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This work shows the preliminary results of an international project for the interdisciplinary study of the limestone used in the plasters of the ancient city of Teotihuacan. The limestone provenance was studied using a new approach based on the chemical analysis of the lime lumps that were selected because they represent the composition of the original limestone rock. The results show that the applied methodology was successful and that the limestone used to produce the lime employed to make the floor of the main courtyard at Teopancazco (Teotihuacan), comes from the region near Tula (Hidalgo).

The identification of chemical activity residues on archaeological surfaces requires the analysis of large numbers of samples, which can be costly and time consuming. Researchers wishing to apply sediment chemistry often are confronted with a dilemma of which technique to use and how to accommodate sediment chemistry into their budget. We propose an approach to the identification of chemical activity residues in which semiquantitative spot tests, which are cheap, quick, and easy to apply, are employed as an initial phase of analysis in order to leverage the results of more timeconsuming and costly instrumental techniques. Three examples that pair spot tests with gas chromatography-mass spectroscopy and inductively coupled plasma-optical emission spectrometry analysis show that spot tests successfully identify areas of interest. This approach can save both time and research funds.

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The objective of this project is to create a new implementation of a deep learning model that uses digital elevation data to detect shipwrecks automatically and rapidly over a large geographic area. This work is intended to apply a new methodology to the field of underwater archaeology. Shipwrecks represent a major resource to understand maritime human activity over millennia, but underwater archaeology is expensive, misappropriated, and hazardous. An automated tool to rapidly detect and map shipwrecks can therefore be used to create more accurate maps of natural and archaeological features to aid management objectives, study patterns across the landscape, and find new features. Additionally, more comprehensive and accurate shipwreck maps can help to prioritize site selection and plan excavation. The model is based on open source topo-bathymetric data and shipwreck data for the United States available from NOAA. The model uses transfer learning to compensate for a relatively small sample size and addresses a recurring problem that associated work has had with false positives by training the model both on shipwrecks and background topography. Results of statistical analyses conducted—ANOVAs and box and whisker plots—indicate that there are substantial differences between the morphologic characteristics that define shipwrecks vs. background topography, supporting this approach to addressing false positives. The model uses a YOLOv3 architecture and produced an F1 score of 0.92 and a precision score of 0.90, indicating that the approach taken herein to address false positives was successful.