Forming a subsurface image from recorded seismic data has truly become a mathematical physicist’s delight. Today, virtually every seismic imaging module is based on someone’s favorite very complex coupled first-order or second-order partial-differential equation or equations. To the not-so-mathematically inclined, the algorithmic hierarchy is a bewildering diagram of confusing labels, terminology and, worse, complicated underlying explanations.

Therefore, it might be surprising to current practitioners of the art to learn that this was not always the case. Seismic imaging has progressed from some simple geometric principles (things the Greeks were well aware of) to the more complex formulations we understand today. The reason this was possible is that the same principles underlie today’s complex equations some of us seem to love. It seems reasonable to decide that if the simple concepts worked well in the past, they might be sufficient to explain the more complicated approach of the modern era.

What I try to do in this short presentation is use this approach to generate some insight into what we do today along with what works, why we need it and why it sometime is not as successful as we would like. I end with a recipe for what seismic imagers will be trying to do in the future, and explain why computers have become such an integral part of the search for subsurface hydrocarbons. Along the way I hope to at least attempt to shed some light on available algorithms, how acquisition affects output results why some algorithms are very sensitive to incorrect velocities, as well as how we usually get the velocities in the first place. However, I promise not to use any equations more complex than what your favorite Greek might put in a Trojan horse.

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