2012/07/02 by Ardeshir M. Ebtehaj, Ebtehaj, Ardeshir M., Efi Foufoula‐Georgiou +5
Computer Science · Earth and Planetary Sciences · #Image and Signal Denoising Methods #Meteorological Phenomena and Simulations #Seismic Imaging and Inversion Techniques
paper · pdf · doi:10.48550/arxiv.1207.0454
This paper proposes an extension to the classical 3D variational data assimilation approach by explicitly incorporating as a prior information, the transform-domain sparsity observed in a large class of geophysical signals. In particular, the proposed framework extends the maximum likelihood estimation of the analysis state to the maximum a posteriori estimator, from a Bayesian perspective. The promise of the methodology is demonstrated via application to a 1D synthetic example.