Systems and methods are disclosed. The method may include receiving observed seismic data, and a first and a second seismic velocity model, each pertaining to a subterranean region of interest and, iteratively, determining synthetic reflection data based on the first and second seismic velocity model, determining traveltime shift data between the synthetic observed seismic data, and determining warped observed seismic data by applying the traveltime shift data to the observed seismic data. The method further includes determining conditioned traveltime shift data using local similarity based on shaping regularization from the synthetic reflection data, the warped observed seismic data, and the traveltime shift data, and determining a seismic velocity model based on the first seismic velocity model and the conditioned traveltime shift data. The method also includes determining a seismic image from observed seismic data and the seismic velocity model, and a location of a hydrocarbon reservoir using the seismic image.

Methods and devices for geophysical exploration include: acquiring seismic data representing a subterranean formation; obtaining a plurality of virtual super gathers (VSGs) comprising the seismic data by sorting the seismic data based on a hypercube with dimensions comprising common midpoint X-Y coordinates, an offset, and an azimuth, wherein one or more seismic attributes are obtained at the dimensions of the hypercube; transforming the seismic data, in the plurality of VSGs from a time-offset domain to a frequency-phase velocity domain; isolating, for VSGs of the plurality, frequencies and phase velocities of the frequency-phase velocity domain based on a windowing operation; and for frequencies isolated by the windowing operation, identifying a maximum magnitude of a phase velocity spectrum from the plurality of VSGs.

A method may include obtaining seismic data regarding a geological region of interest. The seismic data may correspond to a seismic survey that is divided into various bins in a predetermined bin grid. The method may further include determining a first multiblock bin within the seismic survey. The first multiblock bin may correspond to a source bin and a receiver bin among the bins. The method may further include determining traveltime table data using the seismic data and various multiblock bins that include the first multiblock bin. The method further includes determining migrated data using the seismic data, the traveltime table data, a velocity model, a migration function, and various parallel processors. The method further includes generating a seismic image of the geological region of interest using the migrated data.

Example methods and systems for seismic depth migration are disclosed. One example method includes obtaining a surface grid of a region for seismic imaging of subsurface structures of the region. The surface grid includes multiple grid cells with each having multiple vertices, and each of the multiple vertices is associated with a traveltime between the vertex and a first multiple subsurface points to be imaged. An interpolated traveltime associated with a first grid cell of the multiple grid cells and a ray tracing-based traveltime associated with the first grid cell are determined. A difference between the interpolated traveltime and the ray tracing-based traveltime is compared to a threshold. In response to determine that the difference is larger than the threshold, the first grid cell is subdivided into a first multiple smaller grid cells. The surface grid with the first multiple smaller grid cells is provided for seismic depth migration.

Methods and systems are disclosed. The method may include obtaining an anisotropic velocity model for a subterranean region of interest, where the anisotropic velocity model represents a propagation velocity of seismic waves discretized on a first grid of nodes representing the subterranean region of interest and initiating an initial anisotropic traveltime for each node of a second grid representing the subterranean region of interest, where the anisotropic traveltime comprises a seismic traveltime from a source location. The method may further include forming a computational system, comprising a discretization of a traveltime equation for the anisotropic velocity model, and determining an updated anisotropic traveltime for each node on the second grid based, at least in part, on a parallel fast sweeping Cuthill-McKee ordering solution to the computational system and the initial anisotropic traveltime for each node.

The present disclosure discloses a method and apparatus for seismic imaging with VSP while-drilling and well-driven. The method includes: acquiring a seismic data processing result and logging data within a processing area range corresponding to a target well; optimizing and adjusting the seismic data processing result and the logging data to obtain anisotropic parameters for pre-stack depth migration; updating a seismic velocity of the target well with a VSP velocity as a constraint; performing iterative optimization on a VSP-driven corrected seismic velocity field to obtain an iteratively optimized seismic velocity field and anisotropic parameter field; migrating a pre-stack depth migration volume to obtain a migration result; performing post-stack frequency enhancement refinement on the migration result to obtain a migration result after the post-stack frequency enhancement refinement; and determining a maximum probability imaging position of the target well at a target reservoir.

A first arrival wave reverse time migration (RTM) method based on excitation amplitude includes the steps of acquiring calculation parameters of a seismic wave field, performing forward continuation on a source wave field, identifying a first arrival wave of the source wave field, and preserving the maximum amplitude and corresponding time of the first arrival wave; performing reverse time continuation on a receiver wave field; and cross-correlating the maximum amplitude of the source wave field with the receiver wave field at the same moment on each spatial grid node, and superimposing cross-correlation values to obtain an RTM result. In the present disclosure, by preserving the maximum amplitude and time of the first arrival wave, and cross-correlation imaging is conducted with the receiver wave field. The present disclosure can reduce the calculation amount and the interference of multi-path waves and improve a signal-to-noise ratio while ensuring the RTM imaging capability.

A computer-implemented method for determining modeled seismic data of a subsurface region includes receiving observed seismic data from the subsurface region captured by one or more seismic receivers as one or more reflected seismic signals; determining, based on a velocity model of the subsurface region and the observed seismic data, a traveltime function associated with the subsurface region; migrating, using the traveltime function and a Kirchhoff migration operator, the observed seismic data to produce a migrated seismic image; and performing reverse-time demigration on the migrated seismic image, using the velocity model and a solved full wave-equation, to produce modeled seismic data.

Techniques for determining subsurface conditions in a multi-well system may include detecting, at time t.sub.1, a tube wave at a first well system of the multi-well system. The techniques may further include detecting, at time t.sub.2, the tube wave at a second well system of the multi-well system. The techniques may further include determining a time differential t.sub.d between t.sub.1 and t.sub.2. The techniques may further include determining, based at least in part on t.sub.d, that the first well system and the second well system are in fluid communication via a formation.

A computer implemented method for detecting and locating microseismic events is provided. The method comprises using a processor set to receive a dataset from a number of real stations. The dataset comprises information associated with seismic signals in a time period. The processor set trains a neural network comprising a number of neural operators using the dataset. The number of neural operators comprise a combination of neural operator layers for identifying temporal-spatial information associated with the seismic signals in the time period. The neural network further comprises a classification model and a regression model. The classification model and the regression model are trained using the dataset and the temporal-spatial information associated with the seismic signals in the time period. The processor set detects and locates a number of seismic events using the trained neural network.