What settlements leave behind — pXRF compositional data analysis of archaeological layers from Tell el-Fara'in (Buto, Egypt) using machine learning

Ginau, Andreas; Steiniger, Daniel; Hartmann, Rita; Hartung, Ulrich; Schiestl, Robert H.; Altmeyer, Marina; Seeliger, Martin; Wunderlich, Jürgen

doi:10.1016/j.palaeo.2020.109666

Cited by 8 publications

(27 citation statements)

References 20 publications

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“…Janovski and colleagues [ 61 ] identified the chemical elements that relate to human activity in Tel Burna (southern Levant), shedding light on the formation processes of the archaeological deposit. Davis, MacFarlane and Henrickson 2012 [ 62 ] and Ginau et al 2020 [ 63 ] focused on the analysis of archaeological profiles to analyze vertical variability in chemical elements in lithostratigraphic sequences and support chronological reconstruction. Holcomb and Karkansas [ 64 ] applied pXRF on resin-impregnated micromorphological block samples to complement their geoarchaeological study of the profile of an Archaic (7 th century BC) ritual ash midden from the site of Kalapodi (Greece).…”

Section: Introductionmentioning

confidence: 99%

Identifying anthropogenic features at Seoke (Botswana) using pXRF: Expanding the record of southern African Stone Walled Sites

et al. 2021

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Numerous and extensive ‘Stone Walled Sites’ have been identified in southern African Iron Age landscapes. Appearing from around 1200 CE, and showing considerable variability in size and form, these settlements are named after the dry-stone wall structures that characterize them. Stone Walled Sites were occupied by various Bantu-speaking agropastoral communities. In this paper we test the use of pXRF (portable X-ray fluorescence analysis) to generate a ‘supplementary’ archaeological record where evident stratigraphy is lacking, survey conditions may be uneven, and excavations limited, due to the overall site size. We propose herein the application of portable X-ray fluorescence analysis (pXRF) coupled with multivariate exploratory analysis and geostatistical modelling at Seoke, a southern African SWS of historical age (18th century CE). The aim of the paper is twofold: to explore the potential of the application of a low cost, quick, and minimally invasive technique to detect chemical markers in anthropogenic sediments from a Stone Walled Site, and to propose a way to analyse the results in order to improve our understanding of the use of space at non-generalized scales in such sites.

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Section: Introductionmentioning

confidence: 99%

Identifying anthropogenic features at Seoke (Botswana) using pXRF: Expanding the record of southern African Stone Walled Sites

et al. 2021

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“…is represented by unit RB. The presence of a ceramic fragment embedded within coarse sand underlines a sediment transport by a turbulent flow (Goiran et al, 2014). The overlying sediments (6.00-2.80 m b.s.)…”

Section: Interpretation Of Coring M006mentioning

confidence: 95%

“…The Ca/Fe and Ca/Ti ratios show peaks towards Ca, indicating predominantly eolian input at certain intervals (Woronko, 2012;Blanchet et al, 2015;Pint et al, 2015;Pennington et al, 2019). Partially present high S concentrations reflect the existence of reducing conditions, detectable within silty sediments corresponding to phases of low-energy flow or stagnant water (Goiran et al, 2014;Martinez-Ruiz et al, 2015).…”

Section: Classification Of Sedimentary Unitsmentioning

confidence: 98%

“…Fluctuations within the Fe and Al concentrations as well as the Cu/Zn ratio indicate the accumulation within a fluvial system (Stanley et al, 2003;Ginau et al, 2019). The visible lamination of the sand section with silty strata highlights changing hydrodynamics (Vött et al, 2002;Goiran et al, 2014). The Ca/Fe and Ca/Ti ratios show peaks towards Ca, indicating predominantly eolian input at certain intervals (Woronko, 2012;Blanchet et al, 2015;Pint et al, 2015;Pennington et al, 2019).…”

Section: Classification Of Sedimentary Unitsmentioning

confidence: 99%

“…An alternating streamflow leads to the deposition of sediments with varying grain sizes. The lithological character of the deposits most likely reveals laminations of coarse and fine-grained sediments (Wunderlich, 1989;Goiran et al, 2014;Toonen et al, 2018). Thus, due to the portions of coarse-grained material, the electrical resistivity potentially reveals higher values as well.…”

Section: Interpretation Of Ert Profile Cmentioning

confidence: 99%

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Reconstruction of former channel systems in the northwestern Nile Delta (Egypt) based on corings and electrical resistivity tomography (ERT)

Altmeyer

Seeliger

Ginau

et al. 2021

E&G Quaternary Sci. J.

