Processes and systems described herein are directed to imaging a subterranean formation from seismic data recorded in a marine survey with moving marine vibrators. The marine vibrators generate random sweeps with random sweep signatures. Processes and systems generate an up-going pressure wavefield from measured pressure and vertical velocity wavefield data recorded in the marine survey and obtain a downgoing vertical acceleration wavefield that records source wavefields, directivity, source ghosts, and random signatures of the random sweeps. The downgoing vertical acceleration wavefield data is deconvolved from the up-going pressure wavefield to obtain a subsurface reflectivity wavefield that is used to generate an image of the subterranean formation with reduced contamination from source wavefields, directivity, source ghosts, and random signatures of the random sweeps.

Systems and methods include a computer-implemented method for determining normalized apparent power. Drilling acoustic signals corresponding to a time domain and generated during drilling of a well. A fast Fourier transformation (FFT) is performed using the drilling acoustic signals to generate FFT data. Normalized FFT data is generated using normalization parameters and a drill string rotation rate record of a drill string used to drill the well. The drill string rotation rate is received during drilling. Normalized apparent power is determined from data points of a predetermined top percentage of the normalized FFT data within a lithological significant frequency range. The normalized apparent power is a measure of the power of the drilling acoustic signals and it is a function of the amplitude and frequency of the normalized FFT data. The lithological significant frequency range is a frequency range within which the drill sounds are more closely related with lithology.

A method for seismic imaging includes receiving a multi-shot seismic data set that was collected using one or more streamers having recorders configured to detect seismic waves that propagate through a subterranean domain. The method also includes partitioning the multi-shot seismic data set into windows including a source dimension. The method also includes defining one or more first basis functions that describe the windows of the multi-shot seismic data set. The method also includes generating a model that describes a decomposition of the multi-shot seismic data set using the one or more first basis functions. The method also includes defining one or more second basis functions that describe a selected output data. The method also includes combining the one or more second basis functions with the model to produce a result for a source side wavefield and a receiver side wavefield.

Techniques to reduce noise in seismic data by receiving a set of seismic data comprising a plurality of input volumes each inclusive of positional data and at least one additional attribute related to the seismic data, selecting a first input volume of the plurality of input volumes having a first additional attribute related to the seismic data, and generating a pilot volume by selecting a range of input volumes of the plurality of input volumes and stacking input volumes of the range of input volumes with the first input volume. Additionally, generating a trained dictionary based upon transformation of the pilot volume, transforming the first input volume into transformed data, imposing a sparse condition on the transformed data utilizing the trained dictionary to generate sparsified data, and inverse transforming the sparsified data to generate an output data volume as a portion of a set of modified seismic data.

An example method is for producing a seismic wave velocity model of a subsurface area. The method includes receiving, by a processor of a computing system, from a seismic receiver, seismic data input comprising a recorded seismic wave field. The method includes receiving, by the processor, an initial 1D velocity model of the subsurface area. The method includes determining, by the processor, a Laplace-Fourier transform of the recorded seismic wave field. The method includes regenerating, by the processor, the current 1D velocity model to generate inverted data representing the subsurface area. The method may include performing, by the processor, an upscaling of a plurality of 1D velocity models to produce a 3D velocity model.

The present invention belongs to the field of treatment for data identification and recording carriers, and specifically relates to a method and system for evaluating the filling characteristics of a deep paleokarst reservoir through well-to-seismic integration, which aims to solve the problems that by adopting the existing petroleum exploration technology, the reservoir with fast lateral change cannot be predicted, and the development characteristics of a carbonate cave type reservoir in a large-scale complex basin cannot be identified. The method comprises: acquiring data of standardized logging curves; obtaining a high-precision 3D seismic amplitude data body by mixed-phase wavelet estimation and maximum posteriori deconvolution and enhancing diffusion filtering. According to the method and the system, the effect of identifying the development characteristics of the carbonate karst cave type reservoir in the large-scale complex basin can be achieved, and the characterization precision is improved.

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 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.

Geologic modeling methods and systems disclosed herein employ fault face parameterization to constrain and improve the transformation of a faulted physical space geologic model into an unfaulted depositional space geologic model. An illustrative embodiment includes: associating a seismic image with each face of at least one fault in a subsurface region; determining a correspondence map between the seismic images for said at least one fault; parameterizing the faces using the correspondence map to match parameter value assignments for corresponding portions of the faces; creating a displacement map that draws together matching parameter values to align the corresponding portions of the faces; applying the displacement map to the geologic model to create a design space model; modifying the design space model; applying the displacement map in reverse to the modified design space model to obtain a modified geologic model; and outputting the modified geologic model.

Methods and systems for determining a spectral ratio log using a time domain seismic image and a seismic velocity model are disclosed. The method includes determining a first mono-spectral seismic image and a second mono-spectral seismic image from the time domain seismic image. The method further includes determining a time domain spectral ratio image from the first mono-spectral seismic image and the second mono-spectral seismic image and transforming the time domain spectral ratio image into a depth domain spectral ratio image using the seismic velocity model. The method still further includes defining a wellbore path through the depth domain spectral ratio image and determining a spectral ratio log along the wellbore path from the depth domain spectral ratio.