小波变换域K L变换及其去噪效果分析

K-L transformation in wavelet conversion domain and the analysis of de-noise effect

(1.西南石油大学，四川成都610500; 2.中国石油集团四川石油管理局地球物理勘探公司，四川成都610059；3.中国石油天然气股份有限公司西南油气田分公司重庆气矿，重庆404000)

Southwest Petroleum University, Chengdu 610500, China

K L变换利用相邻地震道的相关性来去除随机噪声，但对于倾斜和弯曲同相轴反射去噪效果不佳。采用改进的时变倾角扫描叠加K L变换能够较好地去除随机噪声，但由于在时间域进行，没有考虑有效信号和随机噪声在频率域的特点，高频有效信号易受压制。小波变换具有较强的时频分析能力，在小波变换域进行K L变换，可以实现分时分频K L变换去噪。介绍了小波变换域K L变换压制随机噪声的基本原理，即先将地震信号进行小波分解形成分时分频的小波包剖面，然后用K L变换对小波包剖面进行去噪，再将去噪后的小波包剖面重构回地震剖面，从而达到消除随机噪声的目的。理论模型计算和实际资料处理表明，小波变换域K L变换去噪方法在有效去除随机噪声的同时能够保护高频有效信号。

The K-L transformation uses the coherence of adjacent seismic traces to remove the random noise. But the de-noise effect is not good for sloping and bending event reflection. Although the advanced time-variation dip sweep stack K-L transformation can remove random noises, the characteristics of effective signals and random noises in frequency domain is not taken into consideration, making the high-frequency effective signals lost. Because the wavelet transformation has a good ability in time-frequency analysis, the K-L transformation in wavelet domain can remove noises in time and frequency domain separately. The principles of suppressing random noises with K-L transformation in wavelet conversion domain are that 1) wavelet decomposition is carried out on seismic signals to form wavelet packet sections in time sharing and frequency division, 2) K-L transformation is utilized to remove noises on sections, and 3) the de-noise wavelet packet sections are reconstituted into seismic sections to remove random noises. The theoretical model computation and actual data processing show that the K-L transformation in wavelet conversion domain can remove random noises effectively and preserve the effective high-frequency signals simultaneously.