An apparatus, method, and system for determining body wave slowness from guided borehole waves. The method includes selecting a target axial resolution based on the size of a receiver array, obtaining a plurality of waveform data sets corresponding to a target formation zone and each acquired at a different shot position, computing a slowness-frequency 2D dispersion semblance map for each waveform data set, stacking the slowness-frequency 2D dispersion semblance maps to generate a stacked 2D semblance map, and determining a body wave slowness from the extracted dispersion curve. The method may also include generating a self-adaptive weighting function based on a dispersion model and the extracted dispersion curve, fitting the weighted dispersion curve and the dispersion model to determine a body wave slowness that minimizes the misfit between the weighted dispersion curve and the dispersion model. The method can be applied to both frequency-domain and time-domain processing.

A method and apparatus of locating subsurface geohazards in a geographical area that includes: receiving a plurality of seismic trace signals in the geographical area based on a shot gather from a seismic shot source; isolating and stacking the plurality of seismic trace signals to generate a windowed trace signal associated with refraction traces from the seismic shot source; transforming the windowed trace signal to a frequency domain; calculating a low frequency to high frequency ratio for the transformed trace signal; outputting the calculated ratio to a two-dimensional array representing the geographical area at a source location and at a mean receiver location; repeating the steps for a plurality of other shot gathers in the geographical area; and multiplying each source location ratio with one or more mean receiver location ratios on the two-dimensional array to generate a final frequency ratio map.

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.

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.

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.

One method interpolates simulated seismic data of a coarse spatial sampling to a finer spatial sampling using a neural network. The neural network is previously trained using a set of simulated seismic data with the finer spatial sampling and a subset thereof with the coarse spatial sampling. The data is simulated using an image of the explored underground formation generated using real seismic data. The seismic dataset resulting from simulation and interpolation is used for denoising the seismic data acquired over the underground formation. Another method demigrates seismic data at a sparse density and then increases density by interpolating traces using a neural network.

A method may include obtaining various seismic traces for a geological region of interest. The method may further include determining an offset attribute and an azimuthal attribute. The method may further include determining, using the offset attribute and the azimuthal attribute, a virtual trace bin for the geological region of interest. The method may further include generating a virtual trace using a subset of the seismic traces and corresponding to the virtual trace bin. The method may further include generating a velocity model for the geological region of interest using a virtual shot gather including the virtual trace and various virtual traces. A respective virtual trace among the virtual traces may correspond to a respective virtual trace bin among various virtual trace bins. The method may further include generating a seismic image of the geological region of interest using the velocity model.

Systems, media, and methods for processing seismic data are disclosed. For example, in one embodiment, the method may include receiving a plurality of partial image partitions of a migrated seismic image, and stacking the plurality of partial image partitions such that a first image is generated. The method may also include aligning the plurality of partial image partitions based at least partially on the first image. Aligning may include adjusting at least one of the plurality of partial image partitions and generating a displacement field. The method may also include, based at least in part on the displacement field, stacking the plurality of aligned partial image partitions to generate a second image. The method may further include based at least in part on the second image, realigning the plurality of aligned partial image partitions.

Method for optimal stacking of seismic images to remove noise and enhance signals in seismic images (101) outputted from a Reverse Time Migration (RTM) imaging process. Dip information is calculated (102) and then sorted by image point (104), for each seismic image to be stacked. A dominant dip and azimuth is determined at each image point (106), and only those events are stacked (107). If the image is still noisy or lacking in detail (108), the process may be iterated (109) to improve the selection of most likely dip and azimuth.

Methods for full waveform inversion (FWI) in the midpoint-offset domain include using a computer system to sort seismic traces into common midpoint-offset bins (XYO bins). For each XYO bin, a linear moveout correction is applied to a collection of seismic traces within the XYO bin. The collection of seismic traces is stacked to form a pilot trace. The computer system determines a surface-consistent residual static correction for each seismic trace. The computer system determines that the surface-consistent residual static correction for each seismic trace is less than a threshold. Responsive to the determining that the surface-consistent residual static correction is less than the threshold, the computer system stacks the collection of seismic traces to provide the pilot trace. The computer system groups the pilot traces for the XYO bins into a set of virtual shot gathers. The computer system performs one-dimensional FWI based on each virtual shot gather.