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.

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.

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 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.

Methods of seismic data processing employ neural networks and use a reflectivity image based on the acquired seismic data to generate output training datasets. The neural networks thus trained are used for generating production datasets, without ghosts, source effects, multiples and/or populating a predetermined set of bins in inline-crossline plane for a set of offset classes.

A method for correcting for distortions in a seismic acoustic wavefield produced by a seismic vibrator that is immersed in a fluid, the method comprising: applying a pilot signal to the vibrator such that an acoustic wavefield travels outwards from the interface between the vibrator outer surface and the fluid: using a sensor located within the fluid so as to be sensitive to a property of the wavefield in the fluid to monitor the property of the wavefield as a function of time: and using the measured property to determine a change in wet volume of a component of the vibrator as a function of time V(t) or derivatives of V(t), and using the determined wet volume V(t) or its derivatives to correct for distortions in the seismic data produced by the vibrator.

This specification describes workflows for, but is not limited to, performing full waveform inversion (FWI) to build high resolution velocity models to improve the accuracy of seismic imaging of a subterranean formation. This specification describes processes to automatically edit and enhance S/N quality of seismic data (such as land seismic data) to prepare the datasets for FWI. The methods for automatic corrections and pre-processing include: automatic iterative surface-consistent residual statics calculation, automatic rejection of anomalous traces (such as dead traces), and the automatic correction of surface-consistent amplitude anomalies (such as by scalar or deconvolution approaches). The operations include automatic “muting” of noise before first arrivals.

A method includes receiving seismic data including signals collected using a receiver, separating a downgoing wavefield from an upgoing wavefield in the signals, generating a modified downgoing wavefield by removing direct arrivals from the downgoing wavefield, estimating a first-order multiple reflection signal at least partially by deconvolving the modified downgoing wavefield and the downgoing wavefield, and generating a seismic image based at least in part on the estimated first-order multiple reflection signals.

Scattered body waves are isolated to primary, shear, and surface waves as a receiver wavefield from recorded near-surface scattered wave data generated by scatters. The isolated receiver wavefield is backward propagated through an earth model from a final to an initial state. A source wavefield and the receiver wavefields are cross-correlated. A source wavefield and the receiver wavefields are stacked, over all time steps and sources, to generate a subsurface image. A display of the subsurface image is initiated.