Advanced International Journal for Research

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Pixel-Level Uncertainty Quantification in CCS Seismic Monitoring via Monte Carlo Diffusion Sampling: Application to the Sleipner 4D Benchmark

Author(s) Mr. Sunil Manikani, Dr. Murad Yaghi
Country India
Abstract We introduce Monte Carlo Diffusion Sampling (MCDS), a method for generating pixel-level uncertainty maps from a pretrained diffusion representation model (PDRM) without modifying model architecture, adding dropout, or training an ensemble. By running N independent stochastic passes through the PDRM at a fixed intermediate noise timestep t, each with a fresh random noise realisation, we obtain a set of plausible seismic reconstructions whose pixel-wise standard deviation constitutes an uncertainty map. Applied to all 249 inline sections of the Sleipner 4D CO2 seismic benchmark, MCDS reveals: (1) a global mean pixel uncertainty sigma-bar = 0.0348 with narrow cross-inline variation (std = 0.0011), indicating consistent model calibration across the lateral extent of the survey; (2) substantial within-inline spatial heterogeneity, with peak pixel uncertainty reaching 0.22 (6.3x the global mean), concentrating on structurally complex amplitude zones; (3) strong Pearson correlation (r = 0.738) between inline amplitude heterogeneity and mean inline uncertainty, confirming that the PDRM is uncertain precisely where the seismic data is most complex; and (4) a spatially coherent elevated-uncertainty cluster at inlines 174-184, coinciding with the highest amplitude variability in the survey. We argue that MCDS probability maps provide a practical, annotation-free monitoring diagnostic for CCS operations: anomalous uncertainty excursions between seismic vintages may flag zones where CO2 migration has shifted seismic character outside the model training distribution.
Keywords diffusion, deep learning, ccs, ai, seismic machine learning
Field Computer Applications
Published In Volume 7, Issue 4, July-August 2026
Published On 2026-08-18
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E-ISSN 3048-7641
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