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Abstract. The current state of research about ancient settlements within the Nile Delta allows the hypothesizing of fluvial connections to ancient settlements all over the Nile Delta. Previous studies suggest a larger Nile branch close to Kom el-Gir, an ancient settlement hill in the northwestern Nile Delta. To contribute new knowledge to this little-known site and prove this hypothesis, this study aims at using small-scale paleogeographic investigations to reconstruct an ancient channel system in the surroundings of Kom el-Gir. The study pursues the following: (1) the identification of sedimentary environments via stratigraphic and portable X-ray fluorescence (pXRF) analyses of the sediments, (2) the detection of fluvial elements via electrical resistivity tomography (ERT), and (3) the synthesis of all results to provide a comprehensive reconstruction of a former fluvial network in the surroundings of Kom el-Gir. Therefore, auger core drillings, pXRF analyses, and ERT were conducted to examine the sediments within the study area. Based on the evaluation of the results, the study presents clear evidence of a former channel system in the surroundings of Kom el-Gir. Thereby, it is the combination of both methods, 1-D corings and 2-D ERT profiles, that derives a more detailed illustration of previous environmental conditions which other studies can adopt. Especially within the Nile Delta which comprises a large number of smaller and larger ancient settlement hills, this study's approach can contribute to paleogeographic investigations to improve the general understanding of the former fluvial landscape.

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Comparing different machine‐learning techniques to date Nile Delta sediments based on portable X‐ray fluorescence data

et al. 2022

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Geomorphology generally aims to describe and investigate the processes that lead to the formation of landscapes, while geochronology is needed to detect their timing and duration. Due to restrictions on exporting geological samples from Egypt, modern geoscientific studies in the Nile Delta lack the possibility of dating the investigated sediments and geological features by standard techniques such as OSL or AMS 14 C; therefore, this study aims to validate a new approach using machinelearning algorithms on portable X-ray fluorescence (pXRF) data. Archaeologically dated sediments from the archaeological excavations of Buto (Tell el-Fara'in; on-site) that pXRF analyses have geochemically characterized serve as training data for running and comparing Neural Nets, Random Forests, and single-decision trees. The established pXRF fingerprints are transferred via machine-learning algorithms to set up a chronology for undated sediments from sediment cores (i.e., the test data) of the nearby surroundings (off-site). Neural Nets and Random Forests work fine in dating sediments and deliver the best classification results compared with singledecision trees, which struggle with outliers and tend to overfit the training data.Furthermore, Random Forests can be modeled faster and are easier to understand than the complex, less transparent Neural Nets. Therefore, Random Forests provide the best algorithm for studies like this. Furthermore, river features east of Kom el-Gir are dated to pre-Ptolemaic times (before 332 B.C.) when Kom el-Gir had possibly not yet been settled. The research in this paper shows the success of close interactions from various scientific disciplines (Geoinformatics, Physical Geography, Archaeology, Ancient History) to decipher landscape evolution in the long-term-settled Nile Delta's environs using machine learning. With the approach's design and the possibility of integrating many other geographical/sedimentological methods, this study demonstrates the potential of the methodological approach to be applied in other geoscientific fields.

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What settlements leave behind — pXRF compositional data analysis of archaeological layers from Tell el-Fara'in (Buto, Egypt) using machine learning

Cited by 8 publications

References 20 publications

Identifying anthropogenic features at Seoke (Botswana) using pXRF: Expanding the record of southern African Stone Walled Sites

Identifying anthropogenic features at Seoke (Botswana) using pXRF: Expanding the record of southern African Stone Walled Sites

Reconstruction of former channel systems in the northwestern Nile Delta (Egypt) based on corings and electrical resistivity tomography (ERT)

Comparing different machine‐learning techniques to date Nile Delta sediments based on portable X‐ray fluorescence data

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