Geochemical and physical characterization of lithic raw materials in the Olduvai Basin, Tanzania

Abstract

The invention and proliferation of stone tool technology in the Early Stone Age (ESA) marks a watershed in human evolution. Patterns of lithic procurement, manufacture, use, and discard have much to tell us about ESA hominin cognition and land use. However, these issues cannot be fully explored outside the context of the physical attributes and spatio-temporal availability of the lithic raw materials themselves. The Olduvai Basin of northern Tanzania, which is home to both a wide variety of potential toolstones and a rich collection of ESA archaeological sites, provides an excellent opportunity to investigate the relationship between lithic technology and raw material characteristics. Here, we examine two attributes of the basin's igneous and metamorphic rocks: spatial location and fracture predictability. A total of 244 geological specimens were analyzed with non-destructive portable XRF (pXRF) to determine the geochemical distinctiveness of five primary and secondary sources, while 110 geological specimens were subjected to Schmidt rebound hardness tests to measure fracture predictability. Element concentrations derived via pXRF show significant differences between sources, and multivariate predictive models classify geological specimens with 75-80% accuracy. The predictive models identify Naibor Soit as the most likely source for a small sample of three lithic artifacts from Bed II, which supports the idea that this inselberg served as a source of toolstone during the early Pleistocene. Clear patterns in fracture predictability exist within and between both sources and rock types. Fine-grained volcanics show high rebound values (associated with high fracture predictability), while finer-grained metamorphics and coarse-grained gneisses show intermediate and low rebound values, respectively. Artifact data from Bed I and II suggest that fracture predictability played a role in raw material selection at some sites, but other attributes like durability, expediency, and nodule size and shape were more significant.

Tracing the spatial imprint of Oldowan technological behaviors: A view from DS (Bed I, Olduvai Gorge, Tanzania)

“…The Lemagrut volcano, located 2 km from the southern edge of the gorge, is the main source of mostly medium to fine-grained basalts. Although the primary source of volcanic materials (mostly in the form of boulders) is located on the volcano slopes, secondary sources are easily available as rounded cobbles and pebbles transported by fluvial channels into the basin [104][105][106][107].…”

Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

“…With the advent of Machine Learning techniques, such separation is routinely possible, using iterative methodologies that improve on their results through validation of reliable training data. The utility of such approaches has been seen more widely in Archaeology, including towards remote sensing and prediction or classification of archaeological sites 11 – 13 , the recording and creation of artefact typologies 14 – 19 , and more recently for lithic sourcing 20 – 24 . For this latter topic, these techniques promise more powerful approaches to the separation of geological samples and increased accuracy over classical statistical techniques.…”

“…Despite widespread documentation on the correct usage of these techniques, several problems can be identified in the recent literature on lithic sourcing. These include basic prerequisites such as inadequate sampling from individual geological sources for machine learning techniques to effectively learn from 23 , to perhaps more importantly, a large number which use classification techniques with no ‘none of the above’ or ‘other’ class or method to discriminate from the geological sample sites used to compare artefacts with 20 – 22 , 24 , 25 . The failure to create such a class or method for the model to use can lead to false positive results (type I errors), allowing no option to rule out the geological sample sites used.…”

Petrographic Characterization of Raw Material Sources at Oldupai Gorge, Tanzania

“…Second, while previous studies on Oldupai's lithic raw materials relied on macroscopic, petrographic, and geochemical techniques, they have not comprehensively characterized quartzitic outcrops (Leakey, 1971;Stiles et al, 1974;Hay, 1976;Stiles, 1991Stiles, , 1998Jones, 1994;Leakey and Roe, 1994;Kyara, 1999;Mollel, 2002;Tactikos, 2005;Blumenschine et al, 2008;Santonja et al, 2014;McHenry and de la Torre, 2018). Most recently, Egeland et al (2019) studied local outcrops using a portable X-Ray Fluorescence spectroscope for chemical characterization and a Schmidt Hammer to determine fracture predictability. Based on statistical analyses, they assign Naibor Soit Kubwa as the likely source for three quartzite lithics from BK East (Bed II) (Egeland et al, 2019).…”

“…Most recently, Egeland et al (2019) studied local outcrops using a portable X-Ray Fluorescence spectroscope for chemical characterization and a Schmidt Hammer to determine fracture predictability. Based on statistical analyses, they assign Naibor Soit Kubwa as the likely source for three quartzite lithics from BK East (Bed II) (Egeland et al, 2019). Apart from this most recent study, the lack of characterization studies is unwarranted for several reasons.…”