Welcome to a before and after coherent noise removal series. There is a lot to explain about what is going on here, so I am using this otherwise wasted space to do that, and to pass on a few of the things I am continually learning. Of course I first expect you to pay attention to the differences between the two that I point out. To begin, this gather data is from the Permian Basin and the deep mute you are seeing was required because early critical angle crossings pretty much clobbered the outside traces. You have to take my word for that now, but it does not affect what you are seeing

Interbed multiples (and other coherent noise) are a fact of life, and because they are finely interspersed with the signal, problems are created that conflict with some current industry conceptions. I point you to a major one below. Note that removing the noise has completely changed the relative amplitudes between offsets. Obviously this would affect any AVO work, and again I say such noise is the norm and not the exception. So stop and consider how this phenomenon affects industry conceptions.

This type of noise removal depends on the ability to spot event lineups that don’t agree with the moveout we expect under this velocity assumption. The ideal mode is to work with raw gathers before NMO. Unfortunately I did not have that option here. In any case see this change. Notice once again that the supposed broadening of the wavelet with offset (to the right) disappears with noise removal We keep on learning things by watching the actual data.

Repetition can prove the point. I could show you all the examples the system automatically recorded for this in-line but hopefully the points can be made with just a few. However I will go on with additional comparisons until I have laid out the principles that inspired this presentation. In other shows I have come down hard on the fact that the average wavelet shape changes between offsets, meaning we are stacking apples with oranges even when noise has been removed. Of course this means inversion should be done before the stack.

Continuing the inversion before stack discussion – Whatever method used, computing reflection coefficients accurately depends on the ability to approximate effective wavelet shape statistically, so the question was could the system do this for the individual depth point?. And now I know the answer is yes! The ADAPS system iterates through a series of wavelet guesses until it finds the best one possible. In this new approach I collected all the traces by offset, and gave the system this matrix to work with for each particular depth point/offset trace.

Continuing the inversion before stack discussion – 2. In other words I looped through the pertinent offsets, inverting the entire in-line using only the assigned offset traces. I then simply stacked the results to get my final answer. The amazing (and pleasing) thing was that the results for each offset were about as good as my previous “after stack” inversion. Of course the combination of all is the best, as you will see in the last series.

But before we go there let’s look at a few more. Notice on this circled section that there is a complete change of polarity The explanation I was given for this had to do with the AVO assumption. As you can see below, that was not true. How important is that? I keep seeing places where the system could possibly do better. Of course that is the role of the non-linear driving logic, and it is what keeps the whole game fascinating.

Obviously this is an abbreviated presentation. I have the full set of course, and if you want more detail let me know at Comments would be Now on to my (sonic log simulation) inversions of: A. The client’s original gathers with no noise removal. B. After stack of noise removed gathers. C. My combination of pre-stack individual offset results.

Normal stack Inversion with no noise removal. While surprisingly good in light of the strong multiple content (due to the innate strength of the stack), the target (the red event just below one second) was not clear enough to make seismic useable. Since this last series is a main point of the show, please take time to toggle (using one finger on left arrow, and the other on the right arrow) as you compare the results Toggle with noise removed.

After noise removal. Here the target is much clearer, and fault detail is greatly improved. Toggle with original Toggle with combo of individual offset inversions.. In this display, the amplification depends on the strongest events encountered. Greatly improved shallow resolution affects deeper events here, and although their resolution is improved from the original, they look weaker.The same thing is true on the final results, but the resolution on the deep stuff is even better there.

Toggle with original. Toggle with after stack inversion of noise removed gathers.. Combo of prestack results. Obviously the best of the three, my concept of pre-stack inversions is pretty well proven. The difference in approach utilizes the principle that wavelet shape depends on offset. The fact that the individual interface results track so closely is the key. Improvement should be better when we are able to get farther into the spread, since the inversion removes the wavelet shape influence. The fact that our red target still fades to the right may be due to the reservoir being partially depleted (changing the reflection coefficients). If true, this observation could be of use in maintence work.

Normal stack Inversion with no noise removal. While surprisingly good in light of the strong multiple content, the target area just below one second was not clear enough. Again, note that poorer resolution on the shallow stuff makes deeper events look stronger when they are compared with other finals. So observe the clarity. Click to start over Toggle back with final.