Local Time Slowness Adaptive Filter and Optimal Window Strategies
TL;DRAbstract
The local slant-stack transform (LSST) is a technique commonly used in seismic data processing to filter complex seismic waves, when the τ-p transform and the f-k filters cannot be used. To automatically adapt the characteristics of the filters based on the LSST to the slowness variation of the wavefronts in seismic profiles these filters are usually combined with an instantaneous slowness estimator. To make an objective design, we analyze the relation between the slowness resolution, the window parameters and the main frequency components of the waves to employ the most suitable windows in order to achieve an optimum slowness and space resolutions. We further validate this design procedure with two synthetic examples.
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The local slant-stack transform (LSST) is a technique commonly used in seismic data processing to filter complex seismic waves, when the τ-p transform and the f-k filters cannot be used. To automatically adapt the characteristics of the filters based on the LSST to the slowness variation of the wavefronts in seismic profiles these filters are usually combined with an instantaneous slowness estimator. To make an objective design, we analyze the relation between the slowness resolution, the window parameters and the main frequency components of the waves to employ the most suitable windows in order to achieve an optimum slowness and space resolutions. We further validate this design procedure with two synthetic examples.
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