Seismic imaging for exploration/monitoring purpose is a large-scale computational problem. First, it involves a large amount of seismic measurements, with 10,000’s of experiments, every experiment shooting in 10,000’s of channels, each channel containing a time-series recording. This adds up to 10’s of Tbyte of data. Next, the imaging process is a computationally heavy process that can easily take weeks to months, especially if run in an inversion mode, where the model parameters (the subsurface reflectivity image and propagation velocity model) should be updated to fit the data. In addition to the computational challenges, there is ill-posedness of the inverse problem. We foresee that AI can assist in bringing in a priori knowledge - such as satellite data and geologic constraints - in a proper way such that the inverse problem is sped up and optimally steered into physically consistent solutions.