Post-Stack Seismic Inversion with Non-Convex Total Generalized Variation Regularization.
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| Title: | Post-Stack Seismic Inversion with Non-Convex Total Generalized Variation Regularization. |
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| Authors: | Zou, Jian1 (AUTHOR), Li, Lu1 (AUTHOR), Luo, Lan1 (AUTHOR), Gu, Jun1 (AUTHOR), Chen, Zhong1 (AUTHOR) chenzhong@yangtzeu.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1730. 22p. |
| Subjects: | Acoustic impedance, Mathematical regularization, Optimization algorithms, Petrophysics, Regularization parameter, Electronic data processing, Seismic traveltime inversion |
| Abstract: | Highlights: What are the main findings? Developed a non-convex TGV (NCTGV) model that integrates non-convex sparsity with higher-order structural preservation. Established global convexity conditions to ensure stable optimization and unique solutions via the ADMM algorithm. What are the implications of the main findings? Effectively eliminates staircase artifacts and blurring to produce sharper, high-resolution stratigraphic boundaries. Achieved superior quantitative accuracy and field-scale scalability for high-fidelity reservoir characterization. Post-stack seismic inversion can reconstruct high-resolution acoustic impedance (AI) models from band-limited and noisy seismic reflections, which is crucial for identifying underground structures and characteristics. Traditional regularization methods, including total variation (TV) and total generalized variation (TGV), are prone to oversmoothing and staircase artifacts, thereby limiting their effectiveness in complex geological environments. In this paper, we introduce a novel regularization method based on non-convex TGV (NCTGV), which integrates the classical TGV regularization into a convex non-convex framework. This integration enables the model to simultaneously promote sparsity and preserve higher-order structural continuity. The resulting seismic inversion model was effectively solved using the alternating direction method of multipliers (ADMM), with a provably convergent scheme adapted to the NCTGV structure. Numerical experiments demonstrated the improved performance of the proposed technique. Compared to existing regularization techniques such as TV, NCTV, and TGV, the NCTGV method achieved lower root-mean-square error (RMSE). It also obtained higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) scores, together with enhanced vertical resolution. Visual inspection confirmed that the NCTGV-inverted impedance models exhibited clearer stratigraphic boundaries and sharper geological features. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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