基于波原子变换的三维地震信号盲去噪

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摘要基于波原子变换的三维地震信号盲去噪算法，首先利用基于块的噪声估计算法估计信号噪声，然后采用循环平移处理信号并进行波原子变换，利用估计的噪声标准差按不同尺度分层设置阈值并进行修正，再采用改进的阈值函数处理波原子变换系数，最后进行波原子反变换与逆循环平移，得到去噪后三维地震信号。对含噪的合成与实际地震信号去噪，并与小波、双树复小波、曲波及传统波原子变换的去噪结果对比。结果表明本文算法较其它对比算法有明显优势，且随含噪量的增加，去噪优势愈加明显。从输出信噪比、均方误差以及峰值信噪比等评价指标可看出，基于波原子变换的三维地震信号盲去噪算法去噪效果最佳，其次为传统波原子变换算法，然后为曲波变换与双树复小波变换算法，传统小波变换算法的去噪效果最差。

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	何峰
	周亚同
	赵翔宇
	石超君

关键词 ：波原子, 三维地震信号, 去噪, 阈值修正

Abstract：A blind 3D seismic signal denoising algorithm based on the wave atom transform was proposed.Firstly, the patch-based noise estimation algorithm was used to estimate the noise, then the seismic signal was processed by cycle-spinning and the wave atom transform.The threshold was set and corrected according to different scales by noise standard deviation, and the improved threshold function was used to deal with the transformed coefficients.Finally, the inverse wave atom transform and cycle-spinning were performed to obtain the denoised signal.The synthetic and field seismic signal with noise were denoised and compared with wavelet, dual-tree complex wavelet, curvelet and traditional wave atom transform.The results show that the proposed algorithm has obvious advantages.With the increase of noise, the denoising advantage becomes more obvious.From the evaluation indicators including output signal to noise ratio, mean square error and peak signal to noise ratio can be analyzed, the best denoising algorithm is the blind 3D seismic signal denoising algorithm based on wave atom transform, followed by the traditional wave atom transform algorithm, then the curvelet transform algorithm and dual-tree complex wavelet transform algorithm, the wavelet transform algorithm performs worst.

Key words： wave atom;3D seismic signal denoising threshold correct

收稿日期: 2017-11-23 出版日期: 2019-04-15

引用本文:

何峰，周亚同，赵翔宇，石超君. 基于波原子变换的三维地震信号盲去噪[J]. 振动与冲击, 2019, 38(8): 88-95.
HE Feng，ZHOU Yatong，ZHAO Xiangyu，SHI Chaojun. Blind denoising of 3D seismic signals based on the wave atom transform. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(8): 88-95.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I8/88

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