Abstract
Mathematically, full waveform inversion is a nonlinear and ill-posed inverse problem, requiring a regularization method to obtain a reasonable result. Total variation regularization is an effective regularization method which can preserve the sharp edges of the solution. It is well-known that the standard total variation regularization usually leads to stair-casing artifacts in slanted structures. Thus, the standard total variation regularization may not be effective in solving the full waveform inverse problem, due to the slanted properties of the subsurface structures. In this paper, we propose a rotational total variation regularization method based on the weighting rotational transform operator and the standard total variation regularization for the full waveform inverse problem. To further improve the resolution of the inverted results for different directional structures, a hybrid regularization method combining the rotational and standard total variation regularization is proposed. An efficient version of the conjugate gradient method, i.e., CGOPT, is used to efficiently solve the proposed methods. Numerical experiments based on Sigsbee and Marmousi2 models are carried out to demonstrate the effectiveness of our methods.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 12261021
Award Identifier / Grant number: 11801111
Funding source: China Postdoctoral Science Foundation
Award Identifier / Grant number: 2019M650831
Funding statement: The authors very much appreciate the financial support from the National Natural Science Foundation of China (Grant No. 12261021 and No. 11801111) and China Postdoctoral Science Foundation (Grant No. 2019M650831). The authors also would like to acknowledge the support of the project funded by the Guizhou Science and Technology Plan Project ([2019]1122), Guizhou Science and Technology Platform talents ([2018]5781).
A One-dimensional deblurring example with TV regularization
For the test of one-dimensional (1D) experiments, the Fredholm integral equation of the first type of the convolution type is considered, which is represented as
In this examples,
where C and γ are positive parameters. In order to numerical solve this integral equation inverse problem, we should discretize this equation by using a numerical method to obtain a discrete linear system
where
where
In order to solve (A.4) by using an optimization method, we adopt the standard nonlinear conjugate gradient (NCG) algorithm [13]. In this numerical experiment, the true solution
B Proof of the theorems of rotation total variation method
B.1 The proof of Theorem 2.1
Proof.
(i) Since
Due to
To show the reverse inequality, we take
and observe that
and
arbitrarily closely.
For (ii) and (iii):
For any
Taking the supremum over
where
B.2 The proof of Theorem 2.2
Proof.
(i) Let
(B.6)
Taking the supremum over
(ii)
For any
(B.8)
Taking the supremum in the above inequality over
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© 2025 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Local convergence of the error-reduction algorithm for real-valued objects
- A rotation total variation regularization for full waveform inversion
- Stochastic data-driven Bouligand–Landweber method for solving non-smooth inverse problems
- On determining the fractional exponent of the subdiffusion equation
- Tow-parameter quasi-boundary value method for a backward abstract time-degenerate fractional parabolic problem
- Determining both leading coefficient and source in a nonlocal elliptic equation
- Discrete dynamical systems: Inverse problems and related topics
- Stability estimates for an inverse problem of determining time-dependent coefficients in a system of parabolic equations
- Numerical solutions to inverse nodal problems for the Sturm–Liouville operator and their applications
- Revisiting linear machine learning through the perspective of inverse problems
Articles in the same Issue
- Frontmatter
- Local convergence of the error-reduction algorithm for real-valued objects
- A rotation total variation regularization for full waveform inversion
- Stochastic data-driven Bouligand–Landweber method for solving non-smooth inverse problems
- On determining the fractional exponent of the subdiffusion equation
- Tow-parameter quasi-boundary value method for a backward abstract time-degenerate fractional parabolic problem
- Determining both leading coefficient and source in a nonlocal elliptic equation
- Discrete dynamical systems: Inverse problems and related topics
- Stability estimates for an inverse problem of determining time-dependent coefficients in a system of parabolic equations
- Numerical solutions to inverse nodal problems for the Sturm–Liouville operator and their applications
- Revisiting linear machine learning through the perspective of inverse problems