This dissertation consists of three articles that develop and implement geoarchaeological approaches to the study of hominin biogeography in the Greek islands, a region that may have played a key role during the initial peopling of southeastern Europe in the Middle to Late Pleistocene (770 – 13 ka). The first article addresses the need to find Palaeolithic sites in the Greek islands by reviewing geoarchaeologically informed archaeological surveys that prioritize deposits – Pleistocene sediments and soils – on mainland Greece. I operationalize and implement a four-phase approach for future deposit-centered surveys in the islands. I conclude that future surveys should target geomorphic settings conducive to paleosol preservation, such as in near-shore coastal areas (e.g., uplifted hillslopes and actively eroding alluvial fans) marked by paleo-sea-level-indicators (sea notches, marine terraces, and aeolianites), as well as sediment depo-centers (e.g., internally drained basins) in non-coastal geomorphic settings. The second article develops and employs a multiscalar geoarchaeological approach for investigating and interpreting complex hillslope formation processes at the newly excavated Palaeolithic site of Stelida, located on the island of Naxos, Greece. Here, I integrate traditional geoarchaeological methods (lithostratigraphy, pedostratigraphy, allostratigraphy) with microarchaeological techniques, such as thin-section soil micromorphology and portable x-ray fluorescence spectroscopy (pXRF) to separate sediments and soils aiding in the construction of a stratigraphic framework for Stelida.The third article further develops the method of integrating pXRF and soil micromorphology applied at the site-specific scale. In this article, I developed an Exploratory Spatial Data Analysis (ESDA) method for visualizing geochemistry-facies relationships through the application of pXRF to a resin-impregnated block sample preserving an Archaic (seventh century BCE) ash midden (eschara) from the site of Kalopodi, Greece.This dissertation addresses a primary research effort in Greece’s Aegean Basin (Greek islands): the search for and study of Palaeolithic archaeology dating to Middle and Late Pleistocene. Geoarchaeologically informed research designs, such as deposit-centered surveys in open-air environments, can increase Palaeolithic site inventories for the region by focusing on finding geomorphic settings conducive to paleosol preservation. Moreover, the multi-scalar geoarchaeological approaches here, which integrate sedimentology, pedology, micromorphology, and geochemistry, provide an effective approach for the identification and compositional (mineralogic and geochemical) study of paleosols within those settings.

Imitation of archeological artifacts or replacing the authentic ones with fake replicates is a universal problem / it is particularly important in Turkey for historical metal objects. Traditionally used visual inspection methods alone are not sufficient for the solution of contemporary problems. In this study, chemical characterization has been used to determine the differences between the authentic and fake objects. The non-destructive analyses were carried out by Portable X-ray Fluorescence Spectrometry (P-XRF). Silver Roman Coins (27 B.C. to 244 A.D.) were the objects handled in this research. In particular the concentrations of Zr, Pt, Pb and Bi were used for differentiation / it has been observed that the concentrations have different trends in the authentic and fake silver coins. In authentic coins the average Pb concentration was found to be 0.77%, while this value was 0.055% for the fake ones. Bi could be determined in 86% of the authentic coins while it could not be detected in any fake coin. It has been generally observed that the silver and copper concentrations could not be utilized in authenticity tests. Another approach was the use of Line Scanning Electron Microscopy-Energy Dispersive X-Ray Fluorescence Spectrometry (LSEM-EDX). Using LSEM-EDX technique, it was observed that the concentration changes near the interface between the matrix and the copper-rich locations exhibits difference behaviors for the authentic and fake objects. This difference is originated by the fact that a newly formed copper amalgam contains copper-rich phases while with extended time concentration changes at interfaces become more gradual or not detectable. Pearson correlation was used in order to elucidate the relations between the element concentrations determined by P-XRF. In order to see whether the authentic and silver fake coins can form separate groups, dendograms have been constructed utilizing SPSS 16.0 software and Euclidian Square Distance method. It has been observed that the authentic and fake coins can be successfully grouped when the proper statistical choices are used. It has been observed that these groups have significant differences using t-test. The selected and used technology is proposed for use by museums and entities keeping archaeological collections in order to prevent forgeries.

