基于S变换自适应滤波迭代的多源地震数据分离

S变换是由小波变换和短时傅里叶变换发展而来的时频分析方法，动校正后共中心点道集（NMO-CMP）中相同时刻各道地震信号的振幅、相位基本一致，多源地震数据中的混叠噪声在CMP道集中呈随机分布；将NMO-CMP道集叠加，以叠加道S变换谱为参考，可以判断出各道S变换谱中噪声与信号的分布。根据NMO-CMP道集中地震道S变换谱与叠加参考道S变换谱之间的偏离程度设计自适应滤波器，通过多级滤波、多次迭代的方法，提取多震源数据中的有效反射信号、分离混叠噪声。理论数据和实际数据模拟的多源地震数据试算结果表明，本文方法能够有效提取多源地震数据中的有效反射信号、分离混叠噪声和随机噪声。

Deblending of seismic data based on S-transform adaptive filtering iteration

S-transform is a time-frequency analysis method developed from wavelet transform and short-time Fourier transform.After NMO correction,the amplitude and phase of the signal in every seismic trace of a CMP gather are consistent at the same time,and the blended noises in blended seismic data are distributed randomly.Therefore,the distribution of noises and signals in the S-transform spectra of each trace can be effectively determined by stacking CMP traces after NMO correction,and using the S-transform spectra of the stacked trace as references,the designed filter of S-transform spectra can separate the blended noises.In this study,we first designed a filter based on the deviation between the S-transform spectra of seismic data in CMP gathers and the S-transform spectra of stacked traces after NMO correction,and then extracted signals and deblended noises after multi-level adaptive filtering and multiple iterations.Applications to theoretical data and simulated actual seismic data have proved the method can effectively extract signals and separate deblended noises and random noises.