| Popis: |
SUMMARY We propose a greedy inversion method for sparse linear problems. The kernel of the method is based on a conventional iterative algorithm, conjugate gradients (CG), but it is utilized adaptively in amplitude-prioritized local model spaces, giving rise to a greedy algorithm. The adaptive inversion introduces a coherence-oriented mechanism to enhance focusing of significant model parameters, and hence increases both the image resolution and the convergence rate. We adopt the idea in a time-space domain high-resolution parabolic Radon transform for multiple attenuation and a local linear Radon transform for data interpolation. Synthetic and real data examples show that the method can yield high quality solutions at much lower cost than existing standard methods. |
