Abstract: A method and system for forming a depth-domain seismic image using a predicted seismic velocity model is disclosed. The method includes obtaining a first surface seismic dataset of a subterranean region and obtaining a measured seismic velocity profile through the subterranean region for a surface location. The method also includes determining a set of seismic attributes for the surface location from the first surface seismic dataset, and training, using the measured seismic velocity profile and the set of seismic attributes, a machine-learning network to predict a predicted seismic velocity profile from the set of seismic attributes, wherein the predicted seismic velocity profile is an estimate of the measured seismic velocity profile. The method further includes determining a set of seismic attribute volumes from a second surface seismic dataset, and predicting a seismic velocity model for the subterranean region, using a trained machine-learning network and the set of seismic attribute volumes.

Abstract: A method of processing seismic data representing a physical system, the method comprising a difference between first and second seismic data representing the system in first and second states, respectively, and inverting the difference in accordance with a parameterized model of the physical system to obtain changes in the parameters of the model.

Abstract: A method of processing seismic data representing a physical system, the method comprising a difference between first and second seismic data representing the system in first and second states, respectively, and inverting the difference in accordance with a parameterised model of the physical system to obtain changes in the parameters of the model.