The orientation of the symmetry axis of an underground formation including an HTI layer is determined by comparing azimuthal Fourier coefficient of inversion results in distinct source-receiver azimuth ranges with values expected from the HTI assumption. A branch-stacking technique or prior knowledge may be used to select one of the anisotropy axis orientation values.

Seismic survey data is received, indexed into index sets, and each index set partitioned into data blocks. For each particular data block of a particular index set, the particular data block is sliced into frequency slices. For each particular frequency slice of the particular data block, the particular frequency slice is processed to remove random and erratic noise by: forming a Hankel matrix from the particular frequency slice: determining an optimal rank for the Hankel matrix, determining a clean signal and erratic noise from the ranked Hankel matrix, and returning the clean signal and erratic noise for the particular frequency slice. A clean signal is assembled from the index sets.

Methods for separating the unknown contributions of two or more sources from a commonly acquired set of wavefield signals based on varying parameters at the firing time, location and/or depth of the individual sources in a lateral 2D plane.

One embodiment includes receiving distributed acoustic sensing (DAS) data for responses associated with seismic excitations in an area of interest. The area of interest includes a sea surface, the water column, a seafloor, and a subseafloor. The seismic excitations are generated by at least one seismic source in the area of interest. The responses are detected by at least one fiber optic sensing apparatus configured for DAS that is in the water column, on the seafloor, in a wellbore drilled through the seafloor and into the subseafloor, or any combination thereof. The embodiment includes determining a function of speed of sound in water using the DAS data, and correcting a digital seismic image associated with the area of interest using the function of speed of sound in water to generate a corrected digital seismic image.

Systems and methods for performing seismic migration using an indexed matrix are disclosed. The method includes receiving a seismic trace from a receiver, determining a discretized position of the receiver, and determining a discretized position of a seismic source. The method also includes determining a set of migration indexes based on a matrix, the discretized position of the receiver, and the discretized position of the seismic source, and determining a set of amplitude weights based on the matrix, the discretized position of the receiver, and the discretized position of the seismic source. The method further includes migrating the seismic trace based on the set of migration indexes and the set of amplitude weights.

Systems and methods for performing seismic migration using an indexed matrix are disclosed. The method includes receiving a seismic trace from a receiver, determining a discretized position of the receiver, and determining a discretized position of a seismic source. The method also includes determining a set of migration indexes based on a matrix, the discretized position of the receiver, and the discretized position of the seismic source, and determining a set of amplitude weights based on the matrix, the discretized position of the receiver, and the discretized position of the seismic source. The method further includes migrating the seismic trace based on the set of migration indexes and the set of amplitude weights.

Disclosed herein is a method of stripping a strong reflection layer based on deep learning. The method establishes a direct mapping relationship between a strong reflection signal and seismic data of a target work area through a nonlinear mapping function of the deep neural network, and strips a strong reflection layer after the strong layer is accurately predicted. A mapping relationship between the seismic data containing the strong reflection layer and an event of the strong reflection layer is directedly found through training parameters. In addition, this method does not require an empirical parameter adjustment, and only needs to prepare a training sample that meets the actual conditions of the target work area according to the described rules.

Disclosed herein is a method of stripping a strong reflection layer based on deep learning. The method establishes a direct mapping relationship between a strong reflection signal and seismic data of a target work area through a nonlinear mapping function of the deep neural network, and strips a strong reflection layer after the strong layer is accurately predicted. A mapping relationship between the seismic data containing the strong reflection layer and an event of the strong reflection layer is directedly found through training parameters. In addition, this method does not require an empirical parameter adjustment, and only needs to prepare a training sample that meets the actual conditions of the target work area according to the described rules.

The present invention relates to a method of processing seismic data. The method may include calculating a number of calculated structure tensors for each of a number of seismic data lines, the seismic data lines being spatially distributed about an area of the surface of the Earth. The method also may include interpolating the calculated structure tensors to find interpolated structure tensors in a region of the area between the lines of the seismic data lines, and calculating calculated seismic data from the interpolated structure tensors.

Systems, methods, and computer-readable media for estimating a component of a seismic wavefield. The method may include accessing marine seismic data comprising a plurality of discrete measurements of a seismic wavefield; processing the marine seismic data to determine a relationship between a plurality of components of the seismic wavefield and each of the discrete measurements; and estimating from the marine seismic data processed via the one or more processors, each component of the seismic wavefield separated from each of the other plurality of components of the seismic wavefield and evaluated at a predetermined position.