Assessment of uncertainty propagation within compaction-based exhumation studies using Bayesian inference

Abstract

Exhumation plays a crucial role in shaping the evolution and distribution of resource systems in sedimentary basins, affecting mineral and energy resource 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. For instance, model parameters are usually assumed not to be affected by uncertainties despite varying within measurable ranges. Uncertainties from such variation can propagate and compromise the accuracy of exhumation estimates.

This study 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. The impact of uncertainty propagation on exhumation results was assessed by examining four scenarios: assuming no uncertainty in the model or data, considering data noise without model uncertainty, considering model uncertainty without data noise, and considering model uncertainties and data noise together.

Our study yielded robust exhumation estimates in the Canning and Bonaparte Basins. Comparison with previous studies shows similarities and differences in exhumation estimates for multiple episodes, with discrepancies potentially arising from variations in exhumation models, data quality and coverage. 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.