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Spatial aliasing and 3-D bin size

The quest for cleaner, cheaper data

3D Imaging Technology Corporation, Denver, Colorado

Corresponding author: barryn@rmi.com

The first 20% of the full text of this article appears below.

Nearly all 3-D data sets that I see are badly aliased, which wreaks havoc on the data quality. But there is a simple solution.

Most of us understand that bin-size selection is based on the simple formula

which defines the maximum frequency that events can contain with integrity for a given dip. Another way of looking at it is that the signal aliases when the time shift between traces exceeds half a cycle. Bin size needs to be small enough to image structural dips without aliasing the highest frequency required to solve the geologic problem. But because bin-size selection is very much impacted by economics, it is often severely compromised. In basin areas, where reservoir definition is required, facies changes and faults should be considered as dipping signal when estimating bin-size requirements. Figure 1 shows the relationship among dip, aliasing frequency, and bin size. Note in the aliasing formula that the aliasing frequency is directly proportional to the section velocity. This helps in the Rockies by raising the aliasing frequency, but it hurts in the Gulf Coast and California or other low-velocity tertiary basins. It should be clear that bin size in no way limits the dip of the data being recorded, only the bandwidth of dipping events. The only design parameter that limits dip imaging is insufficient survey size, which restricts the migration aperture. Despite all the myths, design criteria affecting offset range, fold, and azimuth distribution have no primary effect on dip response.

View larger version (31K): [in this window] [in a new window]   Figure 1. Aliasing frequency curves for a section velocity of 10 000 ft/s.