# Curvelet-based primary-multiple separation from a Bayesian perspective

 Title Curvelet-based primary-multiple separation from a {Bayesian} perspective Publication Type Conference Year of Publication 2008 Authors Rayan Saab Conference Name SINBAD 2008 Keywords Presentation, SINBAD, SLIM 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 assumes noisy input data and can be derived from a Bayesian perspective. More precisely, the optimization problem can be arrived at via an assumption of a weighted Laplacian distribution for the primary and multiple coefficients in the transform domain and of white Gaussian noise contaminating both the seismic data and the preliminary prediction of the multiples, which both serve as input to the algorithm. URL https://www.slim.eos.ubc.ca/Publications/Private/Conferences/SINBAD/2008/saab2008SINBADcps/saab2008SINBADcp.pdf Citation Key saab2008SINBADcps