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Multifrequency beam-based migration in inhomogeneous media using windowed Fourier transform frames
Geophysical Journal International ( IF 2.8 ) Pub Date : 2020-08-03 , DOI: 10.1093/gji/ggaa365
Ram Tuvi 1 , Zeyu Zhao 1 , Mrinal K Sen 1
Affiliation  

We consider the problem of inhomogeneous subsurface imaging using beam waves. The formulation is based on the ultra-wide-band phase-space beam summation (UWB-PS-BS) method, which is structured upon windowed Fourier transform (WFT) expansions of surface fields and sources. In this approach, the radiated field is given as a superposition of beam propagators. Here, we use the beams first for expanding the surface sources and the scattered data, and then for imaging where we use the backpropagation and cross-correlation of beams. This formulation enables a target oriented imaging approach, where we take into account only pairs of source and receiver beams that pass near a region of interest, and thus extract only the relevant data arriving from this region. It also leads to a priori sparse representation of both the beam domain data and the beam propagators. A physical cogent for the beam domain data is obtained under the Born approximation. The beam domain data can be approximated as the local interaction between the beam propagators and the medium reflectivity. Thus, one may interpret the beam domain data as a local Snell’s law reflection in the direction defined by the vector summation of the incident beam and backpropagated beam ray parameters. We demonstrate a physical model for the beam domain data and the salient features of the proposed imaging algorithm using numerical examples.

中文翻译:

使用开窗傅立叶变换框架的非均匀介质中基于多频波束的迁移

我们考虑使用束波的地下非均匀成像问题。该公式基于超宽带相空间波束求和(UWB-PS-BS)方法,该方法基于表面场和源的开窗傅立叶变换(WFT)展开构造。在这种方法中,辐射场作为光束传播器的叠加给出。在这里,我们首先使用光束扩展表面源和散射数据,然后在成像时使用光束的反向传播和互相关。该公式使目标定向成像方法成为可能,在这种方法中,我们仅考虑在感兴趣区域附近通过的一对源和接收器光束,因此仅提取从该区域到达的相关数据。这也导致先验光束域数据和光束传播器的稀疏表示。在博恩近似下获得波束域数据的物理余量。束域数据可以近似为束传播器与介质反射率之间的局部相互作用。因此,可以将光束域数据解释为在由入射光束和反向传播的光束参数的矢量和定义的方向上的局部斯涅尔定律反射。我们使用数值示例演示了束域数据的物理模型和提出的成像算法的显着特征。
更新日期:2020-08-31
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