Automated Bayesian Correlation of Aeronian Carbon Isotope Excursions in South China

High-resolution correlation of carbon isotope (δ¹³Ccarb) curves is essential for improving global stratigraphic frameworks, yet traditional correlation approaches remain partly subjective and difficult to reproduce. Here, we present an automated chemostratigraphic correlation of four Lower Silurian sections from northern Guizhou, South China, using the Bayesian alignment tool StratoBayes. The analysis integrates carbonate carbon isotope data with independent biostratigraphic constraints (primarily based on conodonts) to generate an objective composite curve and optimized correlation among sections. The resulting correlation identifies two distinct positive δ¹³Ccarb excursions within the Aeronian. The early Aeronian excursion occurs near the base of the Shiniulan Formation and in the upper Xiangshuyuan Formation, whereas the late Aeronian excursion is recorded in the upper Shiniulan Formation. These excursions are consistently recovered across multiple sections despite lithological variability and uneven sampling density, demonstrating the robustness of the method. The results refine regional stratigraphic correlations and provide a reproducible framework for recognizing Aeronian carbon isotope events in South China. This case study also highlights the broader methodological potential of Bayesian approaches for stratigraphic correlation. A recently developed graphical user interface for StratoBayes improves accessibility and usability for non-specialist users, enabling rapid visualization, parameter testing, and automated correlation workflows. This substantially lowers the barrier to applying Bayesian chemostratigraphic correlation and facilitates wider adoption among stratigraphers, including applications in subsurface and industry datasets. The study therefore demonstrates both the stratigraphic significance of Aeronian isotope events and the value of automated Bayesian approaches for reproducible, scalable chemostratigraphic correlation.