This Poster was part of the Ethnoarchaeology of Fire Symposium 2017, Tenerife, La Laguna Spain
1 Institute for Archaeological Sciences, University of Tuebingen, Tuebingen, Germany
2 Institute for Archaeology, History, Culture and Religious Studies, University of Bergen, Bergen, Norway
3 Department of Anthropology, University of Arizona, Tucson, United States of America
4 Evolutionary Studies Institute, University of the Witwatersrand, Johannesburg, South Africa
5 Senckenberg Centre for Human Evolution and Palaeoenvironment, University of Tuebingen, Tuebingen Germany
Visualization of the effects of heat in archaeological sediments adds a new interpretational layer of fire-related events in archaeological deposits. By applying a newly developed method based on spatially referenced spectral data, we were able to visualize the distribution of heat over an entire thin-section. Here, we present the workflow from the archaeological sample to the final heat map.
The process of fire (Fig.1) results in light, heat and several other reactional products (i.e. ash, charcoal) (Fig. 2. The reactional products and the effects of heat are preserved as fire proxies in the archaeological record (Fig. 3). Depositional and post-depositional processes such as chemical alteration or physical reworking can modify or destroy the proxies themself or their original spatial context (Fig. 4). The archaeological analysis of the proxies is either performed in their present spatio-depositional context (in-situ) or is removed from this context (ex-situ) . Typically, the in-situ analysis is done by qualitative visual observation of the archaeological record on-site, whereas ex-situ identification is done by quantitative measurements off-site. Thin-sections (Fig. 5) made out of block-samples preserve the spatial context of the archaeological record and provides the ability to perform analyses off-site.
Most commonly, fire proxies are visually identified. Because of the qualitative nature of this method, it is not definitively reliable and reproducible. Quantitative measurements not only verify visual observations, they enable us to identify proxies outside of the optical spectrum and thereby adding an additional data dimension representing the measurement values. Although there is an increase in quality and depth of data, the direct spatial context is lost. In-situ measurements on thin-sections bridge the gap, and provide both information of a data point: the spatial context (on the microscale) and a quantifiable value (i.e. heat).
With a significant gain in data depth, there is also increasing complexity. Therefore, it is necessary to make the information more accessible for humans. Visualization in the form of a heat map addresses this need of abstraction of the spatio-contextual data and facilitates the analysis.
Block samples carved out of the archaeological sediment, covered in plaster of paris for transport and later stabilized with polyester resin preserve the sample in a solid in-situ state. A thin-section (Fig. 5) is made by gluing the sample on- to a slide, cutting and grinding it down to 30 µm (thinner than a human hair). The thin-section represents 70 x 90 mm of the original archaeological context. A petrographic polarizing light microscope (Zeiss Axio Imager A.1) is used to describe micromorphological features (in general: ; Combustion Features: ).
High Resolution Scanning
Data analysis by micro-FTIR or similar methods rarely include direct spatial information, this can only be visually identified. Therefore, it is necessary to work within a visual reference system, which is on a similar optical scale on which the data point is measured. Even if the method provides an optical reference, such as an image that indicates the measured points (Fig. 6), the extension of the reference system is limited to the field of view of the attached microscope. One option is to stich such images together to create a mosaic. The resulting image is often distorted and it’s difficult to achieve a continuous image quality. Additionally, the workload of taking many low-scale images and merging them into one image that covers an entire thin-section is demanding.
The use of a film-scanner addresses all of these problems. For our case-studies, the Nikon Coolscan LS-8000 ED (Fig. 7, discontinued) proved to be appropriate. Within a single scan, this device covers an area of 60 x 92 mm, on 4000 dpi, in less than three minutes. The result is a continuous image of comparatively small file-size (ca. 150 Megabyte) whose resolution is comparable to 40x magnification. By inserting polarizing filters, we were able to scan thins-section in plain-polarized-light (PPL) and cross-polarized-light (XPL). The background image (here Fig. 7, No. 6 & 8) of this poster demonstrates the potential of this method which is described in
Heat alters the molecular structure of minerals, which can be quantified using numerical indices derived from absorbance of infrared energy by molecular bonds. The relationships between different indices and intensity of heating (i.e. temperature) is based on samples produced during controlled experiments.
A Fourier transformed infrared spectrometer (FTIR) is able to measure the absorbance of energy along a range of specific infrared wavelengths. The plot of an FTIR-spectra (Fig. 8) represents the transmitted or reflected infrared absorbance spectrum of an object. The technique of Attenuated total reflection (ATR), where a crystal (diamond) is directly placed onto the surface of the thin-section, which allows in-situ measurement. Combined with a microscope (micro-FTIR, Fig. 9), it is possible to place the measurement on a sub-millimeter scale.
