Normal moveout correction is crucial in seismic data processing, but it generates a wavelet-stretching effect, especially on the larger offset or incident-angle seismic data. Wavelet stretching reduces the dominant frequency of seismic data. The greater the incident angle or offset, the lower dominant the frequency becomes. This is an unfavorable effect to amplitude variation with offset analysis. Therefore, we have introduced a wavelet stretching correction method based on the multiwavelet decomposition (MWD) algorithm. First, it decomposes the near-offset pilot trace and all the far-offset seismic traces in the same gather into a series of wavelets via the MWD algorithm. Then, the dominant frequencies of wavelets in the far-offset seismic traces are replaced by those corresponding wavelets in the pilot trace. Finally, the wavelets after the stretching correction are used to reconstruct the seismic trace. The model and field-data processing results show that this method can not only effectively reduce the wavelet stretching effect but it can also maintain the amplitude of each wavelet as invariant during the stretching correction procedure. Because only the frequencies of the decomposed wavelets are used, and no inverse wavelet operators is introduced, the wavelet stretching correction method does not distort the amplitude information.

中文翻译：

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基于多小波分解算法的地震聚集小波拉伸校正

法向运动校正在地震数据处理中至关重要，但是它会产生小波拉伸效果，尤其是对于较大偏移量或入射角的地震数据。小波拉伸降低了地震数据的主导频率。入射角或偏移越大，频率变得越低。这对偏移分析的幅度变化是不利的影响。因此，我们介绍了一种基于多小波分解（MWD）算法的小波拉伸校正方法。首先，它通过MWD算法将同一地点的近偏移量的引航迹线和所有远偏移量的地震迹线分解为一系列小波。然后，将偏移极远的地震轨迹中的小波的主频替换为导频轨迹中的那些对应的小波。最后，拉伸校正后的小波用于重建地震道。模型和现场数据处理结果表明，该方法不仅可以有效地减小小波的拉伸效果，而且可以在拉伸校正过程中保持每个小波的振幅不变。由于仅使用分解后的小波的频率，并且没有引入逆小波算子，因此小波拉伸校正方法不会使幅度信息失真。