StratoBayes: A Bayesian method for automated stratigraphic correlation and age modelling

Abstract. Stratigraphic correlation and age modelling are fundamental to reconstructing Earth’s history, biological evolution, and palaeoclimate, and underpin the exploration for subsurface resources. Correlations are produced by integrating diverse stratigraphic data across multiple sites, typically by visual inspection. Here, we introduce ‘StratoBayes’, a Bayesian statistical framework that combines stratigraphic correlation and depositional age estimation of stratigraphic horizons, i.e. age modelling. Our method aligns quantitative signals from two or more sites by shifting and scaling, allowing for sedimentation rate changes between stratigraphic partitions. The likelihood of an alignment is evaluated by how well the adjusted signals conform to a shared smooth trend, represented by a cubic spline. Tie points or independent age constraints, such as radiometric dates or biostratigraphic markers, can be integrated within this framework, providing age estimates for all sites. Our approach identifies multiple alignments where distinct alternatives exist, estimates their relative probabilities, and quantifies the uncertainty associated with correlations and age estimates. We apply StratoBayes to a lower Cambrian dataset comprising a combination of δ¹³C records, radiometric dates and astrochronology from four sites in Morocco and Siberia. The results demonstrate its capacity to quantify existing alignments, and provide the first precise age estimate for the evolutionary appearance of trilobites in Siberia, one of the hallmarks of the Cambrian Explosion. Beyond this application, StratoBayes offers a generalisable framework for probabilistic stratigraphic correlation, with potential to improve age models across a range of proxy records and time intervals.