Beamlet prestack depth migration and illumination： A test based on the Marmousi model

Beamlet 来源容易让强壮的本地、方向性的人物和罐头完成本地照明和移植。而且，他们比常规迁居方法提供更好的迁居结果。我们介绍让的横梁的基本原则包括窗口的 Fourier 变换和框架理论的 prestack 深度移植。我们基于 Gaussian 功能解释 Gabor-Daubechies (G-D ) 框架。Beamlet 分解在波浪地的本地空格和方向提供信息。我们综合横梁让来源和横梁在使用矩形和 Gaussian 窗口的小浪域让记录然后外推有一个 Fourier 有限差别的操作符的综合数据。我们用标准 Marmousi 模型测试方法。由比较并且分析让并且微笑的方向性的横梁与不同窗户和方向让的单身者的迁居结果，我们证明有 Gaussian 横梁的 prestack 深度迁居的有效性让方法。

Beamlet sources have strong local and directional character and can easily accomplish local illumination and migration. Besides, they provide better migration results than conventional migration methods. We introduce the basic principles of beamlet prestack depth migration that includes a windowed Fourier transform and frame theory. We explain the Gabor-Daubechies （G-D） frame based on a Gaussian function. Beamlet decomposition provides information on the local space and direction of wavefield. We synthesize the beamlet source and beamlet records in the wavelet domain using both rectangle and Gaussian windows and then extrapolate the synthesized data with a Fourier finite-difference operator. We test the method using the standard Marmousi model. By comparing and analyzing the migration results of single directional beamlet and beamlets with different windows and directions, we demonstrate the validity of the prestack depth migration with Gaussian beamlets method.