Scholarlink

<p>Full Waveform Inversion (FWI) stands as a nonlinear, high-resolution technology for subsurface imaging via surface-recorded data. This paper introduces an augmented Lagrangian dual formulation for FWI, rooted in the viewpoint that Lagrange multipliers serve as fundamental unknowns for the accurate linearization of the FWI problem. Once these multipliers are estimated, the determination of model parameters becomes simple. Therefore, unlike traditional primal algorithms, the proposed dual method circumvents direct engagement with model parameters or wavefields, instead tackling the estimation of Lagrange multipliers through a gradient ascent iteration. This approach yields two significant advantages: i) The background model remains fixed, requiring only one LU matrix factorization for each frequency inversion. ii) The convergence of the algorithm can be improved by leveraging techniques like quasi-Newton l-BFGS methods and Anderson acceleration. Numerical examples from acoustic and elastic FWI utilizing different benchmark models are provided, showing that the dual algorithm converges quickly and requires fewer computations than the standard primal algorithm.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Fast and automatic full waveform inversion by dual augmented Lagrangian

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月13日

查看全文
  • Kamal Aghazade,
  • Ali Gholami

摘要

Full Waveform Inversion (FWI) stands as a nonlinear, high-resolution technology for subsurface imaging via surface-recorded data. This paper introduces an augmented Lagrangian dual formulation for FWI, rooted in the viewpoint that Lagrange multipliers serve as fundamental unknowns for the accurate linearization of the FWI problem. Once these multipliers are estimated, the determination of model parameters becomes simple. Therefore, unlike traditional primal algorithms, the proposed dual method circumvents direct engagement with model parameters or wavefields, instead tackling the estimation of Lagrange multipliers through a gradient ascent iteration. This approach yields two significant advantages: i) The background model remains fixed, requiring only one LU matrix factorization for each frequency inversion. ii) The convergence of the algorithm can be improved by leveraging techniques like quasi-Newton l-BFGS methods and Anderson acceleration. Numerical examples from acoustic and elastic FWI utilizing different benchmark models are provided, showing that the dual algorithm converges quickly and requires fewer computations than the standard primal algorithm.