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| Seismic random noise suppression based on the high-precision dictionary learning algorithm |
| GUO Qi1, 2, ZENG Zhao-Fa1, YU Chen-Xia1, ZHANG Si-Meng1 |
1.College of Geo-Exploration Science and Technology,Jilin University,Changchun 130026,China;
2.China Water Northeastern Investigation,Design &
Research Co. Ltd.,Changchun 130062,China; |
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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.
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Received: 26 August 2016
Published: 20 October 2017
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