Machine Learning Applied to K-Bentonite Geochemistry for Identification and Correlation: The Ordovician Hagan K-Bentonite Complex Case Study

Abstract

Altered tephras (K-bentonites) are of great importance for calibration of the geologic time scale, for local, regional, and global correlations, and paleoenvironmental reconstructions. Thus, definitive identification of individual tephras is critical. Single crystal geochemistry has been used to differentiate tephra layers, and apatite is one of the phenocrysts commonly occurring in tephras that has been widely used. Here, we use existing and newly acquired analytical datasets (electron probe micro-analyzer [EPMA] data and laser ablation ICP-MS [LA-ICP-MS] data, respectively) of apatite in several Ordovician K-bentonites that were collected from localities about 1200 km apart (Minnesota/Iowa/Wisconsin and Alabama, United States) to test the use of machine-learning (ML) techniques to identify with confidence individual tephra layers. Our results show that the decision tree based on EPMA data uses the elemental concentration patterns of Mg, Mn, and Cl, consistent with previous studies that emphasizes the utility of these elements for distinguishing Ordovician K-bentonites. Differences in the experimental setups of the analyses, however, can lead to offsets in absolute elemental concentrations that can have a significant impact on the correct identification and correlation of individual K-bentonite beds. The ML model using LA-ICP-MS data was able to identify several K-bentonites in the southern Appalachians and establish links to K-bentonites samples from the Upper Mississippi Valley. Furthermore, the ML model identified individual layers of multiphase eruptions, thus illustrating very well the great potential of applying ML techniques to tephrochronology.

Keywords

• tephrochronology
• tephrostratigraphy
• tephra fingerprinting
• Deicke
• Millbrig
• Dickeyville