Systems and methods for training a model that refines estimated parameter values include computer processors and non-transitory electronic storage that stores subsurface map data sets that correspond to different subsurface volumes of interest, the system configured to obtain training data including unrefined subsurface map data sets specifying estimated parameter values of a first parameter as a function of position within corresponding subsurface volumes of interest, obtain an initial seismic mapping model, generate a conditioned seismic mapping model, and store the conditioned seismic mapping model in the electronic storage.

Prestack images from the object store are hierarchically combined to generate a hierarchically stacked image. The hierarchically stacked image is generated by combining stacked images that includes a stacked image. The stacked image is generated by combining at least the prestack images. Based at least on the hierarchically stacked image, a quality measure of a prestack image is generated. Prior to deleting at least a subset of the prestack images from the object store and based at least on the quality measure, the prestack images are further combined to generate an enhanced stacked image. The stacked image is substituted using the enhanced stacked image. Subsequent to the substituting and prior to deleting at least the subset of the stacked images from the object store, the stacked images are combined to generate an enhanced hierarchically stacked image. The enhanced stacked image and the enhanced hierarchically stacked image are generated using failure recovery metadata. The enhanced hierarchically stacked image is presented.

A method for generating a seismic image representing a subsurface includes receiving seismic data for the subsurface formation, including receiver wavelet data and source wavelet data. Source wavefield data are generated based on a forward modeling of the source wavelet data. Receiver wavefield data are generated that compensate for distortions in the seismic data by: applying a dispersion-only model to the receiver wavelet data to generate a first reconstructed back-propagated receiver wavefield portion, applying a dissipation-only model to the receiver wavelet data to generate a second reconstructed back-propagated receiver wavefield portion, and combining the first back-propagated receiver wavefield portion and the second back-propagated receiver wavefield portion into the receiver wavefield data. The method includes applying an imaging condition to the receiver wavefield data and the source wavefield data and generating, based on applying the imaging condition, visco-acoustic reverse time migration (VARTM) result data.

One method interpolates simulated seismic data of a coarse spatial sampling to a finer spatial sampling using a neural network. The neural network is previously trained using a set of simulated seismic data with the finer spatial sampling and a subset thereof with the coarse spatial sampling. The data is simulated using an image of the explored underground formation generated using real seismic data. The seismic dataset resulting from simulation and interpolation is used for denoising the seismic data acquired over the underground formation. Another method demigrates seismic data at a sparse density and then increases density by interpolating traces using a neural network.

A method of analysing seismic data to detect possible hydrocarbons includes determining a set of data tiles from a seismic data cube of seismic data and testing each data tile in the set of data tiles to determine whether it corresponds to a possible fluid contact.

Estimation of velocity models inclusive of receiving seismic data inclusive of data that corresponds to a seismic image, adding a velocity perturbation to a current velocity model that represents a portion of the subsurface responsible for a distortion in the seismic image to generate a perturbed velocity model, generating an image via seismic migration of the seismic data and the perturbed velocity model, generating and assigning a measure of quality to the image, determining whether the measure of quality assigned to the image is an optimal measure of quality at a particular location of the current velocity model, and updating the current velocity model to generate a revised velocity model utilizing the measure of quality assigned to the image when the measure of quality assigned to the image is determined to be the optimal measure of quality at the particular location of the current velocity model.

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).

Systems and methods that include receiving reservoir data of a hydrocarbon reservoir, receive an indication related to selection of a wavefield propagator, application of the wavefield propagator utilizing Fourier Finite Transforms and Finite Differences to model a wavefield associated with a Tilted Orthorhombic media representative of a region of a subsurface comprising the hydrocarbon reservoir, and processing the reservoir data in conjunction the wavefield propagator to generate an output for use with seismic exploration above a region of a subsurface comprising the hydrocarbon reservoir and containing structural or stratigraphic features conducive to a presence, migration, or accumulation of hydrocarbons.

A process for seismic imaging of a subterranean geological formation includes generating a source wavefield from seismic data representing a subterranean formation. The process includes generating a receiver wavefield from the seismic data representing the subterranean formation. The process includes decomposing the source wavefield to extract a source depth component and decomposing the receiver wavefield to extract a receiver depth component. The process includes applying a transform to each of the source depth component and the receiver depth component. The process includes combining the source depth component and the receiver depth component to generate an imaging condition. The process includes extracting a low-frequency term from the imaging condition to generate a wave-path tracking data, generating a wave path from the wave-path tracking data, and rendering a seismic image of at least a portion of the subterranean geological formation from the generated wave path.

The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for suppressing noises in seismic data. One computer-implemented method includes receiving, at a data processing apparatus, a set of seismic data associated with a subsurface region; flattening, by the data processing apparatus, the set of seismic data according to an identified seismic event; dividing, by the data processing apparatus, the set of seismic data into a plurality of spatial windows; randomizing, by the data processing apparatus, the set of seismic data according to a random sequential order; filtering, by the data processing apparatus, the randomized seismic data; and reorganizing, by the data processing apparatus, the filtered seismic data according to a pre-randomization order.