Abstract Seismic full waveform inversion (FWI) is a powerful tool for monitoring subsurface changes during carbon capture and storage (CCS) operations, but its ill-posed nature makes uncertainty quantification (UQ) essential for reliable interpretation. Sampling-based Bayesian methods such as Markov chain Monte Carlo (McMC) provide rigorous UQ but are computationally demanding. In conventional FWI, elastic properties are assigned to densely discretized space-filling cells, resulting in a high-dimensional parameterization that makes large-scale elastic FWI computationally infeasible. To address this challenge, we propose a rock-physics-guided parameter-reduction strategy that compactly represents the CO2 plume geometry using cubic splines controlled by a limited number of nodes. This parsimonious parameterization not only significantly reduces the number of model parameters and the forward simulations required for an effective UQ using sampling methods but also has the potential to improve the practicality and efficiency of other types of UQ methods. Numerical experiments on a cross-well synthetic scenario and a field-scale case based on the Aquistore storage site in Saskatchewan, Canada, demonstrate that the method efficiently reconstructs the plume shape and its extent and that it converges to consistent posterior distributions across multiple Markov chains.
Mohammadi et al. (Tue,) studied this question.