The present invention relates to a one-way wave equation prestack depth migration method using an elastic generalized-screen (EGS) wave propagator capable of efficiently expressing the movement of an elastic wave passing through a mutual mode conversion between a P-wave and an S-wave while propagating boundary surfaces of an underground medium, by expanding, to an elastic wave equation, a conventional scalar generalized-screen (SGS) technique capable of quickly calculating the propagation of a wave in a medium in which there is a horizontal speed change, and according to the present invention, provided is a prestack EGS migration method for seismic wave multi-component data, which: can calculate a wave field with higher accuracy in a medium having a complex structure by expanding up to a second term of a Taylor series expansion of a vertical slowness term of a propagator; includes a mode separation operator in the propagator so as to directly use a shot gather as a migration input, without the need to separate multi-component data into a P-wave and an S-wave, enabling P-wave and S-wave image sections to be generated; and is configured to improve the quality of an S-wave migration image by correcting a polarity conversion in a wave number-frequency domain prior to S-wave imaging.

A data set representing features of a geologic formation is formed from two or more signal acquisition data set representing independent aspects of the same wavefield. A wavelet transform is performed on the two or more signal acquisition data sets, and the data sets are further transformed to equalize signal portions of the data sets. Remaining differences in the data sets are interpreted as excess noise and are removed by different methods to improve the signal-to-noise ratio of any resulting data set.

A data set representing features of a geologic formation is formed from two or more signal acquisition data set representing independent aspects of the same wavefield. A wavelet transform is performed on the two or more signal acquisition data sets, and the data sets are further transformed to equalize signal portions of the data sets. Remaining differences in the data sets are interpreted as excess noise and are removed by different methods to improve the signal-to-noise ratio of any resulting data set.

Computing device, computer instructions and method for processing input seismic data d. The method includes receiving the input seismic data d recorded in a data domain, solving a linear inversion problem constrained by input seismic data d to obtain a model domain and its energy, wherein the linear inversion problem is dependent on sparseness weights that are simultaneously a function of both time and frequency, reverse transforming the model domain energy to the data domain, and generating an image of a surveyed subsurface based on the reverse transformed model domain energy.

Computing device, computer instructions and method for processing input seismic data d. The method includes receiving the input seismic data d recorded in a data domain, solving a linear inversion problem constrained by input seismic data d to obtain a model domain and its energy, wherein the linear inversion problem is dependent on sparseness weights that are simultaneously a function of both time and frequency, reverse transforming the model domain energy to the data domain, and generating an image of a surveyed subsurface based on the reverse transformed model domain energy.

Methods of analyzing and optimizing a seismic survey design are described. Specifically, the sampling quality is analyzed as opposed to the overall quality of the whole survey. This allows for analysis of the impact of the offsets, obstacles, and other aspects of the survey on the sampling quality, which will improve the ability to compress the resulting data and minimize acquisition footprints.

Methods of analyzing and optimizing a seismic survey design are described. Specifically, the sampling quality is analyzed as opposed to the overall quality of the whole survey. This allows for analysis of the impact of the offsets, obstacles, and other aspects of the survey on the sampling quality, which will improve the ability to compress the resulting data and minimize acquisition footprints.

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.

This disclosure is directed to methods and systems to evaluate noise contend of seismic data received during a marine survey. The seismic data includes pressure and particle motion data generated by collocated pressure and particle motion sensors of a seismic data acquisition system. The pressure and particle motion data are cross ghosted and temporal and spatial wavelet transforms are applied to the cross-ghosted pressure and particle motion data in order to compute pressure energies and particle motion energies in temporal and spatial scales of a temporal and spatial scale domain. The pressure and particle motion energies may be compared to evaluate noise content in the pressure and particle motion data, evaluate changes in the noise content during the marine survey, and adjust marine survey parameters to reduce the noise content.

This disclosure is directed to methods and systems to evaluate noise contend of seismic data received during a marine survey. The seismic data includes pressure and particle motion data generated by collocated pressure and particle motion sensors of a seismic data acquisition system. The pressure and particle motion data are cross ghosted and temporal and spatial wavelet transforms are applied to the cross-ghosted pressure and particle motion data in order to compute pressure energies and particle motion energies in temporal and spatial scales of a temporal and spatial scale domain. The pressure and particle motion energies may be compared to evaluate noise content in the pressure and particle motion data, evaluate changes in the noise content during the marine survey, and adjust marine survey parameters to reduce the noise content.