Seismic modeling and imaging algorithms are embarrassingly parallel. However, there are great strides that need to be made in algorithmic efficiency and data passing. Problems of interest will run several months on the worlds largest computers. More important, there are fundamental problems in 3-D algorithm development, particularly in the area of model perturbations, or what some folks call invariant embedding. Here, we would like to construct hybrid algorithms that might achieve speed improvements of 10-100 fold.
Seismic migration is the adjoint operation of seismic modeling.
In this application we will iteratively model and migrate 3-D seismic data sets using a conjugate gradient solver, thereby obtaining superior images when we try to migrate insufficiently sampled seismic data. Again, the algorithms are not overly complex, with Marfurt again having roughed out a 2-D algorithm in the past. The third need is in the area of seismic interpretation and visualization. However, seismic data processors generate multiple cubes of data derived from seismic data, including amplitude, amplitude variation with offset, tuning frequency, coherence, reflector dip and azimuth, attenuation, interval velocities and pore pressures. While color and opacity can be used to represent 3 or 4 of these attributes, we need to develop more sophisticated means to interact with more than 3, perhaps based on principal components, neural nets, or feature extraction.