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.
Highlights
Introduction iationsThe geochemistry of igneous apatite is complex, and in response to various magmatic conditions during crystallization, its trace-element composition can acquire a characteristic geochemical fingerprint [1,2]
We treated the data from the known Deicke and Millbrig K-bentonite locations of Carey et al [14] as unknowns to test the Upper Mississippi Valley (UMV)-model
Deicke K-bentonite was much more reliably identified than the Millbrig K-bentonite
Summary
Introduction iationsThe geochemistry of igneous apatite is complex, and in response to various magmatic conditions during crystallization (e.g., oxygen fugacity, temperature, crystallization rate, chemical composition), its trace-element composition can acquire a characteristic geochemical fingerprint [1,2]. The geochemistry of apatite is a powerful tool for testing tectonostratigraphic hypotheses and for the high-resolution correlation of pyroclastics produced by large volcanic eruptions, as demonstrated by its successful use in many investigations of tephras ranging in age from the Paleozoic to the recent Cenozoic [3,4,5,6]. The tephrochronological approach has led to development of a high-resolution framework useful for identifying and correlating stratigraphic packages that are otherwise difficult to identify. Much stratigraphic correlation in the northern part of Laurentia (Figure 1) is based on K-bentonite tephrostratigraphy [4,5,14,15]. A high-resolution stratigraphic framework based on apatite phenocryst geochemistry facilitates testing of stratigraphic and geological hypotheses, e.g., apatite geochemistry provided evidence that the stratigraphic changes across the Sandbian-Katian boundary are time-transgressive across part of the Appalachian basin and are most likely not global events [5]
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