小波包分析与奇异值分解（SVD）叠前去噪方法

小波分析多用于去除面波，奇异值分解多用于去除相干干扰，而对于强相干干扰和面波共存的地震记录，仅使用单一去噪方法难以奏效。为此本文将小波包分析与奇异值分解结合起来，就可以解决单一去噪方法难以解决的去噪问题。此法首先利用小波包变换进行分频处理，然后在分频的记录中，用自动追踪同相轴的方法找出相干干扰和面波同相轴的方向，再利用奇异值分解(SVD)方法恢复相干干扰和面波的波场，最后将其从原始地震记录中减去，即可获得去噪后的地震记录。通过对实际资料的试算，表明该计算方法的效果是明显的。

Analysis of wavelet package and prestack noise suppressed method in singular value decomposition (SVD).

The wavelet analysis is mostly used to suppress the surface wave and singular value decomposition is mostly used to suppress coherent interference,but if strong coherent interference coexisted with surface wave in seismic records,only using a single noise suppressed method is difficult to achieve success.Therefore,the paper combined the wavelet package analysis with singular value decomposition that can solve the noise suppressed issue which can't be successfully treated only by a single noise suppressed method.The method first uses the wavelet package for frequency shared processing,then uses automatic events tracing method to find out the direction of events of interference and surface wave on frequency shared records,next uses singular value decomposition (SVD) method to restore the wavefields of coherent interference and surface wave,finally minus of the results from the original seismic records resulted in noise suppressed seismic records.It is shown by evaluation of practical data that the effects of the computational method are evident.