1. Think stratigraphy, This picture, all by itself, not only proves strike slip theory, but also verifies the logic used to bring out the fault detail. This is North Sea data and to my knowledge, the operator still has no idea this structural relationship exists.
Stratigraphy to the left of the main green fault fits together, as does that to the right. However there’s a drastic difference between the two.
The obvious fact is we are seeing two locations (originally far enough apart to make such a big difference geologically reasonable).
Take your time - but remember: if true here it will be true all over.

2. Back to justification for “before stack” inversion and integration.
The justification for doing “before stack” inversion & integration is the drastic down-wave shape differences between stations.
But before going on. let’s examine what the data shows us.:
On the globe, we see the South American coastline duplicated on the African side, with the Atlantic ocean in between.
We might explain what we see here two ways – either saying the right hand block was pulled to the left, or that the left hand block was pulled to the right. While plate theorists can play with this, it is immaterial to our thesis. For us it is enough to say that lateral shift is here, and it can be huge. If you study the exquisite detail here I would hope you will agree that this slide proves the strike slip thesis.
Back to justification for “before stack” inversion and integration.
Primary reflections come from individual interfaces (like bed tops and bottoms). Because of the two way depth point geometry there are significant differences in the traveled distance between individual gather traces, Earth filtering (creation of successive side lobes) is tied to distance traveled. Events do not mix until they reach the receiver, where all overlapping primaries are composited. In other words, the phase damage is done before anything is recorded, Thus, contrary to original thinking, the normal stack is adding a fairly wide variety of event shapes, and this requires inversion resolution before the distorting addition occurs.

3. Basic reflection theory that we should all keep in mind.
The seismic energy continuum consists of thousands of independent primary reflections, each coming from it’s own reflecting interface.
There is no mixing of primaries in the subsurface.
Geophones can only record the energy that exists at instants of time, and this forces an accidental form of compositing at the recording point.
The earth filter generates trailing lobes with travel, gradually emphasizing lower frequencies. The total travel difference from inner to outer station is great and by the time the energy reaches the recording points there will be significant differences in primary wave shapes. This (mostly ignored) problem is exacerbated by excessively long spreads. It heavily affects gather trace character, probably dwarfing any possible AVO effect.
The separation between primaries changes with offset, modifying the way they combine at the recording point, again affecting gather trace character. As with the earth filter, it happens before any processing can be done. In summary, the stack will produce an almost accidental waveform mixture.
The above 5 points are demonstratable facts, not just my opinion. It appears to me that they have largely been ignored by current frequency domain developers. Their effects contribute to the difficulty of determining a usable average waveform (the weakest link in their logic). Of course my own system faces the same set of problems, but using advanced pattern recognition to establish reflection coefficient spikes allows it to get the best answers possible. So my after stack inversion provides a safer and arguably better route.
However it was clear that inversion should ideally be done before stack. So that is what I did, and this show is mostly about comparing the before and after results.
So we start with these comparisons. The arrows have been located at specific times to help you keep the correlation straight. Because my personal goal was to improve the resolution enough to facilitate picking the strike slip faults I knew were there (in this North Sea data), I have added a final section for that subject.

4. Toggle Normal inversion, no noise removal – arrows show fxd times.
To understand the processing, it is
Almost necessary to toggle between
The indicated slides.The purpose of.
the arrows is to tie locations for the
Sake of comparison.
Toggle

5. Toggle Noise removed, before stack inversion – (note positions).
The seismic resolution has been greatly
Improved by focusing the inversion and
integration on the offset. Notice how the
bed thicknesses have been established.
Toggle

6. Another line – normal inversion with noise removed

7. Before stack inversion, noise removed. Note thickness.
Toggle

8. Before stack inversion, noise removed – Pick section.
The area if interest

9. Expanded Excerpt - before stack inversion with noise removal.
Use this slide to toggle with the faulted version, to check evidence. But first remember that strike slip faults may not exhibit vertical throw, so abrupt changes in character may be all we have to go on.

