Abstract: Systems and methods of identifying a drilling target are disclosed. The method includes obtaining a training set of base subsurface models and generating, using a first artificial intelligence neural network, a plurality of subsurface model realizations based on the base subsurface models. The method further includes simulating, for each subsurface model realization, a synthetic seismic dataset and training a second artificial intelligence neural network, using the plurality of subsurface model realizations and the corresponding synthetic seismic dataset for each subsurface model realization, to predict an inferred subsurface model from a seismic dataset. The method still further includes obtaining an observed seismic dataset for a subterranean region of interest, predicting, using the trained second artificial intelligence neural network, an inferred subsurface model from the observed seismic dataset, and identifying the drilling target based on the inferred subsurface model.

Abstract: A method may include obtaining a P-wave velocity model and velocity ratio data regarding a geological region of interest. The method may further include generating, based on the P-wave velocity model and the velocity ratio data, an initial S-wave velocity model regarding the geological region of interest. The method may further include determining various velocity boundaries within the initial S-wave velocity model using a trained model. The method may further include updating the initial S-wave velocity model using the velocity boundaries, an automatically-selected cross-correlation lag value based on various seismic migration gathers, and a migration-velocity analysis to produce an updated S-wave velocity model. The method further includes generating a combined velocity model for the geological region of interest using the updated S-wave velocity model and the P-wave velocity model.

Abstract: A method may include obtaining a P-wave velocity model and velocity ratio data regarding a geological region of interest. The method may further include generating, based on the P-wave velocity model and the velocity ratio data, an initial S-wave velocity model regarding the geological region of interest. The method may further include determining various velocity boundaries within the initial S-wave velocity model using a trained model. The method may further include updating the initial S-wave velocity model using the velocity boundaries, an automatically-selected cross-correlation lag value based on various seismic migration gathers, and a migration-velocity analysis to produce an updated S-wave velocity model. The method further includes generating a combined velocity model for the geological region of interest using the updated S-wave velocity model and the P-wave velocity model.

Abstract: A method may include obtaining an initial velocity model regarding a subterranean formation of interest. The method may further include generating various seismic migration gathers with different cross-correlation lag values based on a migration-velocity analysis and the initial velocity model. The method may further include selecting a predetermined cross-correlation lag value automatically using the seismic migration gathers and based on a predetermined criterion. The method may further include determining various velocity boundaries within the initial velocity model using a trained model, wherein the trained model is trained by human-picked boundary data and augmented boundary data. The method may further include updating, by the computer processor, the initial velocity model using the velocity boundaries, the automatically-selected cross-correlation lag value, and the migration-velocity analysis to produce an updated velocity model.

Abstract: A method may include obtaining an initial velocity model regarding a subterranean formation of interest. The method may further include generating various seismic migration gathers with different cross-correlation lag values based on a migration-velocity analysis and the initial velocity model. The method may further include selecting a predetermined cross-correlation lag value automatically using the seismic migration gathers and based on a predetermined criterion. The method may further include determining various velocity boundaries within the initial velocity model using a trained model, wherein the trained model is trained by human-picked boundary data and augmented boundary data. The method may further include updating, by the computer processor, the initial velocity model using the velocity boundaries, the automatically-selected cross-correlation lag value, and the migration-velocity analysis to produce an updated velocity model.

Abstract: A subsalt imaging tool and seismic imaging process for complex geological environments such as subsalt structures having a rugged seafloor topology are provided. The subsalt imaging tool operates on stacked data as opposed to prestack data and uses a wave equation tomography to iteratively update a velocity model. Improved seismic images that improve the visibility of various events may be produced using the updated velocity model.