Archaeological Ceramic Thin Section

By Charles C. Kolb (associate editor, archaeological ceramics)

The following publication reminded me of ceramic-related archaeometric applications that I received while working as Senior Program Office at the National Endowment for the Humanities and proposals that I evaluated for the National Science Foundation (this publication is not related to any of the NEH or NSF applications).  Nonetheless, this is an important article: Magnus M. Haaland, Matthias Czechowski, Frank Carpentier, Mathieu Lejay, and Bruno Vandermeulen  (2019) Documenting archaeological thin sections in highresolution: A comparison of methods and discussion of applications. Geoarchaeology34(1):100-114 ( thin section observations represent the core empirical basis for most micromorphological interpretations at archaeological sites. These observations, which often vary in size and shape, are usually documented through digital graphic representations such as photomicrographs, scans, or figures. Due to variability in documentation practices, however, visual thin section data can be captured with a range of methods and in many different formats and resolutions. In this paper, we compare and evaluate five common imagebased methods for documenting thin sections in highresolution: a flatbed scanner, a film scanner, a macro photography rig, and conventional stereo and light microscopes. Through the comparison results, we demonstrate that advances in digital imaging technology now allow for fast and highresolution visual recording of entire thin sections up to at least ×30 magnification. We suggest that adopting a digital micromorphological documentation practice has several advantages. First, a digital thin section may be observed more efficiently and consistently, for example,on a computer screen, and the spatial configuration of large or complex features may be more accurately documented. Second, they allow for the establishment of digital repositories that may promote scientific reproducibility and interlaboratory communication, as well as lay the foundations for more consensusbased educational training of archaeological micromorphology.
A related paper was presented at a conference a few years ago but, to my knowledge, hasn’t been published: S. Budennyy, A. Pachezhertsev, A. Bukharev, A. Erofeev, D. Mitrushkin, and B. Belozerov (2017) Image processing and machine learning approaches for petrographic thin section analysis.  Conference Paper presented at the SPE[Society of PetroleumEngineers Russian Petroleum Technology Conference, 1618 October 2017, Moscow, Russia.