11. And here we are back at the final composite of the “before stack” results.

12. Proof of the importance of wavelet shape focus –
Because it is so important (and generally ignored) I repeat the down-wave dialogue.
Primary reflections come from individual interfaces (like bed tops and bottoms). Because of the two way depth point geometry there are significant differences in the traveled distance between individual gather traces, Earth filtering (creation of successive side lobes) is tied to distance traveled. Events do not mix until they reach the receiver, where all overlapping primaries are composited. In other words, the phase damage is done before anything is recorded, Thus, contrary to original thinking, the normal stack is adding a fairly wide variety of event shapes, and this requires inversion resolution before the distorting addition occurs.
Proof of the importance of wavelet shape focus –
Improving the attribute resolution by taking spread geometry into consideration has so increased seismic resolution that we find inversions of individual channel data almost beating previous full “after stack” results.
On the left, the non-linear inversion logic was applied to the matrix of all stacked traces. On the right it was applied to the set of gather traces that were collected on single channel E.

13. Because noise removal is central to my later work, I go on with a discussion on how intertwined coherent noise creates a random effect that confuses all frequency domain calculations and generally screws up the works – Pay close attention here, since this is a vital point.
To the left we have a raw gather and to the right the same data with noise lifted.
The fact is such noise is present on every prospect, to some degree. The problem is seeing it! To do so requires intense pattern searching on the gathers, and this is not normally done. When we add the factors I outline next, such things as AVO claims and frequency domain waveform generation become questionable, and the need for non-linear approaches becomes apparent.

14. For the reasons listed below the final stack is adding different waveform shapes. Thus the need to invert before this distortion happens.
The seismic energy continuum consists of thousands of independent primary reflections, each coming from a single reflecting interface.
There is no mixing of primaries in the subsurface.
Geophones can only record the energy that exists at instants of time, and this forces an accidental form of compositing at the recording point. In essence this is a preliminary stack, and we have no control over it.
The earth filter generates trailing lobes with travel, gradually emphasizing lower frequencies. The total travel difference from inner to outer station is great and by the time the energy reaches the recording points there will be significant differences in primary wave shapes. This (mostly ignored) problem is exacerbated by excessively long spreads. It heavily affects gather trace character, probably dwarfing any possible AVO effect.
The separation between primaries changes with offset, modifying the way they combine at the recording point, again affecting gather trace character. As with the earth filter, it happens before any processing can be done. In summary, the final stack is an almost accidental waveform mixture.
Some thoughts on frequency oriented inversions - The frequency domains were essentially invented mathematically to make solutions by equations possible. The new tool was the transform, which models what happens in the time domain into this new form. This conversion enabled the designer to invoke equations to generate filters that change the spectrum of the data to equal a desired one. This was as far as their early deconvolutions went. Of course these efforts were attempts at inversion. They just had to be limited to keep the processes stable.
The problem with going farther is that any particular spectrum can represent a variety of wave shapes. Later phase work has concentrated on determining that shape. This is the weak link in their process. When noise is present it gets much harder. My non-linear approach by-passes this problem by determining spike location via pattern recognition. The great well log matches I have shown pretty well prove the validity of this approach.
I continue with a graphic that tries to put the basic seismic problem into perspective.

15. The argument for non-linear methods.
Computing reflection coefficient spikes via statistical optimization eliminates frequency and phase from the picture.
The crazy down-wave at the left is just there to show the nodular character that evolves with depth. The “shape” of the primary reflections at each offset, as well as the offsets between them, will depend on the total distance traveled. They are stacked at the receiver location, and the resulting trace character will vary greatly between offsets.
Rigid, mathematical solutions of complex problems like this are extremely tough. Optimization is typically the answer when coming up with the best answer possible is what we want.
1 The geology
3. The down wave Its direction
2. The reflection coefficients (spikes in non-linear lingo).
shale
Lime
Sand
shale
The argument for non-linear methods.
Because linear inversions are not able to compute reflection coefficients without knowing the wave shape, the industry has become obsessed with that problem, with opinions coming from everywhere.
I use the oval to emphasize that this is what inversion should be doing. Once we have effectively computed the reflection coefficient spikes we have raised seismic to the well log level. This is where my non-linear inversion takes us, avoiding the exact wave shape hurdle by calculating spike position via pattern recognition. We’re looking at completely different approaches, and the way statistical optimization can handle error is the key to being able to get answers under difficult circumstances. Again, the proof lies in the well log match.

