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Volume 68, Issue 8
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Ill‐posedness is one of the most common and intractable issues that arise when solving geophysical inverse problems. Ill‐posedness could be induced by various factors such as noise, band‐limited intrinsic property of seismic data and inappropriate forward operators. Regularization has been proven to be an effective method widely accepted for mitigating the adverse effects of ill‐posedness. Aiming to improve the stability and fidelity of the pre‐stack seismic inversion process, we implement the inversion in a Bayesian framework, with a logarithmic absolute criterion taken as a likelihood function, and an ‐norm metric as a priori constraint. Here, we exploit the linear approximation as the forward operator, and optimize the regularized misfit function by the alternating direction method of multipliers. Applications of the method to synthetic and real data sets yielded improved inversion results in terms of accuracy and resolution, and demonstrated the robustness of the method to noise.

© 2020 European Association of Geoscientists & Engineers © 2020 European Association of Geoscientists & Engineers
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2020-08-23
2024-04-24

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Pre‐stack seismic inversion based on ℓ1−2‐norm regularized logarithmic absolute misfit function
Geophysical Prospecting 68, 2419 (2020); https://doi.org/10.1111/1365-2478.13012
/content/journals/10.1111/1365-2478.13012
/content/journals/10.1111/1365-2478.13012
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  • Article Type: Research Article
Keyword(s): Logarithmic absolute likelihood function; Pre‐stack seismic inversion; ℓ1−2 prior distribution

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