aiqbench analysis
This research introduces Decoupled Latent Optimization (DLO), a novel method for full waveform inversion that integrates diffusion models with physical constraints to overcome the trade-off between data fidelity and geological realism. By decoupling gradient updates in physical space from latent variable sampling, DLO demonstrates robust performance across noisy datasets while preserving intricate fault structures on standard benchmarks like Marmousi. The approach represents a significant advancement in applying generative AI techniques to solve ill-posed partial differential equations in geophysics without relying solely on fragile prior consistency methods.
Decoupled Latent Optimization of Diffusion Models for Full Waveform Inversion
arXiv:2606.14139v1 Announce Type: new Abstract: Full waveform inversion (FWI) recovers subsurface velocity from seismic recordings by solving a severely ill-posed, nonconvex PDE-constrained optimization. Classical regularizers stabilize the inversion but fail to reproduce realistic geological structures; recent diffusion-prior methods improve realism at the cost of a fragile trade-off between data fidelity and prior consistency. We propose Decoupled Latent Optimization (DLO), which relaxes the standard latent-optimization formulation into a quadratic-penalty objective over an auxiliary physical variable and a latent variable. The data-fidelity gradient acts in physical space, the diffusion sampler contributes only through a decoded prior sample, and the standard smoothed-velocity initialization of classical FWI is preserved. On the OpenFWI benchmark, DLO outperforms classical regularizers and existing diffusion-based methods under clean, noisy, and missing-trace acquisitions. The prior, trained on 70*70 OpenFWI models, transfers directly to the Marmousi and Overthrust benchmarks, where DLO recovers intricate fault structures and remains robust to initialization smoothing and measurement noise.
Abstract:Full waveform inversion (FWI) recovers subsurface velocity from seismic recordings by solving a severely ill-posed, nonconvex PDE-constrained optimization. Classical regularizers stabilize the inversion but fail to reproduce realistic geological structures; recent diffusion-prior methods improve realism at the cost of a fragile trade-off between data fidelity and prior consistency. We propose Decoupled Latent Optimization (DLO), which relaxes the standard latent-optimization formulation into a quadratic-penalty objective over an auxiliary physical variable and a latent variable. The data-fidelity gradient acts in physical space, the diffusion sampler contributes only through a decoded prior sample, and the standard smoothed-velocity initialization of classical FWI is preserved. On the OpenFWI benchmark, DLO outperforms classical regularizers and existing diffusion-based methods under clean, noisy, and missing-trace acquisitions. The prior, trained on 70*70 OpenFWI models, transfers directly to the Marmousi and Overthrust benchmarks, where DLO recovers intricate fault structures and remains robust to initialization smoothing and measurement noise.
Machine Learning (cs.LG)
arXiv:2606.14139 [cs.LG]
(or arXiv:2606.14139v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.14139 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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