HEMNO is well-suited to image multiples with the data geometry described above, primarily because HEMNO images pegleg multiples with a vertical time shift. Rather than correlating wavefields across possibly-undersampled axes like migration, HEMNO uses a measurement of the data's zero-offset time dip to account for structure-induced moveout variations. Because the crossline offset axis is removed, the computational cost increase of applying HEMNO to 3-D data versus 2-D data is only proportional to the number of crossline midpoints.
My particular LSJIMP implementation, presented in Section , uses HEMNO, in conjunction with three amplitude normalization operators to produce a ``true amplitude'' image of pegleg multiples. From Figure , recall that Snell Resampling moves multiple energy across offset to make the multiple's AVO response comparible with its primary. For this reason, the use of Snell Resampling in the crossline direction runs contrary to the stated assumption that we store only one crossline offset bin per 3-D CMP gather. Therefore, in this thesis, I do not apply crossline Snell Resampling for narrow-azimuth data. In practice, little useful angular information is anyway obtained in the crossline direction, since in most cases the data will have a maximum crossline offset of merely a few hundred meters.