Comparative study of three quantitative methods for biostratigraphic correlations

Quantitative stratigraphy, based on the principles of biostratigraphy and mathematical models, is a new research field compared with traditional stratigraphy. Traditional stratigraphic researstratigraphic framework and to make correlations among different sections. This method, in which only the index fossils are emphasized while other fossils are ignored, relies largely on personal experiences. On the other hand, quantitative stratigraphy aims to make full use of the stratigraphic information by translating all fossil records into structured data and reconstruct a composite time scale with mathematical tools, such as regression, graph theory and randomized algorithms. Quantitative methods have remarkably improved the resolution of stratigraphic correlation, which is critical for understanding geological events that span a relatively short time interval and a wide geographic range. At present, there are three commonly used quantitative methods, including Graphic Correlation, Constrained Optimization (CONOP) and Unitary Association Method (UAM). In this study we introduce the fundamentals of the three methods and evaluate the possible factors that may influence correlation outcomes using mathematical models. Four factors, including the distribution of index fossils among the sections, the number of species studied, the proportions of species occurring in more than one section and the proportion of singletons are considered. Our results show that Graphic Correlation is highly dependent on the isochronous biostratigraphic events such as first appearance data of index fossils. This method is reliable when the biostratigraphic timeframe is well-established, but not suitable for the situation that sections have different regional time scales. UAM responses significantly to the proportion of common species and singletons, and shows high bias and constrained power of resolution, based on its algorithm. In contrast, CONOP has the highest applicability and obtains a relatively stable outcome with different factors considered.

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