Curvelet-based primary-multiple separation from a Bayesian perspective

  title={Curvelet-based primary-multiple separation from a Bayesian perspective},
  author={Rayan Saab and Deli Wang and {\"O}zg{\"u}r Yılmaz and Felix J. Herrmann},
In this abstract, we present a novel primary-multiple separation scheme which makes use of the sparsity of both primaries and multiples in a transform domain, such as the curvelet transform, to provide estimates of each. The proposed algorithm utilizes seismic data as well as the output of a preliminary step that provides (possibly) erroneous predictions of the multiples. The algorithm separates the signal components, i.e., the primaries and multiples, by solving an optimization problem that… CONTINUE READING