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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Fast Single-Step Least-Squares Reverse-Time Imaging via Adaptive Matching Filters in Beams
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Fast Single-Step Least-Squares Reverse-Time Imaging via Adaptive Matching Filters in Beams

机译：通过光束中的自适应匹配滤波器快速单步次数倒像成像

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摘要

Least-squares reverse time migration (LSRTM) is a powerful tool in seeking broadband-wavenumber reflectivity images. It produces better images over reverse-time migration (RTM) at the expense of computational cost. The Hessian effect can be measured in the image domain with the point-spread function (PSF). Here, we try to measure the Hessian effect in the data domain with the so-called trace-spread function (TSF). The difference between PSF and TSF is that the former originates from ${{mathbf{L}}<^>{T}}{mathbf{L}}$ in the image domain while the latter from ${mathbf{L}}{{mathbf{L}}<^>{T}}$ in the data domain. By comparing the TSFs with their original corresponding traces (or beams), we can design adaptive matching filters for preconditioning to alleviate the Hessian effect. However, the full TSF matrix is expensive. In this article, we propose a multiscale solution, which first has a diagonal approximation to ${mathbf{L}}{{mathbf{L}}<^>{T}}$ in beams, and then handle the full submatrix composed of the one-beam traces using the Sherman-Morrison formula. The preconditioned beams are superimposed into a "deblurred" data for remigration. Through synthetic and real data examples, we see that: 1) single-step data-domain LSRTM can yield deblurred RTM images via adaptive matching filters and 2) the beam-by-beam consideration outperforms the trace-by-trace one.
机译：最小二乘反向时间迁移（LSRTM）是寻求宽带波数反射率图像的强大工具。它以牺牲计算成本为代价产生更好的图像更好的图像迁移（RTM）。 Hessian效果可以用点扩展功能（PSF）在图像域中测量。在这里，我们尝试使用所谓的轨迹扩展功能（TSF）测量数据域中的Hessian效果。 PSF和TSF之间的差异是前者源自在图像域中的$ {{ mathbf {l}} <^> {t}} { mathbf {l}} $ { mathbf {l }} {{ mathbf {l}}在数据域中$ in。通过将TSF与其原始相应的迹线（或光束）进行比较，我们可以设计自适应匹配过滤器，以便进行预处理以缓解Hessian效果。但是，完整的TSF矩阵昂贵。在本文中，我们提出了一个多尺度解决方案，该解决方案首先具有比对角线近似为$ { mathbf {l}} {{ mathbf {l}} $ in beam，然后处理完整的subsatrix由使用Sherman-Morrison公式的单束迹线组成。预处理光束叠加到用于丢失的“deBlurred”数据中。通过合成和实际数据示例，我们看到：1）单步数据域LSRTM可以通过自适应匹配滤波器产生解束RTM图像，2）光束考虑优于跟踪逐个轨道。

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