Decoupled Latent Optimization of Diffusion Models for Full Waveform Inversion

• URL: http://arxiv.org/abs/2606.14139v1
• Date: Fri, 12 Jun 2026 05:55:10 GMT
• Title: Decoupled Latent Optimization of Diffusion Models for Full Waveform Inversion
• Abstract summary: Full waveform inversion (FWI) recovers subsurface velocity from recordings by solving a severely ill-posed, nonconstrained quadratic PDE-velocity optimization.<n>Recent diffusion-prior methods improve realism at the cost of a fragile trade-off between the standard formulation and prior consistency.<n>We propose De Latent Optimization (DLO), which relaxes the latent-penalty objective into a latent-penalty objective over an auxiliary physical variable and a latent variable.<n>On the OpenFWI benchmark, DLO outperforms classical regularizers and existing diffusion-based methods under clean, noisy, and missing-trace.
• Score: 4.293965179144764
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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