mlsanityData to 14 Oct 2026

Integrating Score-Based Diffusion Models with Machine Learning-Enhanced Localization for Advanced Data Assimilation in Geological Carbon Storage

arXiv 2511.05266 ↗First posted 7 Nov 2025cs.LG cs.AIcited by 1

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Accurate characterization of subsurface heterogeneity is important for the safe and effective implementation of geological carbon storage (GCS) projects. This paper explores how machine learning methods can enhance data assimilation for GCS with a framework that integrates score-based diffusion models with machine learning-enhanced localization in channelized reservoirs during CO2 injection. We employ a machine learning-enhanced localization framework that uses large ensembles (Ns = 5000) with permeabilities generated by the diffusion model and states computed by simple ML algorithms to improve covariance estimation for the Ensemble Smoother with Multiple Data Assimilation (ESMDA). We apply ML algorithms to a prior ensemble of channelized permeability fields, generated with the geostatistical model FLUVSIM. Our approach is applied on a CO2 injection scenario simulated using the Delft Advanced Research Terra Simulator (DARTS). Our ML-based localization maintains significantly more ensemble variance than when localization is not applied, while achieving comparable data-matching quality. This framework has practical implications for GCS projects, helping improve the reliability of uncertainty quantification for risk assessment.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 22 of its 68 references that are in the map. First posted on arXiv 7 Nov 2025.

Builds on

22 of 68 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Latent diffusion models for parameterization and data assimilation of facies-based geomodels21 Jun

Other references · 21

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
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  6. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
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9 more
  1. 2022Why do tree-based models still outperform deep learning on tabular data?cited by 199
  2. 2020Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 modelcited by 74
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  4. 2020Combining data assimilation and machine learning to infer unresolved scale parametrisationcited by 40
  5. 2020Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximizationcited by 38
  6. 2021Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Componentscited by 18
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniquescited by 0

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