Non-destructively characterizing sandstones, orthoquartzites, agates, and petrified wood for provenance research: Perspectives from the Southeastern Coastal Plain, United States

被引:0
|
作者
Sherman, Simon P. [1 ,2 ]
Parish, Ryan M. [1 ]
Kwon, Youngsang [1 ]
Meredith, Steven [3 ]
Johnson, David [4 ]
机构
[1] Univ Memphis, Dept Earth Sci, Memphis, TN USA
[2] Washington Univ St Louis, Dept Anthropol, St Louis, MO 63130 USA
[3] Cedars Consulting LLC, Epes, AL USA
[4] Alabama Archaeol Soc, Livingston, AL USA
来源
关键词
Lower Mississippi Valley; machine learning; provenance; quartzite; reflectance spectroscopy; sandstone; toolstone; STONE ARTIFACTS; QUARTZITE; CHERT; SEDIMENTARY; MOUNTAINS; SITES; BASIN;
D O I
10.1002/gea.22018
中图分类号
K85 [文物考古];
学科分类号
0601 ;
摘要
Siliceous sandstone (including quartzites), petrified wood, and agates located in Alabama and Mississippi were utilized as a toolstone resource during every recognized cultural period in the Lower Mississippi Valley region of the Southeastern United States. Regrettably, these materials have not been the focus of many provenance-related investigations. Recent analyses of quartzite and sandstone from other regions in North America and from the Pyrenees were successful in discriminating sources using petrographic techniques. The current study examines the application of visible/near-infrared reflectance and Fourier transform infrared reflectance (FTIR) spectroscopy on sourcing siliceous materials besides chert, particularly sandstones, orthoquartzites (quartz sandstone), petrified woods, and agates. This source characterization investigation focuses on a case study involving materials gathered from eight distinct collection sites, encompassing nine different siliceous resources collected in Alabama and Mississippi. These materials were sourced from two distinct geological formations: the Hattiesburg and Tallahatta. Results demonstrate the ability of non-destructive reflectance spectroscopy and introduces a new outlier modeling method that detects, clusters, and separately models outliers with their own set of basis vectors. Principal component analyses, least absolute shrinkage and selection operator regression, linear discriminant function analysis (LDA), and random forest classification are used in this paper to better identify outlier elements as well as discriminate for stone materials accurately (between 67% and 100%). Although this is the first reflectance spectroscopy investigation used to characterize these materials for provenance applications, the preliminary results compare favorably with other provenance techniques whose aim is to quantify between-formation (inter) and within-formation (intra) outcrop variation. The quantified and differentiated sources, based on the hyperspectral signatures of the material, will provide a better understanding of prehistoric reliance on these lithic resources and produces a proxy to determine mobility, social interaction, and other past behavior.
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页码:628 / 644
页数:17
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