1. shale Lime Sand The argument for non-linear methods. 1 The geology 2. The reflection coefficients (spikes in non-linear lingo). 3. The down wave 4. Its direction 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. You have already seen the above as it was the come-on to get you here. When you click I will start a short conversation - Ok – I use this oval to emphasize that this is what initial seismic processing should be all about. Once we have effectively computed the reflection coefficient spikes we have risen seismic to the well log level. This is where my non-linear inversion takes us, and linear mathematics does not get us here. The point is that we are looking at a completely different computational world, not just a comparison of coding. I am keeping this show short because this single point is so important.

2. The true power of statistical optimization was unknown to me until I started pushing it hard. In my original predictive deconvolution I had used the autocorrelation extensively to get a guess at wave shape. Then, one day, working on my new inversion, I decided to see if I could improve my initial wavelet guess. The fresh idea here was to use it to make a convolution pass, record the spike guess timings and go back to use this information to compute a new wavelet. It soon became evident that each new wavelet explained the trace energy better than the last. Thousands of hours later the system was showing me the statistical power was there. My philosophy was to ignore the time it took and just concentrate on how deep I could go. I must say I was continually surprised, (These are the kinds of things one can do when one does not answer to anyone else). Of course I evolved tests that allowed the system to exit the loop if the improvement was not significant The operating theory of my inversion is to keep making wavelet and spike position guesses until the original trace energy is explained to the limit of system ability. The logic consists of 3 layers of iteration. The first (open ended) level loops through consecutive wavelet guess runs, starting with an initial wavelet that is computed using autocorrelation like logic The 2 nd layered level loops through the selected set of stacked traces, and the 3 rd through the per/trace optimization. Here, at the third level, is where the coefficients are calculated (the same waveform being used for all). The system subtracts the pertinent energy (associated with each spike) from the current working trace. If, at the end of the major loop no improvement has been made, the system exits this phase of the operation. If improvement has been made, a new wavelet (see below) is computed, and the original, untouched trace is loaded back into the work area. This “return to the original data” keeps the system entirely honest about what it is doing. To compute the new wavelet for the next major pass, the system moves through the previous spike guesses, adding data from their effective spans to a summation vector.It then formalizes the new wavelet from this vector. Each guess is displayed during the run, and watching the shape develop is an education in itself. Obviously I developed some driving logic tricks to push the convergence but the system is as honest as it can get. It can display the spiked output, and if you understand what you are looking at, it is impressive. I soon learned however that potential users did not like the complexity of multiple interface interpretation so I went to the integration and soon became convinced that it in itself was a major contribution. It still puzzles me that this truth does not seem to excite interpreters, but I feel the same on the other major points. This is what I mean by getting the best answer possible. Where formal mathematics have to give up, I can still get a fair set of spikes. This can be looked at as the ability to shift the boundary error to one that statistics can handle. The reason non –linear approaches like mine can beat the frequency/phase methods is they have the freedom needed to truly harness the amazing power of statistical optimization. The ability to build the required logic into formulae that can be proven mathematically is flashy, but solving the wavelet shape problem is the important key. Obviously the logic still has to be mathematically sound, but perhaps there is a higher level that more effectively combines statistics into the fold. And now for a look at my compendium of non-linear subjects.

3. Introduction – take a minute to see where I am coming from and why this might be worth your time. Then look at some great well log matches to see the merits of non-linear optimization. Or some seismic basics all interpreters should be aware of. And now look at some sources of seismic noise And a quick look at refractions spawned by critical angle crossing. Now to the results of noise removal on a deep South Louisiana project. Or back up to look at the system in action on this last one. Sit back and watch the timed slides. Or here to the results from seemingly hopeless Permian basin data. Or here to still another example of down wave truncation. Or here to where I first identified strike slip faulting on a North Sea project. Or here to a Gulf Coast strike slip fault example. Or here where I discuss direct reservoir detection. Or here for a different discussion of intertwined signal and noise. Or here for a different twist on why ignored noise saved prospects for newcomers. Or here for a more complete noise primer. Or here for a comprehensive look at my inversion. Or here for another look at well log matches. Or here for a fairly sarcastic look at near/middle/far stack options and a wrap-up. This is the router in Paige ’ s set of non-linear seismic thoughts. If you were there, browsing through would be super fast and simple. To get there, see next slide.

4. I have spent a good bit of time collecting PowerPoints into a folder, which I have sent to my FTP site. If you are interested, You have to do the following to access the work. 1. Enter " adaps.exavault.com " in your browser and go there. The username is adaps and the password is adaps1.. 2. Select the folder PN and "download all". It sends a zipped file. Create a new folder on your PC named PN, unzip and load the two files (shows and base.ppsx) into PN. 3 access "base.ppsx" and you will get the router which will lead you to all the others. This eliminates the load time problem. At this fairly late time I have no idea if anyone is accessing the shows the way I had hoped. Just going into the list blindly is not as productive, since the menu puts them into a more reasonable form. Communication can be lonely. Thanks in advance Dave Paige