Methods for separating the unknown contributions of two or more sources from a commonly acquired set of wavefield signals based on varying parameters at the firing time, location and/or depth of the individual sources in a lateral 2D plane.

A method can include receiving microseismic data of microseismic events as acquired by sensors during hydraulic fracturing of a geologic region; jointly calibrating sensor orientation of the sensors and a velocity model of the geologic region via an objective function and the microseismic data; and, based at least in part on the jointly calibrating, determining one or more locations of the one or more microseismic events.

A method of seismic modeling using an elastic model, the elastic model including a grid having a grid spacing sized such that, when synthetic seismic data is generated using the elastic model, synthetic shear wave data exhibits numerical dispersion, the method including: generating generated synthetic seismic data using the elastic model, wherein the generated synthetic seismic data includes synthetic compression wave data and synthetic shear wave data, and modifying the generated synthetic seismic data to produce modified synthetic seismic data by attenuating at least some of the synthetic shear wave data in order to attenuate at least some of the numerically dispersive data.

A method for exploring for hydrocarbons, including: simulating a seismic waveform, using a computer, wherein computations are performed on a computational grid representing a subsurface region, said computational grid using perfectly matched layer (PML) boundary conditions that use an energy dissipation operator to minimize non-physical wave reflections at grid boundaries; wherein, in the simulation, the PML boundary conditions are defined to reduce computational instabilities at a boundary by steps including, representing direction of energy propagation by a Poynting vector, and dissipating energy, with the dissipation operator, in a direction of energy propagation instead of in a phase velocity direction; and using the simulated waveform in performing full waveform inversion or reverse time migration of seismic data, and using a physical property model from the inversion or a subsurface image from the migration to explore for hydrocarbons.

A method can include selecting a type of geophysical data; selecting a type of algorithm; generating synthetic geophysical data based at least in part on the algorithm; training a deep learning framework based at least in part on the synthetic geophysical data to generate a trained deep learning framework; receiving acquired geophysical data for a geologic environment; implementing the trained deep learning framework to generate interpretation results for the acquired geophysical data; and outputting the interpretation results.

A system and method for modeling seismic data using time preserving tomography including storing an initial set of parameter values representing an initial seismic data model. The initial seismic model may correspond to at least two or more ray pairs. Each ray pair may have a traveltime. An altered model may be generated by altering two or more parameter values in the initial set of parameter values for each of two or more ray pairs in the initial model. Altering one parameter value without altering the remaining of the two or more parameter values may correspond to a change in the traveltime of each of the ray pairs, while altering the two or more parameter values in combination typically corresponds to no net change in the traveltime of each of the ray pairs.

Methods and apparatuses for directional designature in shot domain are provided. Azimuth and take-off angles are calculated for each record in the seismic data. Directional designature is then applied to the seismic data using a source signature dependent on the azimuth and take-off angles.

Methods and systems for computing notional source signatures from modeled notional signatures and measured near-field signatures are described. Modeled near-field signatures are calculated from the modeled notional signatures. Low weights are assigned to parts of a source pressure wavefield spectrum where signatures are less reliable and higher weights are assigned to parts of the source pressure wavefield spectrum where signatures are more reliable. The part of the spectrum where both sets of signatures are reliable can be used for quality control and for comparing the measured near-field signatures to modeled near-field signatures. When there are uncertainties in the input parameters to the modeling, the input parameters can be scaled to minimize the differences between measured and modeled near-field signatures. Resultant near-field signatures are computed by a weighted summation of the modeled and measured near-field signatures, and notional source signatures are calculated from the resultant near-field signatures.

Disclosed are systems and methods for numerically simulating seismic-wave propagation in tilted transversely isotropic (TTI) media, using an adaptive Lebedev staggered grid. In various embodiments, the adaptive grid includes multiple horizontal zones having different associated grid spacings, which may be determined based on a vertical wave-velocity model. The numerical simulation may involve iteratively solving a set of finite-difference equations including finite-difference coefficients that vary spatially depending on the grid spacing. Additional embodiments and features are described.

A method includes receiving seismic data including signals collected using a receiver, the seismic data representing a subsurface volume, identifying a downgoing wavefield and an upgoing wavefield in the seismic data, identifying direct arrivals in the downgoing wavefield, estimating at least first-order multiple reflection signals in the upgoing wavefield based on the downgoing wavefield, the upgoing wavefield, and the direct arrivals, and generating seismic images representing the subsurface volume based at least in part on the at least first-order multiple reflection signals.