16. Processes should be judged by results - One could spend hours studying what there is to learn from this display of a “simulated sonic log” section. First, notice how bed thicknesses make the “stratigraphic differences” across the strike slip faults very evident. To me, as an interpreter, this is vital.
When I re-look at this picture I shake my head and wonder why almost no interest has been shown. Nobody had noticed them before I made this run and I guess they will continue to be ignored.
Strike slip faults are a result of shallower beds being torn apart as a result of deep plate movement (continental drift). They are a given fact of geological life. The reason they have been missed by the industry is that they often are very hard to see. Where the stratigraphy is regionally regular, no vertical throw may result, making patterns very hard to establish. When one can’t see a pattern between the bursts of energy, it is normal to assume we are looking at noise.
Each fault block in this pattern exhibits stratigraphic consistency within itself. Before the pattern was set it looked like an unconnected hodgepodge. Much of my later efforts to improve resolution were prompted by the need to see the fault patterns I was sure existed. My time domain inversion and subsequent sonic log simulation are vital parts of this improvement, and that Is why I place this subject first on the list.
Next you might observe that the changes in amplitude now seem to make structural trapping sense. This ties into my claim that direct reservoir spotting is now a good possibility!
First time In skim to see all subjects covered. For more on strike slip faults, come back & click on oval -

17. Raw seismic sections are “coincidental” mixtures of primary reflections.
The amplitudes and polarities of stacked events depends on how the primary reflections were aligned when they reached the recorder.
These alignments depend on effective velocities (a function of distance from source to receiver). Earth filtering continually produces trailing side lobes with distance traveled. Because of large differences in recording travel time, these character changes can be significant. Of course they occur before any processing is possible.
In this mix, the strong will prevail. The amplitude of each primary reflection is a function of both the velocity of the previous layer, and that of the current one. Thus the lower interface between a sand and a limestone will generate a strong event,making the sand look like a lime, where that between a sand and a shale will be weaker. Of course the same is true on the upper interface. The point here is that trusting the amplitude of any stacked event before inverted data is integrated can be specious, at best. In my own development work I was continually surprised (and pleased) at how the integrated results matched with available well logs. Not perfect, but certainly a big improvement.
The simulated sonic log section at the left is a good example of where integration after inversion made good sense out of amplitudes. The well match on this strong red event matched beautifully, to the point we could have predicted success if the run had been made before drilling.
Unfortunately, due to me being isolated from the Ikon client, no one ever saw these visual results.
For more on simulation, click oval -

18. If you need to see more, click on the oval -
Stack (input)
Inverted & integrated
This simulated sonic log looks too good to be true (but it is, and all the other ones we have matched at least come close to this quality).
Once again, we inverted down to the reflection coefficient (spike), then integrated the results.
While it looks great to me, many seem to have trouble adjusting to the squarish nature, not realizing that was the goal all along. Those thicknesses are crucial to long range seismic correlations, and to detailed stratigraphy.
The curves are not sinusoids, and frequency analysis does not apply in any sense. What they really are is truth and beauty, thank you.
If you need to see more, click on the oval -

19. Now let’s talk about noise removal – To begin, it is vitally important that we lift it off gently, so as not to disturb the underlying signal we are trying to bring out. Obviously frequency sensitive filtering is a no no. Predicting the individual noise events and computing correlation coefficients is the key. The example comparison below proves that can be done. For more, click on oval -

20. I refer to the fact that all sorts of academic assumptions have been made that ignore the effect of noise inter-twining with signal.
The next series of pictures introduces several shows I believe are important. After moving through the possibilities, come back and click on the image that interests you, giving the PowerPoint plenty of time to load. Since you might have trouble getting back here, it would not hurt to bookmark this file.

21. This show is my latest attempt to explain my work
This show is my latest attempt to explain my work. It uses a set of data that was giving the geophysicist problems. Click on the oval here for the PowerPoint -

22. Click on oval for direct reservoir detection -

24. This south Louisiana work is perhaps the one I am most proud of
This south Louisiana work is perhaps the one I am most proud of. It really was the beginning of my serious noise removal efforts. The flank events butting up against the salt dome did not even show up before the noise was removed. My advanced scanning for noise events was developed here, I was able to track strike slip faults on the
both ends of the dome, virtually proving that they contributed to the actual formation.
I started here with data in the shot point format, with no NMO applied. This allowed a more precise and logical scanning to detect non reflection NMOs.
The system removed so many refraction events (stemming from the central noise cone) that I wondered if there could be any energy left. The results showed that there was.
Click on oval to enter this one.
There are more things to talk about but I will leave it here for now. Click on oval below to go to router.