Abstract: A process for overcoming aliasing using a minimum weighted norm interpolation (MWNI) technique may include computing an initial, regularly interpolated model with no data gaps and computing a plurality of initial spectral weights using the initial, regularly interpolated model. The initial, regularly interpolated model is used to compute the spectral weights as initial constraints in a least-squares solution methodology. The initial spectral weights are used as initial constraints in a constrained minimum weighted norm interpolation data reconstruction. The process may further include converting the initial, regularly interpolated model into a frequency domain and computing unknown spectral weights from frequency data at each frequency slice of the initial, regularly interpolated model using Fourier transform. The process results in reducing aliasing artifacts and improving data regularization.

Abstract: A process for overcoming aliasing using a minimum weighted norm interpolation (MWNI) technique may include computing an initial, regularly interpolated model with no data gaps and computing a plurality of initial spectral weights using the initial, regularly interpolated model. The initial, regularly interpolated model is used to compute the spectral weights as initial constraints in a least-squares solution methodology. The initial spectral weights are used as initial constraints in a constrained minimum weighted norm interpolation data reconstruction. The process may further include converting the initial, regularly interpolated model into a frequency domain and computing unknown spectral weights from frequency data at each frequency slice of the initial, regularly interpolated model using Fourier transform. The process results in reducing aliasing artifacts and improving data regularization.

Abstract: A method and system of seismic data processing includes acquiring first and second prestack seismic datasets wherein the seismic datasets have a time-lapse relationship. The first prestack seismic dataset is inverted to determine a first source wavelet data set and a first reflectivity parameters data set. A perturbation data set is obtained by simultaneously minimizing an Lp-Norm between a portion of the first reflectivity parameters data set and reflectivity parameters for the second data set, an Lp-Norm between the first source wavelet data set and a source wavelet for the second data set and an Lp-Norm for data misfit between the first prestack seismic data set and the second prestack seismic data set. The perturbation dataset may be inverted for petrophysical parameters and for determining time-lapse changes in a reservoir.

Abstract: A method and system of seismic data processing includes acquiring first and second prestack seismic datasets wherein the seismic datasets have a time-lapse relationship. The first prestack seismic dataset is inverted to determine a first source wavelet data set and a first reflectivity parameters data set. A perturbation data set is obtained by simultaneously minimizing an Lp-Norm between a portion of the first reflectivity parameters data set and reflectivity parameters for the second data set, an Lp-Norm between the first source wavelet data set and a source wavelet for the second data set and an Lp-Norm for data misfit between the first prestack seismic data set and the second prestack seismic data set. The perturbation dataset may be inverted for petrophysical parameters and for determining time-lapse changes in a reservoir.