With the pyramid transform, 2-D dip spectra can be characterized by
1-D prediction-error filters (pefs) and 3-D dip spectra by 2-D
pefs. This transform takes data from
-space to data in
-space using a simple mapping procedure that leaves
empty locations in the pyramid domain. Missing data in
-space
create even more empty bins in
-space.
We propose a multi-stage least-squares approach where both
unknown pefs and missing data are estimated. This approach is tested
on synthetic and field data examples where aliasing and irregular
spacing are present.