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Volume 28, Issue 1
Estimating Primaries by Sparse Inversion with Cost-Effective Computation

Xiaopeng Zhou, Yike Liu & Lanshu Bai

DOI: 10.4208/cicp.OA-2018-0065

Commun. Comput. Phys., 28 (2020), pp. 477-497.

Published online: 2020-05

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  • Abstract

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.

  • Keywords

Inverse problem, multiple removal, primary estimation, impulse response.

  • AMS Subject Headings

65K10, 86-08, 86-A22

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address

zhouxiaopeng@mail.iggcas.ac.cn (Xiaopeng Zhou)

ykliu@mail.iggcas.ac.cn (Yike Liu)

bailanshu@seis.ac.cn (Lanshu Bai)

  • BibTex
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  • TXT
@Article{CiCP-28-477, author = {Zhou , XiaopengLiu , Yike and Bai , Lanshu}, title = {Estimating Primaries by Sparse Inversion with Cost-Effective Computation}, journal = {Communications in Computational Physics}, year = {2020}, volume = {28}, number = {1}, pages = {477--497}, abstract = {

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.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2018-0065}, url = {http://global-sci.org/intro/article_detail/cicp/16849.html} }
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 -

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.

Xiaopeng Zhou, Yike Liu & Lanshu Bai. (2020). Estimating Primaries by Sparse Inversion with Cost-Effective Computation. Communications in Computational Physics. 28 (1). 477-497. doi:10.4208/cicp.OA-2018-0065
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