频率域奇异值分解压制随机噪声方法

 奇异值分解(SVD)是提高信噪比的一种较新的有效手段之一。本文从数学角度阐述了奇异值分解SVD滤波技术增强地震资料信噪比的原理,对比了时间域和频率域SVD技术压制随机噪声的处理效果。结果表明,时间域SVD技术只能对水平或接近水平的同相轴进行信号增强,对倾斜同相轴的处理效果较差;而频率域SVD技术既可以处理水平同相轴,也可以处理倾斜同相轴,对提高地震剖面信噪比具有很好的效果。本文用3个简单的合成地震记录和1个实际地震资料检验了SVD两种方法的应用效果。结果表明,本文方法可以达到随机噪声压制的效果。

Seismic Random Noise Suppression by Using Frequency-domain Singular Value Decomposition

Singular Value Decomposition (SVD) is a new and effective method for random noise suppression. The principle of the SVD filtering technique, which enhances the signal to noise ratio of seismic data, is illustrated. Then, by applying time-domain and frequency- domain SVD techniques to suppress random noise generated from a variety of seismic model data, their processing results are compared with each other. The results indicate that the time-domain SVD technique is only able to enhance flat or near flat events. Whenencountering dip events, it is less effective. However, the frequency-domain SVD technique is able to enhance both flat and dip events, and improve the signal to noise ratio of seismic profile. Three simple synthetic seismograms and one real seismic data are used, and the practical effects of these two SVD methods are verified. The results show that the frequency-domain SVD is effective for suppressing the random noise.