Latent diffusion model parameterization allows MCMC and SMC to outperform latent-space ESMDA in data mismatch and uncertainty reduction for 3D subsurface DA, while model-space ESMDA produces unrealistic posteriors.
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Introduces power-law, logistic, and discrepancy-based tapers for correlation-based localization that suppress spurious correlations and often preserve more posterior ensemble variance than distance-based methods in synthetic reservoir assimilation tests.
Machine-learning-based distance-free localization and a prior-covariance correction mitigate variance loss in ensemble data assimilation, tested on reservoir and CO2 storage models.
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Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniques
Latent diffusion model parameterization allows MCMC and SMC to outperform latent-space ESMDA in data mismatch and uncertainty reduction for 3D subsurface DA, while model-space ESMDA produces unrealistic posteriors.
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Statistical Tapers for Correlation-Based Localization in Ensemble Data Assimilation
Introduces power-law, logistic, and discrepancy-based tapers for correlation-based localization that suppress spurious correlations and often preserve more posterior ensemble variance than distance-based methods in synthetic reservoir assimilation tests.
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Mitigating loss of variance in ensemble data assimilation: machine learning-based and distance-free localization
Machine-learning-based distance-free localization and a prior-covariance correction mitigate variance loss in ensemble data assimilation, tested on reservoir and CO2 storage models.