Sparsity-promoting least-square migration with linearized Bregman and compressive sensing

TitleSparsity-promoting least-square migration with linearized Bregman and compressive sensing
Publication TypeConference
Year of Publication2015
AuthorsPhilipp A. Witte, Ning Tu, Ernie Esser, Mengmeng Yang, Mathias Louboutin, Felix J. Herrmann
Conference NameInaugural Full-Waveform Inversion Workshop
KeywordsBregman, compressed sensing, FWI, migration, sparsity promotion, time domain

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.


(Natal, Brazil)

Citation Keywitte2015IIPFWIspl