Assessment of uncertainty propagation within exhumation studies using Bayesian inference

Patrick Makuluni, Juerg Hauser, Stuart Clark

Exhumation significantly influences resource distribution in sedimentary basins, impacting mineral and energy exploration. Accurate exhumation estimates, derived primarily from empirical equations based on compaction and thermal datasets, are essential but are often compromised by data errors and unquantified uncertainties in model parameters.

Our recently published study in Tectonophysics (Makuluni et al., 2025) introduces a novel and refined approach to exhumation estimation using Markov Chain Monte Carlo (MCMC) methods to quantify and address uncertainties in data and model parameters. Using this approach, we developed a workflow for quantifying exhumation magnitudes and their associated uncertainties and applied it to sonic log datasets from the Canning and Bonaparte Basins. Uncertainty propagation analysis reveals that considering data-related and model uncertainties together produces variable distributions of exhumation estimates with wider uncertainty ranges. Overall, data quality and coverage proved more critical for the accuracy and precision of exhumation estimates than model refinement. Our models can be integrated into basin evolution studies, help refine fluid migration models, and improve understanding of sedimentation and ore preservation to optimise resource exploration in sedimentary basins