A method may include obtaining an input gather regarding a geological region of interest. The method may further include obtaining parameterization data regarding a seismic processing operation. The parameterization data may correspond to a first set of process parameter values that are different from a second set of process parameter values that are used to generate the input gather. The method may further include generating a predicted output gather using a machine-learning model, the input gather, and the parameterization data. The machine-learning model may include an encoder model and a decoder model. The method may further include generating a seismic image of the geological region of interest using the predicted output gather.

The invention relates to a computer-implemented method for generating an image of a subsurface of an area of interest from seismic data. The method comprises providing seismic wavefields, providing a zero-offset seismic wavefield dataset, determining a seismic velocity parameter model w(x) comprising an initial model w.sub.0(x), a low frequency perturbation term δm.sub.b(x) and a high frequency perturbation term δm.sub.r(x), determining an optimal seismic velocity parameter model w.sub.opt(x) by computing a plurality of iterations, each iteration comprising calculating and optimizing a cost function, said cost function being dependent on the zero-offset seismic wavefield and on the low frequency perturbation term δm.sub.b(x) as a parameter in the optimization of the cost function, the high frequency perturbation term δm.sub.r(x) being related to the velocity parameter model w(x) to keep the provided zero-offset seismic wavefield data invariant with respect to the low frequency perturbation term δm.sub.b(x).

A method may include obtaining seismic data regarding a geological region of interest. The seismic data may include various pre-processed gathers. The method may further include obtaining a machine-learning model that is pre-trained to predict migrated seismic data. The method may further include selecting various training gathers based on a portion of the pre-processed gathers, a migration function, and a velocity model. The method may further include generating a trained model using the training gathers, the machine-learning model, and a machine-learning algorithm. The method may further include generating a seismic image of the geological region of interest using the trained model and a remaining portion of the seismic data.

The present disclosure provides a method of high-resolution amplitude-preserving seismic imaging for a subsurface reflectivity model, including: performing reverse time migration (RTM) to obtain an initial imaging result, performing Born forward modeling on the initial imaging result to obtain seismic simulation data, and performing RTM on the seismic simulation data to obtain a second imaging result; performing curvelet transformation on the two imaging results, performing pointwise estimation in a curvelet domain, and using a Wiener solution that matches two curvelet coefficients as a solution of a matched filter; and applying the estimated matched filter to the initial imaging result to obtain a high-resolution amplitude-preserving seismic imaging result.

Seismic exploration of an underground formation uses seismic excitations to probe the formation's properties such as reflectivity that can be imaged using reverse time migration. Using an equal area spherical binning at reflection points improves and simplifies RTM imaging together with adaptability to the data acquisition geometry, while overcoming drawbacks of conventional cylindrical binning.

A computer-implemented method and system implementing the method, are disclosed for computing small cave recognition models, using seismic reflection data. User inputs and earth-model data are obtained over points of incidence of a survey region, at various angles of incidence. Various models are then computed that serve for cave identification and take part in preliminary seismic exploration and reservoir characterization. Therefore, the attributes developed by the computer-implemented method and system serve as indicators of low velocity and density cave recognition which are capable of separating the cave events from the normal layer events; identifying caves with size larger than half to one wavelength of the dominant signal; and identifying cave diffractions from caves that contain a local maximal/minimal at around nine degrees in amplitude versus angle models.

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. The method may further include generating an image of the subterranean formation of interest using the updated velocity model.

There is described embodiments relating to a method of determining a wave field in an anisotropic subsurface of the Earth. The method includes numerically solving a decoupled quasi-acoustic single wave mode wave equation based on spatially varied anisotropic parameters, to determine the wave field in the anisotropic subsurface.

The invention relates to a computer-implemented method for generating an image of a subsurface of an area of interest from seismic data. The method comprises providing seismic wavefields, providing a zero-offset seismic wavefield dataset, determining a seismic velocity parameter model w(x) comprising an initial model w.sub.0(x), a low frequency perturbation term δm.sub.b (x) and a high frequency perturbation term δm.sub.r(x), determining an optimal seismic velocity parameter model w.sub.opt(x) by computing a plurality of iterations, each iteration comprising calculating and optimizing a cost function, said cost function being dependent on the zero-offset seismic wavefield and on the low frequency perturbation term δm.sub.b(x) as a parameter in the optimization of the cost function, the high frequency perturbation term δm.sub.r(x) being related to the velocity parameter model w(x) to keep the provided zero-offset seismic wavefield data invariant with respect to the low frequency perturbation term δm.sub.b(x).

A computer-implemented method and system implementing the method, are disclosed for computing small cave recognition models, using seismic reflection data. User inputs and earth-model data are obtained over points of incidence of a survey region, at various angles of incidence. Various models are then computed that serve for cave identification and take part in preliminary seismic exploration and reservoir characterization. Therefore, the attributes developed by the computer-implemented method and system serve as indicators of low velocity and density cave recognition which are capable of separating the cave events from the normal layer events; identifying caves with size larger than half to one wavelength of the dominant signal; and identifying cave diffractions from caves that contain a local maximal/minimal at around nine degrees in amplitude versus angle models.