A mixing board for the seismic symphony

Seismic processing is busy chasing its tail. OK, maybe an over-generalization, but researchers in the field are very skilled at finding incremental—and sometimes great—improvements in imaging algorithms, geometric corrections, and fidelity. But I don't want any of these things. Or, to be more precise: I don't need any more. 

Reflection seismic data are infested with filters. We don't know what most of these filters look like, and we've trained ourselves to accept and ignore them. We filter out the filters with our intuition. And you know where intuition gets us.

If I don't want reverse-time, curved-ray migration, or 7-dimensional interpolation, what do I want? Easy: I want to see the filters. I want them perturbed and examined and exposed. Instead of soaking up whatever is left of Moore's Law with cluster-hogging precision, I would prefer to see more of the imprecise stuff. I think we've pushed the precision envelope to somewhere beyond the net uncertainty of our subsurface data, so that quality and sharpness of the seismic image is not, in most cases, the weak point of an integrated interpretation.

So I don't want any more processing products. I want a mixing board for seismic data.

To fully appreciate my point of view, you need to have experienced a large seismic processing project. It's hard enough to process seismic, but if there is enough at stake—traces, deadlines, decisions, or just money—then it is almost impossible to iterate the solution. This is rather ironic, and unfortunate. Every decision, from migration aperture to anisotropic parameters, is considered, tested, and made... and then left behind, never to be revisited.

But this linear model, in which each decision is cemented onto the ones before it, seems unlikely to land on the optimal solution. Our fateful string of choices may lead us to a lovely spot, with a picnic area and clean toilets, but the chances that it is the global maximum, which might lie in a distant corner of the solution space, seem slim. What if the spherical divergence was off? Perhaps we should have interpolated to a regularized geometry. Did we leave some ground roll in the data? 

Look, I don't know the answer. But I know what it would look like. Instead of spending three months generating the best-ever migration, we'd spend three months (maybe less) generating a universe of good-enough migrations. Then I could sit at my desk and—at least with first order precision—change the spherical divergence, or see if less aggressive noise attenuation helps. A different migration algorithm, perhaps. Maybe my multiples weren't gone after all: more radon!

Instead of looking along the tunnel of the processing flow, I want the bird's eye view of all the possiblities. 

If this sounds impossible, that's because it is impossible, with today's approach: process in full, then view. Why not just do this swath? Ray trace on the graphics card. Do everything in memory and make me buy 256GB of RAM. The Magic Earth mentality of 2001—remember that?

Am I wrong? Maybe we're not even close to good-enough, and we should continue honing, at all costs. But what if the gains to be made in exploring the solution space are bigger than whatever is left for image quality?

I think I can see another local maximum just over there...

Mixing board image: iStockphoto.