The downward-continuation result is significantly better than the NMO result, but it does contain some suspicious reflections (boxed). My final effort, shown on the right, includes the idea that the data contains random noise which could be windowed away in velocity space. To understand how this was done, recall that the basic model is , where is the left panel, are constants determined by least squares, and are the regressors, which are panels like but delayed and diffracted. Let denote an operator that transforms to velocity space. Instead of solving the regression ,I solved the regression and used the resulting values of in the original (t,x)-space. (Mathematically, I did the same thing when making Figure .) This procedure offers the possible advantage that a weighting function can be used in the velocity space. Applying all these ideas, we see that a reflector remains which looks more like a multiple than a primary.
|A regression () can be done in any space. You must be able to transfer into that space (that is, to make and ) but you do not need to be able to transform back from that space (you do not need ). You should find the in whatever space you are able to define the most meaningful weighting function.|
A proper ``industrial strength'' attack on multiple reflections involves all the methods discussed above, wave-propagation phenomena described in IEI, and judicious averaging in the space of source and receiver distributions.