionary atoms are recurrently updated to adapt them to the specific seismic data.In the dictionary domain,large coefficients represent effective signals.Taking full advantage of this characteristic,the authors use several large coefficients from the last round of iteration as initial coefficients.In this way,the computational efficiency of the learning algorithm can be improved.The new algorithm is applied to synthetic and field seismic records and compared with the conventional K-SVD algorithm.The denoising results are satisfactory.It is shown that the new method can remove the random noise and protect the effective information at the same time.It is competitive in improving the signal-to-noise ratio of seismic records.关键词
地震去噪/字典学习/稀疏表示/随机噪声/信噪比Key words
seismic denoising/dictionary learning/sparse representation/random noise/signal-to-noise ratio分类
天文与地球科学引用本文复制引用
郭奇,曾昭发,于晨霞,张思萌..基于高精度字典学习算法的地震随机噪声压制[J].物探与化探,2017,41(5):907-913,7.
