A method can include acquiring imagery of an exposed surface of the Earth; generating a multi-dimensional model based at least in part on the imagery; generating synthetic seismic data utilizing the multi-dimensional model; acquiring seismic data of a subsurface region of the Earth; performing a search that matches a portion of the acquired seismic data and a portion of the synthetic seismic data; and characterizing the subsurface region of the Earth based at least in part on the portion of the synthetic seismic data.

A computer-implemented method and computing system apparatus programmed to perform operations of the computer-implemented method for obtaining a subsurface stack image, subsurface angle gathers, and a subsurface velocity model over an entire survey region having high velocity contrast geo-bodies. Particularly, user inputs, input velocity models, and surface-seismic data are obtained by fixed source and receiver pairs and then used by the computer program product embedded within the computing system apparatus to minimize the number of iterations, required to obtain a final velocity model, a final stack image, and final angle gathers wherein their flatness deviation is equal to, or less than, a user-defined flatness value. Therefore, the attributes developed by said computer-implemented method and system can help solve the imaging problem of sub high velocity contrast geo-bodies like subsalt, or salt overhung deep mini basins.

Methods for processing seismic data acquired with non-impulsive moving sources are provided. Some methods remove cross-talk noise from the seismic data using emitted signal data and an underground formation's response estimate, which may be iteratively enhanced. Some methods perform resampling before a spatial or a spatio-temporal inversion. Some methods compensate for source's motion during the inversion, and/or are usable for multiple independently moving sources.

Systems and methods for modeling dispersion curves are disclosed. The method includes obtaining an acoustic dataset along a well that accesses a hydrocarbon reservoir. The method further includes determining a set of depth windows along the well and determining a first subset of dispersion curves for a first subset of depth windows using a dispersion model. The method still further includes initializing a second subset of dispersion curves for a second subset of depth windows using a nearest neighbor search of the first subset of dispersion curves. The method still further includes determining slowness-frequency pairs for the second subset of depth windows using the acoustic dataset and updating the second subset of dispersion curves using a recursive scanning method. The method still further includes characterizing rock properties near the well based, at least in part, on the first subset of dispersion curves and the second subset of dispersion curves.

The present disclosure belongs to the technical field of seismic exploration imaging, and relates to an interpretive-guided velocity modeling seismic imaging method and system, a medium and a device. The method comprises the following steps: S1. performing first imaging on a given initial velocity model to obtain a first imaging result; S2. performing relative wave impedance inversion on the first imaging result to obtain a relative wave impedance profile; S3. performing Curvelet filtering on the relative wave impedance profile to obtain a first interpretation scheme; S4. superposing the first interpretation scheme and the initial velocity model to obtain a new migration velocity field; S5. performing second imaging on a new migration velocity field to obtain a second imaging result; and S6. repeating steps S2-S4 for the obtained second imaging result until a final seismic imaging result is obtained.

This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.

A neural network is utilized to improve the resolution of subsurface inversion. The neural network leverages posterior distribution of samples and adds high frequency components to the inversion by utilizing the data in both the time domain and the frequency domain. The improved resolution of the subsurface inversion enables more accurate prediction of subsurface characteristics (e.g., reservoir architecture).

Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving seismic data acquired by at least one receiver of a geologic survey system configured to perform a geologic survey of a subterranean formation, wherein the seismic data is associated with reflected acoustic signals generated by at least one source of the geologic survey system; calculating a ground force signal by stacking the acoustic signals generated by the least one source; calculating, using the ground force signal, a time and depth variant quality factor (Q) of the subterranean formation; and compensating, based on the time and depth variant Q, attenuation in the seismic data.

Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving an input dataset that represents partial spatial information of an area of interest; providing the input dataset to a spatial context generator, wherein the spatial context generator comprises a machine learning model trained to generate, based on the partial spatial information, contextual spatial information for the area of interest; and using the spatial context generator to generate, based on the partial spatial information, at least one output dataset associated with the area of interest, where each output dataset comprises simulated contextual spatial information for the area of interest.

A global objective function is initialized to an initial value. A particular model simulation process is executed using prepared input data. A mismatch value is computed by using a local function to compare an output of the particular model simulation process to corresponding input data for the particular model simulation process. Model objects associated with the particular model simulation process are sent to another model simulation process. An optimization process is executed to predict new values for input data to reduce the computed mismatch value.