Discrimination, correlation, and provenance of Bed I tephrostratigraphic markers, Olduvai Gorge, Tanzania, based on multivariate analyses of phenocryst compositions

Abstract

The chronology of Pleistocene flora and fauna, including hominin remains and associated Oldowan industries in Bed I, Olduvai Gorge, Tanzania, is primarily based on 40Ar/39Ar dating of intercalated tuffs and lavas, combined with detailed tephrostratigraphic correlations within the basin. Although a high-resolution chronostratigraphic framework has been established for the eastern part of the Olduvai Basin, the western subbasin is less well known due in part to major lateral facies changes within Bed I combined with discontinuous exposure. We address these correlation difficulties using the discriminative power of the chemical composition of the major juvenile mineral phases (augite, anorthoclase, plagioclase) from tuffs, volcaniclastic sandstones, siliciclastic units, and lavas. We statistically evaluate these compositions, obtained from electron probe micro-analysis, applying principal component analysis and discriminant analysis to develop discriminant models that successfully classify most Bed I volcanic units. The correlations, resulting from integrated analyses of all target minerals, provide a basin-wide Bed I chemostratigraphic framework at high lateral and vertical resolution, consistent with the known geological context, that expands and refines the geochemical databases currently available. Correlation of proximal ignimbrites at the First Fault with medial and distal Lower Bed I successions of the western basin enables assessment of lateral facies and thickness trends that confirm Ngorongoro Volcano as the primary source for Lower Bed I, whereas Upper Bed I sediment supply is mainly from Olmoti Volcano. Compositional similarity between Tuff IA, Bed I lava, and Mafic Tuffs II and III single-grain fingerprints, together with north- and northwestward thinning of Bed I lava, suggests a common Ngorongoro source for these units. The techniques applied herein improve upon previous work by evaluating compositional affinities with statistical rigor rather than primarily relying on visual comparison of bivariate plots.