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This is a Quantitative Study study.

October 15, 2025Open Access

Generative modeling of seismic data using diffusion models and its application to multi-purpose posterior sampling for noisy inverse problems

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The proposed method efficiently generates diverse stochastic solutions from seismic data, improving uncertainty quantification.
Our diffusion model uses an enhanced noise schedule, achieving fast, quality-controllable unconditional sampling.
A novel posterior sampling method adapts to various noise levels while requiring minimal function evaluations.
Experiments confirm superior out-of-distribution generalization and competitive performance in modeling seismic data.

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This approach reveals stochastic solutions in noisy observations, enhancing posterior sampling for inverse problems.

Meng et al. (2025) studied this question.

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