This disclosure presents processes and systems for generating an image of a subterranean formation from seismic data recorded in a seismic survey of the subterranean formation. The seismic data is contaminated with low frequency noise in a low frequency band. Processes and systems reconstruct seismic data in the low frequency band of the seismic data to obtain low frequency reconstructed seismic data that is free of the low frequency noise. The low frequency reconstructed seismic data is used to construct a velocity model of the subterranean formation. The velocity model and the low frequency reconstructed seismic data are used to generate an image of the subterranean formation that reveals structures of the subterranean formation without contamination from the low frequency noise.

A seismic dataset and a task to be performed with the seismic dataset may be received. A representative seismic line representative of the seismic dataset may be generated. The representative seismic line may include pixel data representative of the seismic dataset. Based on the representative seismic line, the task may be performed. The task may include at least finding an analogous geological region by searching for an analogous seismic dataset existing in a seismic database by comparing the representative seismic line with the analogous seismic dataset's representative seismic line.

The disclosure provides systems and methods to enhance pre-stack data for seismic data analysis by: sorting the reflection seismic data acquired from cross-spread gathers into sets of data sections; performing data enhancement on the sets of data sections to generate enhanced traces by: (i) applying forward normal-moveout (NMO) corrections such that arrival times of primary reflection events become more flat, (ii) estimating beamforming parameters including a nonlinear traveltime surface and a summation aperture, (iii) generating enhanced traces that combine contributions from original traces in the sets of data sections, and (iv) applying inverse NMO corrections to the enhanced traces such that temporal rearrangements due to the forward NMO corrections are undone.

Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data; and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.

A method for modeling a subterranean volume includes receiving seismic data comprising a signal, generating a semblance in the frequency-wavenumber domain for the seismic data, wherein the semblance represents a coherence of the signal in the frequency-wavenumber domain, extracting one or more wave energy modes in the semblance using a machine learning model trained to identify dispersion curves in the semblance based on a visible characteristic of the dispersion curves, and generating a model representing surface wave propagation based at least in part on the identified one or more wave energy modes.

Methods and devices for seismic exploration of an underground structure apply W.sup.2-based full-wave inversion to transformed synthetic and seismic data. Data transformation ensures that the synthetic and seismic data are positive definite and have the same mass using an adaptive normalization. This approach yields superior results particularly when the underground structure includes salt bodies.

Methods and devices for seismic exploration of an underground structure apply W.sup.2-based full-wave inversion to transformed synthetic and seismic data. Data transformation ensures that the synthetic and seismic data are positive definite and have the same mass using an adaptive normalization. This approach yields superior results particularly when the underground structure includes salt bodies.

A distributed acoustic sensor is positioned within a wellbore of a geologic formation. Seismic waves are detected using the distributed acoustic sensor. A raw seismic profile is generated based on the detected seismic waves. Resonant noise is identified and reduced in seismic data associated with the raw seismic profile.

A distributed acoustic sensor is positioned within a wellbore of a geologic formation. Seismic waves are detected using the distributed acoustic sensor. A raw seismic profile is generated based on the detected seismic waves. Resonant noise is identified and reduced in seismic data associated with the raw seismic profile.

Methods and seismic data processing apparatuses use a down-going annihilation operator to generate an image from seismic data acquired over a water-covered subsurface formation. The down-going annihilation operator is derived using a down-going wavefield and an estimated water-wave extracted from the seismic data. The down-going annihilation operator may be derived in plane-wave domain.