Abstract: A computer-implemented method for obtaining reconstructed seismic data for determining a subsurface feature, includes: determining an initial training velocity model, training a machine learning model based on first training seismic data and second training seismic data generated from the training velocity model, the first training seismic data corresponding to one or more first frequencies, the second training seismic data corresponding to one or more second frequencies lower than the one or more first frequencies, obtaining, based on measured seismic data and the machine learning model, reconstructed seismic data corresponding to the one or more second frequencies, generating a velocity model based on the measured seismic data, the reconstructed seismic data, and a full waveform inversion (FWI), and when the generated velocity model does not satisfy a preset condition, updating the training velocity model based on the generated velocity model, to obtain updated reconstructed seismic data for determining a s

Abstract: Methods and systems for adjusting migration gather are disclosed. A method for adjusting migration gather may include generating a first migration gather based on acquired seismic data. The method may also include generating a compensation volume based on reference data produced utilizing a reference seismic model. The method may further include applying the compensation volume to adjust the first migration gather.

Abstract: A computer-implemented method for determining a subsurface feature, includes: determining a first velocity model based on an initial velocity model; generating a second velocity model based on measured seismic data at one or more first frequencies, the first velocity model, and a full waveform inversion (FWI); and in response to the second velocity model not satisfying a preset condition, performing a seismic forward simulation on the second velocity model to generate simulated seismic data at one or more second frequencies lower than the one or more first frequencies; updating the first velocity model based on the simulated seismic data at the one or more second frequencies; and updating the second velocity model based on the measured seismic data at the one or more first frequencies, the updated first velocity model, and the FWI, to determine the subsurface feature.

Abstract: A memory-efficient Q-RTM computer method and apparatus for imaging seismic data is described. A seismic image may be formed from a memory-efficient Q-RTM module utilizing received attenuated seismic data. Seismic data is processed by the memory-efficient Q-RTM module to compensate for amplitude attenuation and phase velocity dispersion simultaneously during back-propagation in RTM. A negative quality factor, Q, is obtained by modifying the wave equation to compensate for amplitude attenuation. One or more dispersion optimization terms introduced to a wave equation for compensation of Q effects on the phase, solved by a finite difference algorithm, compensate for phase velocity change and further adjust amplitude attenuation compensation.

Abstract: A full wave inversion (FWI) may utilize an Amplitude-Frequency-Differentiation (AFD) or a Phase-Frequency-Differentiation (PFD) operation to form a velocity model of a subterranean formation utilizing recovered low wavenumber data. Received seismic data is processes to isolate two data signals at different frequencies. In an AFD operation, the two data signals are summed and the data of the envelope of the summed function is used for the FWI. In a PFD operation, the phase data of the quotient of the two data signals is used for the FWI. The FWI proceeds iteratively utilizing either the AFD or PFD data or with single frequency data until the cost function of the AFD or PFD is satisfied.

Abstract: A method for seismic modeling on a GPU includes creating a discretized model containing a volume enclosed by boundaries with arbitrary boundary conditions, dividing the discretized model into subsets, transferring a data to the GPU, initializing the data stored in a selected number of the subsets on the GPU, performing computation related with a number of time steps for the selected number of the subsets on the GPU, and substantially simultaneously computing, off-loading a finished subsets data from the GPU, and up-loading a new subsets data to the GPU. The provided method and corresponding system can reduce the amount of data stored in GPU memory by an order of magnitude, minimize the GPU device-to-host transfer bandwidth requirements, and reduce the GPU setup time.

Abstract: A method for seismic modeling on a GPU includes creating a discretized model containing a volume enclosed by boundaries with arbitrary boundary conditions, dividing the discretized model into subsets, transferring a data to the GPU, initializing the data stored in a selected number of the subsets on the GPU, performing computation related with a number of time steps for the selected number of the subsets on the GPU, and substantially simultaneously computing, off-loading a finished subsets data from the GPU, and up-loading a new subsets data to the GPU. The provided method and corresponding system can reduce the amount of data stored in GPU memory by an order of magnitude, minimize the GPU device-to-host transfer bandwidth requirements, and reduce the GPU setup time.