Methods and systems are disclosed for forming an image of a subterranean region of interest. The method includes receiving an observed seismic dataset and a seismic velocity model for the subterranean region of interest, and generating a simulated seismic dataset based on the seismic velocity model and the geometry of the observed seismic dataset. The method further includes determining a transformed observed seismic dataset by applying a nonlinear amplitude transform to the observed seismic dataset and determining a transformed simulated seismic by applying the same transform to the simulated seismic dataset. The method still further includes forming an objective function based on the transformed observed seismic and the transformed simulated seismic dataset, and determining an updated seismic velocity model based upon finding an extremum of the objective function.

A computer-implemented method is provided for interpreting geophysical data utilising an Artificial Neural Network (ANN), performed by electronic operations executed by a computing device, comprising: performing a training processing step on at least one training-data set, comprising the steps of: (a) generating a first label-data by segmenting said at least one training-data set into at least a first region, representing a known first region having at least one identified geological feature, and/or a second region, representing a known second region having at least one unidentified geological feature, and a third region, representing an unknown region; (b) generating a first ANN model output for a dynamically adaptable Region of Interest (ROI) of said first label-data, said dynamically adaptable ROI including said first and/or second region; (c) generating an updated label-data by selecting at least a first portion of any one of said first, second and third region, and labelingly append at least said first portion to any one of said first, second and third region; (d) generating an updated ANN model output for an updated dynamically adaptable ROI of said updated label-data; (e) repeating steps (c) and (d) until a predetermined condition is met, providing a final ANN model output; and then applying said final ANN model output to a target-data set utilising said ANN, generating a desired output data.

Seismic exploration of an underground formation uses seismic excitations to probe the formation's properties such as reflectivity that can be imaged using reverse time migration. Using an equal area spherical binning at reflection points improves and simplifies RTM imaging together with adaptability to the data acquisition geometry, while overcoming drawbacks of conventional cylindrical binning.

An optical seismic surveying system including, a multibeam laser source including a plurality of laser-sources, a Diffractive-Optical-Element (DOE), an imager and a processor. The laser-sources direct respective laser-beams toward a single common focal point. The DOE is located at a single common focal point and configured to split each laser-beam into a plurality of laser-beams, toward an instantaneous area of interest. The laser-beams impinging on the instantaneous area of interest produce a laser spot assemblage including a plurality of laser spots. The imager acquires a plurality of defocused images of speckle patterns produced by diffused reflections of the laser spots. The speckle pattern correspond to a respective laser spot and thus to a respective sensing point in the instantaneous area of interest. The processor determines a relative displacement between corresponding speckle patterns in sequential pairs of images and determines a respective time-signal for each sensing point representing vibrations thereat.

An optical seismic surveying system including, a multibeam laser source including a plurality of laser-sources, a Diffractive-Optical-Element (DOE), an imager and a processor. The laser-sources direct respective laser-beams toward a single common focal point. The DOE is located at a single common focal point and configured to split each laser-beam into a plurality of laser-beams, toward an instantaneous area of interest. The laser-beams impinging on the instantaneous area of interest produce a laser spot assemblage including a plurality of laser spots. The imager acquires a plurality of defocused images of speckle patterns produced by diffused reflections of the laser spots. The speckle pattern correspond to a respective laser spot and thus to a respective sensing point in the instantaneous area of interest. The processor determines a relative displacement between corresponding speckle patterns in sequential pairs of images and determines a respective time-signal for each sensing point representing vibrations thereat.

A method, apparatus, and program product utilize a super resolution machine learning model to reconstruct high resolution seismic data from low resolution seismic data in connection with generating seismic visualizations, e.g., to reduce storage and/or communication costs associated with generating seismic visualizations.

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.

A device may provide, to a user device, a first message instructing a technician to move fiber cables and may receive a first signal based on the technician moving the fiber cables and a rest signal based on the technician stopping movement of the fiber cables. The device may calculate a distance, an average peak signal, and a baseline signal based on the first signal and the rest signal and may calculate a data collection window based on the distance, the average peak signal, and the baseline signal. The device may provide, to the user device, a second message instructing the technician to move one fiber cable at a time and may receive second signals based on the technician moving one fiber cable at a time. The device may provide, for display to the user device, the data collection window and indications of the second signals.

A device may provide, to a user device, a first message instructing a technician to move fiber cables and may receive a first signal based on the technician moving the fiber cables and a rest signal based on the technician stopping movement of the fiber cables. The device may calculate a distance, an average peak signal, and a baseline signal based on the first signal and the rest signal and may calculate a data collection window based on the distance, the average peak signal, and the baseline signal. The device may provide, to the user device, a second message instructing the technician to move one fiber cable at a time and may receive second signals based on the technician moving one fiber cable at a time. The device may provide, for display to the user device, the data collection window and indications of the second signals.

A least one seismic attribute is determined for each voxel of the seismic volume. A first horizon is selected for mapping and a sparse global grid is generated which includes the horizon, at least one constraint point identifying the horizon, and a number of points having a depth in the seismic volume. A value of at least one seismic attribute is determined for each point and their depths are adjusted based on the value of the seismic attribute. A map of the horizon can be generated based on the adjusted depths. Multiple local grids can be generated based on the sparse global grid, and the depths of the local grid points adjusted to generate a map of the horizon at voxel level resolution. The seismic volume can be mapped into multiple horizons, where previously mapped horizons can function as constraints on the sparse global grid.