The diplopia is a pathological vision condition, in which two images of a single object are seen: also called double vision, it can be temporarily caused by alcohol intoxication, head injuries, tiredness of the eye muscles. Our brain receives two superimposed images, shifted or rotated. Identifying the position of objects becomes very difficult: I imagine how hard it can be driving with diplopia on a busy road of Jakarta.
The seismic vision is complicated: the final image is built summing the information coming from many different eyes, summing the images seen under different angles by many receivers at different locations. If these individual images are not correctly superimposed, the resulting image is not constructed at all, or it can be severely distorted, mispositioned, blurred.
Besides all the imaging of the subsurface complexity, in land data, the near-surface lateral variations of thickness, velocity, absorption can produce large distortions of the seismic reflections, even for a simple ‘layered’ subsurface. Different positions at short distance have different shallow velocities, and the last and first shallow part of the path can be heavily affected by the near-surface properties.
Even a very simple subsurface can be difficult to image: like looking into the earth through a broken glass.
You might have recognised that these images come from a slice of pharaonic alabaster. And might have guessed that I am going back to seismic: land seismic, to be precise. When you are onshore, sources and receivers at the surface send and receive energy though the near-surface, which adds complexity to the ray-path. The problem is that part of the path is too thin to be identified by reflection, but complex enough to distort the image.
So, what happens in land seismic? Using the layered flattish alabaster section, the low-relief structure that is shown in the previous images, I created seismic data with near-surface perturbations, with statics (yes, surface-consistent statics). In the image below, I assume that all the imaging is perfect: the only problem comes from uncorrected statics, and only on the right half of the image. If you are into seismic processing, you can appreciate the gathers on both sides.
This type of perturbations is considered short wavelength, it reduces the resolution but doesn’t change the overall shape of the identified subsurface. But there is another type of problem, the long-wavelength distortions. If the velocity anomalies are spatially large, they can create pull-ups or push-downs, and create deformations of the image that are more dangerous, since they can create fake structures.
You can see two humps where there’s only one hump (like mistaking a dromedary for a camel). Or you can see a hump where there’s no hump at all: and then drill a static anomaly.
So, correcting the statics is very important: it’s putting your eyeglasses on when it gets blurry (and this explains the post’s title photo). When I am tempted to take shortcuts on the statics, I imagine myself driving with double vision on a busy road in Jakarta.