Fast “Online” Migration with Compressive Sensing

References

1. Beck, A. and Teboulle, M.
   [2009] A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Img. Sci., 2(1), 183–202, ISSN 1936-4954, doi:10.1137/080716542.
   https://doi.org/10.1137/080716542 [Google Scholar]
2. Hennenfent, G., van den Berg, E., Friedlander, M.P. and Herrmann, F.J.
   [2008] New insights into one-norm solvers from the Pareto curve. Geophysics, 73(4), A23–A26, doi:10.1190/1.2944169.
   https://doi.org/10.1190/1.2944169 [Google Scholar]
3. Herrmann, F.J. and Li, X.
   [2012] Efficient least-squares imaging with sparsity promotion and compressive sensing. Geophysical Prospecting, 60(4), 696–712, doi:10.1111/j.1365‑2478.2011.01041.x.
   https://doi.org/10.1111/j.1365-2478.2011.01041.x [Google Scholar]
4. Lorenz, D.A., Schöpfer, F. and Wenger, S.
   [2013] The Linearized Bregman Method via Split Feasibility Problems: Analysis and Generalizations. ArXiv e-prints.
   [Google Scholar]
5. Lorenz, D.A., Wenger, S., Schöpfer, F. and Magnor, M.
   [2014] A sparse Kaczmarz solver and a linearized Bregman method for online compressed sensing. ArXiv e-prints.
   [Google Scholar]
6. Needell, D. and Tropp, J.A.
   [2014] Paved with good intentions: Analysis of a randomized block kaczmarz method. Linear Algebra and its Applications, 441(0), 199–221, ISSN 0024-3795, doi:http://dx.doi.org/10.1016/j.laa.2012.12.022, special Issue on Sparse Approximate Solution of Linear Systems.
   [Google Scholar]
7. Strohmer, T. and Vershynin, R.
   [2009] A randomized kaczmarz algorithm with exponential convergence. Journal of Fourier Analysis and Applications, 15(2), 262–278, ISSN 1069-5869, doi:10.1007/s00041‑008‑9030‑4.
   https://doi.org/10.1007/s00041-008-9030-4 [Google Scholar]
8. Tu, N. and Herrmann, F.J.
   [2014] Fast imaging with surface-related multiples by sparse inversion, accepted for Geophysical Journal International.
   [Google Scholar]
9. van den Berg, E. and Friedlander, M.P.
   [2008] Probing the pareto frontier for basis pursuit solutions. SIAM Journal on Scientific Computing, 31(2), 890–912, doi:10.1137/080714488.
   https://doi.org/10.1137/080714488 [Google Scholar]
10. van Leeuwen, T. and Herrmann, F.J.
    [2013a] Fast waveform inversion without source encoding. Geophysical Prospecting, 61, 10–19, doi:10.1111/j.1365‑2478.2012.01096.x, article first published online: 10JULY2012.
    https://doi.org/10.1111/j.1365-2478.2012.01096.x [Google Scholar]
11. van Leeuwen, T. and Herrmann, F.J.
    [2013b] Mitigating local minima in full-waveform inversion by expanding the search space. Geophysical Journal International, 195, 661–667, doi:10.1093/gji/ggt258.
    https://doi.org/10.1093/gji/ggt258 [Google Scholar]
12. Yin, W., Osher, S., Goldfarb, D. and Darbon, J.
    [2008] Bregman iterative algorithms for ℓ1-minimization with applications to compressed sensing. SIAM Journal on Imaging Sciences, 1(1), 143–168, doi:10.1137/070703983.
    https://doi.org/10.1137/070703983 [Google Scholar]