| 摘要 |
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. |
| 主权项 |
1. A process for overcoming aliasing in a minimum weighted norm interpolation (MWNI) technique, the process comprising:
computing, by a processor, an initial, regularly interpolated model; computing a plurality of initial spectral weights using the initial, regularly interpolated model; converting the initial, regularly interpolated model into a frequency domain, and, unknown spectral weights Pk in x′=|(THT+μF−1|Pk|−2F)−1THd, a from frequency data at each frequency slice of the initial, regularly interpolated model using Fourier transform, wherein μ is a weighing factor controlling tradeoff between model norm and misfit of observations, H is a conjugate transpose operator, xH is a conjugate transpose of x, F is a multi-dimensional forward Fourier transform, F−1 is a multi-dimensional inverse Fourier transform, and x′ is a least-squared solution with a minimum weighted norm (MWNI) to recover missing data; wherein the process results in reducing aliasing artifacts and improving reconstruction of multi-dimensional data regularization from otherwise irregular data. |
