Seismic acquisition footprint generally consists of modulations in recorded amplitudes that are spatially correlated to the surface locations of sources and receivers used in a survey. These amplitude variations obscure the true reflection response of the subsurface. In this study, synthetic seismic data were produced using numerical modelling code written in MATLAB. An "exhaustive" dataset was created using a survey design incorporating dense grids of sources and receivers, chosen to guarantee fully unaliased sampling of the seismic reflections. A more commonly used survey design, involving sparser spatial sampling and resulting in forms of spatial aliasing, was created by selecting specific traces from the exhaustive survey. Both datasets were subjected to two distinct processing flows: one including stacking and poststack migration, and the other involving prestack migration. Final processed images from the exhaustive dataset were compared to those from the decimated dataset. Algorithm-dependent footprint, including edge artefacts and aperture imprints, was observed in both the exhaustive and decimated datasets. Footprint consisting of periodic amplitude variations in the interior of the surveys, similar to that observed in field data and likely produced by poor sampling, was observed in the decimated dataset. This type of footprint was also observed to vary in strength between images produced with different processing algorithms. Percent amplitude variations of up to 6% in stacks and poststack migrations, and up to 24% in prestack migrations were produced.
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