Paper Summary

The seismic industry will adopt a diverse range of AI solutions to a diverse range of seismic problems in coming years; both to augment better decision making, and to significantly accelerate the cycle time of seismic projects. Computationally-demanding processes such as velocity model building and seismic imaging are obvious targets for the development of innovative ways to do things much faster. Using the example here of velocity model building, although neural network-based alternatives to full waveform inversion (FWI) have been most popular in the literature, it can also be demonstrated that stochastic modeling methods using highly efficient reflection tomography can delivery accurate results up to two orders of magnitude faster. What is clear is that this arena is in its infancy, and pragmatic implementations will more likely move success from inflated expectations to demonstrable success.