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  
\[\mathrm{aliasing\ frequency\ =\ velocity/(4\ {\times}\ bin\ size\ {\times}\ sin\ dip)}\]
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...
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