A Bayesian Approach to Uncertainty Quantification in Geophysical Basin Modeling

Abstract

Summary Geophysical basin modeling helps constrain the non-uniqueness of seismic velocity inversion methods by employing basin modeling to incorporate geo-history constraints into inversion. Traditionally, basin modeling is performed in a deterministic manner and thus does not facilitate uncertainty quantification. We present a Bayesian approach for propagation of basin modeling uncertainties into velocity models. Our methodology constitutes defining prior probability distributions on uncertain basin modeling parameters and likelihood models on basin modeling calibration data. Posterior realizations of basin models are generated by sampling the prior, performing Monte-Carlo basin simulations and evaluating the corresponding likelihood values. These posterior models are finally linked to velocity models by rock physics modeling. We demonstrate the applicability of our proposed workflow using a 2D real case study from Gulf of Mexico.