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.

One or more first sensors may be configured to sense seismic signals and one or more second sensors may be configured to sense electromagnetic crosstalk signals. The second sensors are not responsive to the seismic signals. The data from the first and second sensors may be recorded as first data and second data, respectively. The first data may be modified based on the second data to remove the electromagnetic crosstalk noise form the seismic data.

A computing program product and method for interpolating wavelets coefficients and estimating spatial varying wavelets using the covariance interpolation method in the data space over a survey region having multiple well locations, are disclosed. The method and computing program product, embodied in a non-transitory computer readable device, that stores instructions for performing by a device are based on interpolating coefficient models in the data space domain using covariance analysis methods to overcome inaccuracy and instability issues commonly observed during wavelet estimation and interpolation.

A method for generating an image of a subsurface based on blended seismic data includes receiving the blended seismic data, which is recorded so that plural traces have uncontaminated parts with different uncontaminated recording time lengths, selecting plural subgroups (SG1, SG2) of traces so that each subgroup (SG1) includes only uncontaminated parts that have a same uncontaminated recording time length, processing the traces from each subgroup to generate processed traces, mapping the processed traces to a same sampling, combining the processed traces from the plural subgroups (SG1, SG2) to generate combined processed traces, and generating an image of a structure of the subsurface based on the combined processed traces.

This disclosure relates to geophysical exploration and seismic imaging, and more particularly to a seismic imaging method, system, and device based on pre-stack high-angle fast Fourier transform (FFT). The method includes: acquiring seismic data acquired during seismic exploration; extracting a common shot point gather from the seismic data followed by conversion into a frequency wavenumber domain common offset gather; calculating wave propagation angles; dividing an imaging region into a first region and a second region; solving constant coefficients of the first region and the second region; performing frequency-division layer-by-layer wavefield continuation on a frequency-wave number domain common offset gather to obtain imaging results at different depths and frequencies; subjecting the imaging results to integration followed by transformation to a spatial domain to obtain common offset imaging profiles; and subjecting the common offset imaging profiles to superposition obtain final imaging results.

A reflection full waveform inversion method updates separately a density model and a velocity model of a surveyed subsurface formation. The method includes generating a model-based dataset corresponding to the seismic dataset using a velocity model and a density model to calculate an objective function measuring the difference between the seismic dataset and the model-based dataset. A high-wavenumber component of the objective function's gradient is used to update the density model of the surveyed subsurface formation. The model-based dataset is then regenerated using the velocity model and the updated density model, to calculate an updated objective function. The velocity model of the surveyed subsurface formation is then updated using a low-wavenumber component of the updated objective function's gradient. A structural image of the subsurface formation is generated using the updated velocity model.

A fiber optic cable positioned along a casing string in a wellbore may be calibrated by exciting a tube wave in the wellbore and detecting, by the fiber optic cable, a reflected tube wave. The reflected tube wave may correspond to a reflection of the tube wave off an obstacle within the wellbore. The obstacle may have a known location such that a reference point along the fiber optic cable may be associated with the known location of the obstacle for calibrating the fiber optic cable. Downhole applications utilizing data collected by the calibrated fiber optic cable, including location data, may weight the data collected based at least in part on an uncertainty value associated with a particular calibrated location along the length of the fiber optic cable.

A fiber optic cable positioned along a casing string in a wellbore may be calibrated by exciting a tube wave in the wellbore and detecting, by the fiber optic cable, a reflected tube wave. The reflected tube wave may correspond to a reflection of the tube wave off an obstacle within the wellbore. The obstacle may have a known location such that a reference point along the fiber optic cable may be associated with the known location of the obstacle for calibrating the fiber optic cable. Downhole applications utilizing data collected by the calibrated fiber optic cable, including location data, may weight the data collected based at least in part on an uncertainty value associated with a particular calibrated location along the length of the fiber optic cable.

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.

Methods and apparatuses for seismic data processing perform a least-squares reverse time migration method in which surface-attribute-independent coefficients for the surface attribute gathers are demigrated to reduce the computational cost.