Towards Digital Twins: Uncertainty and Sensitivity Analysis for Safety-Case Modelling

Deep geological repositories rely on robust, transparent, and scientifically based safety concepts to ensure the long-term safety of radioactive waste. As safety cases become increasingly data-rich and computationally integrated, Digital Twins are emerging as a powerful tool to represent, test, and communicate the behavior of complex geosystems over geological timescales. A core requirement for such Digital Twins is the explicit quantification of parameter uncertainties and sensitivities, ensuring that the model is both reliable and efficient in reproducing key safety functions.

In this contribution, we introduce a workflow designed to assess uncertainties and sensitivities associated with radionuclide retention in geological host formations. Our approach combines geostatistical as well as geochemical simulation and global sensitivity analysis. Mineralogical heterogeneity is represented using geostatistical realizations generated through custom Python implementations of Markov-chain methods and truncated Gaussian random field simulations, producing spatially realistic mineral distributions. These mineralogical scenarios are then propagated through a geochemical modelling step using Geochemist Workbench, in which the distribution coefficient (K_d) is computed for each realization to quantify the effect of mineralogical and geochemical variability on uranium retention.

To identify the key indicators of variability, the workflow incorporates variance-based sensitivity analysis (SA) based on a custom Python toolbox. The SA reveals both first- and second-order effects, highlighting the influence of individual parameters on the resulting K_d values as well as pairwise parameter interactions. In almost all cases, the identified sensitivities and interactions can be explained by underlying chemical and physical processes. Additionally, this approach enables targeted dimensionality reduction, a critical step for constructing Digital Twins that maintain scientific robustness while remaining computationally tractable.

The workflow is presented for crystalline host rocks, where we focus on uranium retention within granitic systems governed by solid–liquid interactions: sorption, aqueous speciation, precipitation, and dissolution. A key advantage of our workflow is its modular structure. Each component, geostatistical simulation, geochemical modelling, and sensitivity analysis, can be independently adapted, extended, or replaced. This makes the framework readily transferable to other host rocks such as salt or clay, which exhibit fundamentally different retention mechanisms, as well as to other radionuclides with distinct sorption, solubility, or redox characteristics.

Our results highlight (i) the magnitude of uncertainty introduced by mineralogical heterogeneity, (ii) the non-linear sensitivity of uranium retention to coupled mineral–solution systems, and (iii) the potential to substantially reduce model complexity by focusing on a small subset of high-impact parameters. Overall, the workflow provides a structured and scalable method for quantifying uncertainties and identifying the parameters most relevant to long-term safety. In this way, it provides the essential, uncertainty-aware input data required for the generation of reliable and computationally efficient Digital Twins in geological disposal scenarios.