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A Full-Waveform Inversion Method Based on Structural Tensor Constraints
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DOI：10.1109/TGRS.2025.3532078.png)
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Abstract
En 中文
Full-waveform inversion (FWI) is a method for obtaining velocity models. Because of its theoretical completeness, the final modeling results are often superior to those of velocity modeling methods such as stacking velocity analysis, migration velocity analysis, and velocity tomography. FWI usually takes the L2 norm of the residuals between observed and simulated data as the objective function. The current velocity model that best fits the observed data is obtained by solving the objective function. However, during FWI, there is a strong ill-posedness due to its limited conditions. To solve this problem, regularized constraints or gradient preconditioning methods are commonly introduced. In this article, we propose a regularization method that incorporates prior information, aiming to alleviate the aforementioned issues to some extent. This method adds a regularization term based on structural tensors to the original L2 norm objective function, introducing subsurface structural information in conventional regularized constraints of FWI. It smooths the velocity model along layer interfaces to ensure that the obtained inversion results are more consistent with the true velocity model. The inversion results from synthetic data of the Marmousi model and BP 2004 benchmark velocity model demonstrate the feasibility and effectiveness of this method. The noisy data FWI further demonstrates the robustness of this method. Moreover, the proposed method can better recover the small-scale structures in the velocity model and significantly improve the resolution of the velocity model.
Keywords:
Linear programming
Tensors
Computational modeling
Data models
Stratigraphy
Optimization
Geoscience and remote sensing
Eigenvalues and eigenfunctions
Accuracy
Computational efficiency
Full-waveform inversion (FWI)
objective function
regularization
structure tensor
velocity modeling
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W