Xi’an Research Institute of China Coal Technology and Engineering Group,Xi’an　 710077,China

针对地震资料中背景噪声较强,有效弱信号淹没其中难以识别,且在时间域地震有效信号和随机噪声又较难分离的问题,尝试将其通过Curvelet变换进行信噪分离。在Curvelet的不同尺度域采用自适应阈值函数对噪声进行压制,保留有效信号系数;同时,阈值函数中引入不同尺度域地震剖面信噪比,通过与信噪比相关的权值系数降低具有高信噪比的尺度域阈值,从而保留被随机噪声淹没的弱信号;最后对残留噪声系数再应用中值滤波,进一步压制噪声,突出弱信号。与常用于弱信号识别处理的小波变换,以及Curvelet变换的固定阈值处理方法相比,具有多尺度多方向性的Curvelet变换能够更加有效的刻画地震信号,结合自适应的阈值处理时,在弱信号识别及去噪方面具有明显优势。

The strong background noise,which overwhelms effective signal,is one of common problems in seismic data. Moreover,the effective signal and random noise are difficult to be separated in the time domain. However,they may be separated in the Curvelet domain. With its multi-scale characteristic,the Curvelet transform can attenuate random noise while retain effective signal by setting a threshold to curvelet coefficient. Moreover,the SNR ratio of different scale seismic sections is introduced in the threshold function,then the effective signal and weak signal can be kept to the greatest extent by reducing the threshold in some domains with high SNR ratio. Finally,using the median filter to the residual coefficient of noise,the denoised seismic data are obtained,also the weak signal is enhanced at the same time. Compared with other methods which commonly used in the detection of weak signal processing,such as wavelet trans- form and curvelet transform using fixed threshold,the Curvelet transform using adaptive threshold describes the seismic signal more effectively,and also has obvious advantages in seismic weak signal identification and noise elimination.

国家科技重大专项资助项目(2011ZX05040-002)；科技部科研院所技术开发研究专项资金资助项目(2013EG122200)；

Jin Dan,Cheng Jianyuan,Wang Baoli,et al. Seismic weak signal identification and noise elimination based on curvelet domain[J]. Journal of China Coal Society,2016,41(2):332-337.