To determine the temperature from an FTIR spectrum, it is necessary to build a reference collection (Fig. 8.). Heating minerals (e.g.: glauconite and bones) in a muffle-furnace at different temperature stages provided controlled samples for FTIR-references. These made it possible to identify and establish diagnostic indices (i.e. peaks) for specific temperature ranges (for Glauconite: ; for Bones: ). To finally classify the archaeological FTIR spectra, they need to be statistically analyzed according to the temperature indices.
A Geographical Information System (GIS) is a powerful tool to manage, analyze and visualize large amounts of spatio-contextual data. It is able to process raster-images, vector-data and attributive data (i.e. temperature values). Although the common use of GIS is more related to cartographic tasks on meter to multiple kilometer scale, it can be used on (an abstracted ) millimeter scale. For our two case-studies, we also used the commercial software package ArcGIS ArcMap 10.3 (Blombos Cave) as the free QGIS 2.14 LTS (Mt. Lykaion). Both packages provide similar basic features and are able to generate data in open file-formats to guarantee long-term reproducibility of this method.
GIS provides several ways to spatially reference data. The high resolution scan of the thin-section was used as root-reference. Based on this, other images like microFTIR Micrographs (Fig. 2) can be referenced with a point-to-point method, where two spatial (visual) corresponding points in both images are marked by the operator. The micrograph is automatically scaled, rectified and aligned to the root image. Another method is the direct referencing (mapping) of points, lines and areas (polygons). The latter method is not limited to indicating the measurement points, even more it is the preferable method to highlight micro-morphological features that provide further analytical value.
Although the spectral data can be processed independently (see Spectral Analysis/Reference Samples), most GIS Packages provide their own application programming interface (e.g. Python API) that allows statistical classification of the spectral data and automatically integrate this as attributive data.
The spatio-contextual analysis of GIS can be best described on a layer model (Fig. 10). The spatial analysis is based on intersection of data (e.g. glauconite and color-value), distance (bones and temperatures) or distribution. For the latter, it is useful to convert the discrete data into continuous data by interpolation.
Once the data is temperature-classified, GIS provides several methods for visualizing the data. Adding a color-scheme based on the temperature results is the best form of optical feedback.
Application and outlook
The application of this method on two (on-going) case studies revealed interesting results as well as significant benefits for the analytical workflow.
The sediments of the ancient Greek ash-altar at Mt. Lykaion, Greece consist of combustion-derived residues: charcoal, burnt bone, fat-derived char, wood-ash and other charred materials. Despite compositional changes over time, the deposits show similar types of burning activities. A previous micromorphological study by analyzed the geoarchaeology and the ritual behavior behind the deposits. Here, our case study aims to identify depositional events (potentially on a seasonal scale). The integration of all image material like micro-FTIR and microscope micrographs, scans of the cutted block and the high resolution scan (PPL and XPL) significantly reduce the micromorphological analytical workload. This is due to less analytical redundancies (microscope time), the spatial tracking of visual observations, and the measurement progress. Fig. 11 not only highlight the bones which are not yet analyzed by micro-FTIR, it also shows the semi-transparent overlay of the already measured bones including the exact location of the measurements. Since visualization is part of the analytical process, there is an almost immediate output of graphical content. This eases the generation of materials for publication and promotes sharing and communication of complex relations.
The case study on fire-related sediments in the deposits of the Middle Stone Age site of Blombos Cave, South Africa, highlights the advanced analytical value of this method. The frequently occuring mineral glauconite in the sediments proves to be an excellent proxy to measure heat. Not only could the micro-FTIR analysis establish a spectral temperature classification (Fig. 12), it also confirmed the optical color-heat relation of the glauconite grains (See for detailed case-study). Furthermore, this case-study exemplifies the potential for interpretation beyond the scale of the thin-section. Due to the basic spatial design, it is easy to integrate and extrapolate the data into larger (site-) scale environments (Fig. 13) such as photogrammetric 3D models.
We would like to thank Golnaz Ahadi and Elizabeth Velliky for qualtity assessment and Sonia Varandas for dealing with organisational questions.
Financial support was provided to MMH by the Meltzer Research Fund and the Travel Fund for Doctoral Fellows at the Department of Archaeology, History, Cultural Studies and Religion, University of Bergen, Norway. Mt. Lykaion Excavation and Survey Project, with the micromorphology component funded by an NSF grant BCS#1125523 to David G. Romano and Mary E. Voyatzis. FTIR was funded by a grant of the Deutsche Forschungsgemeinschaft to Christopher Miller (MI 1748/1-1). Christopher S. Henshilwood was funded by European Research Council Advanced Grant, TRACSYMBOLS No.249587, awarded under the FP7 programme at the University of Bergen, Norway and by a National Research Foundation/Department of Science and Technology funded Chair at the University of the Witwatersrand, South Africa.
All illustrations are made by Matthias Czechowski and under CreativeCommons BY 4.0 License (share, adopt, attribute).
The Fire and Worm illustration is under CC0 License (public domain) and provided by pixabay.com