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Diffusion models for multivariate subsurface generation and efficient probabilistic inversion
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DOI：10.1016/j.cageo.2025.106076.png)
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Abstract
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• Diffusion models enable accurate multivariate modeling of hydro-geological properties. • They significantly outperform variational autoencoders and generative adversarial networks. • We adapt diffusion posterior sampling for Bayesian inference, modifying the generative diffusion by an approximate likelihood score. • The inversion method works well for both local and indirect geophysical data, in linear and nonlinear settings. • Compared to benchmarks, we demonstrate improved accuracy, robustness and reduced computational costs.
Keywords:
Diffusion models
Diffusion Posterior Sampling
Bayesian inversion
Geophysics
Multivariate modeling
Subsurface characterization
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