ABSTRACT DIFFERENTIATION AND CLASSIFICATION OF COUNTERFEIT AND REAL COINS BY APPLYING STATISTICAL METHODS Tansel, I&ccedil / ten M.Sc, Archaeometry Graduate Program Supervisor : Assist. Prof. Dr. Zeynep Isil Kalaylioglu Co-Supervisor : Prof. Dr. Sahinde Demirci June 2012, 105 pages In this study, forty coins which were obtained from Museum of Anatolian Civilizations (MAC) in Ankara were investigated. Some of those coins were real (twenty two coins) and the remaining ones (eighteen coins) were fake coins. Forty coins were Greek coins which were dated back to middle of the fifth century BCE and reign of Alexander the Great (323 &ndash / 336 BCE). The major aims of this study can be summarized as follow

Soil characterization provides the basic information necessary for understanding the physical, chemical, and biological properties of soils. Knowledge about soils can in turn be used to inform management practices, optimize agricultural operations, and ensure the continuation of ecosystem services provided by soils. However, current analytical standards for identifying each distinct property are costly and time-consuming. The optimization of laboratory grade technology for wide scale use is demonstrated by advances in a proximal soil sensing technique known as portable X-ray fluorescence spectrometry (pXRF). pXRF analyzers use high energy Xrays that interact with a sample to cause characteristic reflorescence that can be distinguished by the analyzer for its energy and intensity to determine the chemical composition of the sample. While pXRF only measures total elemental abundance, the concentrations of certain elements have been used as a proxy to develop models capable of predicting soil characteristics. This study aimed to evaluate existing models and model building techniques for predicting soil pH, texture, cation exchange capacity (CEC), soil organic carbon (SOC), total nitrogen (TN), and C:N ratio from pXRF spectra and assess their fittingness for California soils by comparing predictions to results from laboratory methods. Multiple linear regression (MLR) and random forest (RF) models were created for each property using a training subset of data and evaluated by R2 , RMSE, RPD and RPIQ on an unseen test set. The California soils sample set was comprised of 480 soil samples from across the state that were subject to laboratory and pXRF analysis in GeoChem mode. Results showed that existing data models applied to the CA soils dataset lacked predictive ability. In comparison, data models generated using MLR with 10-fold cross validation for variable selection improved predictions, while algorithmic modeling produced the best estimates for all properties besides pH. The best models produced for each property gave RMSE values of 0.489 for pH, 10.8 for sand %, 6.06 for clay % (together predicting the correct texture class 74% of the time), 6.79 for CEC (cmolc/kg soil), 1.01 for SOC %, 0.062 for TN %, and 7.02 for C:N ratio. Where R2 and RMSE were observed to fluctuate inconsistently with a change in the random train/test splits, RPD and RPIQ were more stable, which may indicate a more useful representation of out of sample applicability. RF modeling for TN content provided the best predictive model overall (R2 = 0.782, RMSE = 0.062, RPD = 2.041, and RPIQ = 2.96). RF models for CEC and TN % achieved RPD values >2, indicating stable predictive models (Cheng et al., 2021). Lower RPD values between 1.75 and 2 and RPIQ >2 were also found for MLR models of CEC, and TN %, as well as RF models for SOC. Better estimates for chemical properties (CEC, N, SOC) when compared to physical properties (texture), may be attributable to a correlation between elemental signatures and organic matter. All models were improved with the addition of categorical variables (land-use and sample set) but came at a great statistical cost (9 extra predictors). Separating models by land type and lab characterization method revealed some improvements within land types, but these effects could not be fully untangled from sample set. Thus, the consortia of characterizing bodies for ‘true’ lab data may have been a drawback in model performance, by confounding inter-lab errors with predictive errors. Future studies using pXRF analysis for soil property estimation should investigate how predictive v models are affected by characterizing method and lab body. While statewide models for California soils provided what may be an acceptable level of error for some applications, models calibrated for a specific site using consistent lab characterization methods likely provide a higher degree of accuracy for indirect measurements of some key soil properties.