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Spatial aliasing removal using deep learning super-resolution
- Source: First Break, Volume 37, Issue 9, Sep 2019, p. 87 - 92
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- 01 Sep 2019
Abstract
Seismic data are often either irregularly or insufficiently sampled. Irregular sampling can be due to encountered obstacles in the acquisition, thus resulting in a seismic data gap, whereas insufficient sampling is the result of a coarse acquisition grid, thus leading to sparse sampling along the spatial direction of the data. This irregular or insufficient sampling can affect the accuracy and resolution of seismic data processing steps such as surface-related multiple elimination, migration and inversion. For example, in the simple case of sparse sampling, it leads not just to the loss of high-wavenumbers, but also causes spatial aliasing due to the overlap of aliasing energy artifacts with the signal energy. When we image this spatially aliased coarse data, we encounter the trade-off between the resolution of the image and the aliasing artifacts. Therefore, seismic data interpolation has always been an essential requirement in seismic data processing.