Full waveform inversion (FWI) reconstructs subsurface velocity models from seismic mea surements but faces the challenges of ill-posedness and nonlinearity, making accurate inversion dependent on suitable initialization and effective prior constraints. Classical methods stabi lize the inversion and improve convergence by designing hand-crafted regularization terms, yet these terms provide limited descriptions of complex geological structures. Recent diffusion based methods learn more detailed structural priors from data and integrate them into inverse problem solvers. We propose Physical-State-Guided Diffusion Sampling (PSG), which decouples diffusion posterior sampling into two mutually interacting processes: regularized FWI optimiza tion and conditional diffusion sampling with linear guidance. PSG shows overall improvements in reconstruction accuracy, preservation of prior structure, and robustness over classical and diffusion-based baselines across diverse OpenFWI inversion scenarios. Through domain decom position, PSG can generalize without retraining to more complex, realistic velocity fields with structural patterns distinct from those represented by the prior learned from training datasets and larger spatial domains than the training models. Overall, PSG offers a flexible framework for integrating learned priors with physical constraints, with potential for extension to other nonlinear inverse problems.

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