@conference {witte2015IIPFWIspl, title = {Sparsity-promoting least-square migration with linearized Bregman and compressive sensing}, booktitle = {Inaugural Full-Waveform Inversion Workshop}, year = {2015}, note = {(Natal, Brazil)}, month = {08-09}, abstract = {We present a novel adaptation of a recently developed relatively simple iterative algorithm to solve large-scale sparsity-promoting optimization problems. Our algorithm is particularly suitable to large-scale geophysical inversion problems, such as sparse least-squares reverse-time migration or Kirchoff migration since it allows for a tradeoff between parallel computations, memory allocation, and turnaround times, by working on subsets of the data with different sizes. Comparison of the proposed method for sparse least-squares imaging shows a performance that rivals and even exceeds the performance of state-of-the art one-norm solvers that are able to carry out least-squares migration at the cost of a single migration with all data.}, keywords = {Bregman, compressed sensing, FWI, migration, sparsity promotion, time domain}, presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/witte2015IIPFWIspl/witte2015IIPFWIspl_pres.pdf}, author = {Philipp A. Witte and Ning Tu and Ernie Esser and Mengmeng Yang and Mathias Louboutin and Felix J. Herrmann} }