TY - JOUR
T1 - Estimating Primaries by Sparse Inversion with Cost-Effective Computation
AU - Zhou , Xiaopeng
AU - Liu , Yike
AU - Bai , Lanshu
JO - Communications in Computational Physics
VL - 1
SP - 477
EP - 497
PY - 2020
DA - 2020/05
SN - 28
DO - http://doi.org/10.4208/cicp.OA-2018-0065
UR - https://global-sci.org/intro/article_detail/cicp/16849.html
KW - Inverse problem, multiple removal, primary estimation, impulse response.
AB - <p style="text-align: justify;">Recently, attenuation of surface-related multiples is implemented by a large-scale sparsity-promoting inversion where the primaries are iteratively estimated without a subtraction process, which is called estimation of primaries by sparse inversion
(EPSI). By inverting for surface-free impulse responses, EPSI simultaneously updates
the primaries and multiples, both of which contribute to explaining the input data,
and therefore promote the global convergence gradually. However, one of the major concerns of EPSI may lie in its high computational cost. In this paper, based on
the same gradient-descent framework with EPSI, we develop a computationally cost-effective primary estimation approach in which a newly defined parameterization of
primary-multiple model is adopted and an efficiently defined analytical step-length
is developed. The developed approach can yield a better primary estimation at less
computational cost as compared to EPSI, which is verified by two synthetic datasets
in numerical examples. Moreover, we apply this approach to a shallow-water field
dataset and achieve a desirable performance.</p>