Seismic data filtering using a Gabor representation

Gabor representations are signal expansions using sets of functions that are localized and concentrated in time and frequency. This characteristic makes them suitable candidates for filtering data where the desired signal or noise is nonstationary or time-dependent. It is shown that Gabor representations formulated with frame theory can be used for time-dependent noise removal. Furthermore, their ability to filter noise in the presence of a nonstationary signal enables them to outperform singular value decomposition eigenimage filtering techniques in the removal of incoherent noise in common midpoint data with a moderate fold.<
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