mlsanityData to 7 Oct 2026

Total-variation regularization strategies in full-waveform inversion

arXiv 1608.06159 ↗First posted 22 Aug 2016math.OCcited by 17

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Abstract (arXiv)

We propose an extended full-waveform inversion formulation that includes general convex constraints on the model. Though the full problem is highly nonconvex, the overarching optimization scheme arrives at geologically plausible results by solving a sequence of relaxed and warm-started constrained convex subproblems. The combination of box, total-variation, and successively relaxed asymmetric total-variation constraints allows us to steer free from parasitic local minima while keeping the estimated physical parameters laterally continuous and in a physically realistic range. For accurate starting models, numerical experiments carried out on the challenging 2004 BP velocity benchmark demonstrate that bound and total-variation constraints improve the inversion result significantly by removing inversion artifacts, related to source encoding, and by clearly improved delineation of top, bottom, and flanks of a high-velocity high-contrast salt inclusion. The experiments also show that for poor starting models these two constraints by themselves are insufficient to detect the bottom of high-velocity inclusions such as salt. Inclusion of the one-sided asymmetric total-variation constraint overcomes this issue by discouraging velocity lows to buildup during the early stages of the inversion. To the author's knowledge the presented algorithm is the first to successfully remove the imprint of local minima caused by poor starting models and band-width limited finite aperture data.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Aug 2016.

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Citing papers per year: 2022: 1; 2023: 2; 2024: 4; 2025: 3; 2026: 7.

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  1. 2022Implicit Full Waveform Inversion with Deep Neural Representationcited by 26
  2. 2024ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problemscited by 11
  3. 2023Solving multiphysics-based inverse problems with learned surrogates and constraintscited by 9
  4. 2024Paired Autoencoders for Likelihood-free Estimation in Inverse Problemscited by 6
  5. 2025RED-DiffEq: Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversioncited by 5
  6. 2024Real-Time Model-Based Quantitative Ultrasound and Radarcited by 2
  7. 2025Diffusion prior as a direct regularization term for FWIcited by 2
  8. 2026Dual-space posterior sampling for Bayesian inference in constrained inverse problemscited by 2
  9. 2026Decoupled Latent Optimization of Diffusion Models for Full Waveform Inversioncited by 2
  10. 2024GPR Full-Waveform Inversion through Adaptive Filtering of Model Parameters and Gradients Using CNNcited by 1
  11. 2026Unveiling the Mechanism of Continuous Representation Full-Waveform Inversion: A Wave Based Neural Tangent Kernel Frameworkcited by 1
  12. 2023Object based Bayesian full-waveform inversion for shear elastographycited by 0
5 more
  1. 2025Simultaneous Automatic Picking and Manual Picking Refinement for First-Breakcited by 0
  2. 2026Total Generalized Variation regularization closes the gap between neural-eld and classical methods in seismic travel-time tomographycited by 0
  3. 2026Deciphering Neural Reparameterized Full-Waveform Inversion with Neural Sensitivity Kernel and Wave Tangent Kernelcited by 0
  4. 2026Extending Occam's inversion with lasso fusion, overcomplete dictionaries, and isotropic total variation regularisationcited by 0
  5. 2026Physical-State-Guided Diffusion Sampling for Full-Waveform Inversioncited by 0