The Linearized Bregman Method for Frugal Full-waveform Inversion with Compressive Sensing and Sparsity-promoting

Chai, Xintao; Tang, Genyang; Peng, Ronghua; Liu, Shaoyong

doi:10.1007/s00024-017-1734-4

Cited by 12 publications

(1 citation statement)

References 58 publications

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“…Although this method can solve the instability problem of seismic inversion well, there are some problems such as low accuracy and too smooth inversion results (Berkhout, 1977). And in terms of sparse representation, the L1 norm is better than the L2 norm (Li et al, 2012;Kong et al, 2016;Chai et al, 2018). Liu et al (2015) and Yuan et al (2015) verify the validity of the L1 norm in inversion by using 2D model and 3D data respectively, and both confirm that the inversion method with the L1 norm as a regularization constraint has higher inversion accuracy.…”

Section: Introductionmentioning

confidence: 74%

Data-driven acoustic impedance inversion with reweighted L1 norm sparsity constraint

Zhao

Kai

et al. 2023

Front. Earth Sci.

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Acoustic impedance (AI) inversion is widely used in geophysics and reservoir prediction. But the traditional impedance inversion method cannot fully exploit the sparse characteristics of geological attributes. There are problems with multiplicity and low resolution. To solve this problem, a data-driven acoustic impedance inversion method with reweighted L1 norm constraints (DRL1) is proposed. In the inversion process, the reweighted L1 norm and local cross-correlation analysis are introduced to solve the above problems. The reweighted L1 norm is introduced as a sparse constraint (RL1) to replace the traditional inversion method which is constrained by L1 norm. The RL1 method can describe more sparsity information and improve the resolution of inversion. In addition, the quality of seismic data plays a decisive role in seismic inversion. We add local cross-correlation analysis to the inversion process. We evaluated the rationality of each sampling point in the seismic data by introducing cross-correlation analysis, controlling for their contribution to the inversion, making inversion results more stable and accurate. The inversion objective function is solved by the alternating direction multiplier method (ADMM) algorithm and soft threshold shrinkage algorithm. Finally, we validate the effectiveness of the proposed method through model tests and field data. The results show that our proposed method not only provides a more accurate portrayal of the stratigraphy, but also yields more accurate inversion results.

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