Deposit modeling for archaeological projects: Methods, practice, and future developments

Abstract

This paper considers the application of deposit modeling within archaeological projects, summarizing the results of a Historic England funded project aimed at developing “good practice” for the application of these techniques within terrestrial environments. It provides an overview of what deposit modeling is, the environments where it can be used and the methods most commonly used. The paper identifies a series of issues and challenges in using deposit models within archaeological investigations, such as the archiving of geotechnical data, the testing of deposit models against the distribution of archaeological sites and features, and integrating sediment stratigraphies derived from deposit modeling with post‐excavation records. We argue that deposit modeling is a valuable tool for archeologists to employ and can be used to design appropriate excavation strategies to target sites and features, thereby increasing the information yield of a project. In this sense deposit modeling can be used as a framework for archaeological projects that are investigating sediment sequences in complex geomorphological environments, starting with pre‐evaluation data gathering, through to focusing excavation strategies, and within the post‐excavation analysis. Whilst this paper is focused upon the terrestrial archaeological record, the themes discussed have resonance for archaeological records across the globe, including the offshore zone.

Cone penetration testing for extensive mapping of deeply buried Late Glacial coversand landscape paleotopography

“…CPT‐Es can be applied purposefully within an archaeological assessment study in a development‐led archaeological context. Parallel with this, existing CPT‐E data from geotechnical studies or from national subsurface data archives can be reused because CPT‐E data are largely operator‐independent in contrast to core descriptions (Carey et al, 2019). Geotechnical CPT‐E data potentially hold unexplored geoarchaeological information, because thin (Late Glacial) sediment layers are rarely interpreted in geological engineering studies.…”

“…Many paleoenvironmental reconstructions of the NW European Late Glacial coversand belt investigate a limited number of core or trench locations to characterize the environment (e.g., Bos et al, 2013; Derese et al, 2012; Tolksdorf et al, 2014). In contrast, geoarchaeological land evaluations attempt to map the paleotopography using (litho‐)stratigraphic observations on many locations (Bates, Barham, Pine, & Williamson, 2000) to create (archaeological) deposit models (e.g., Carey, Howard, Corcoran, Knight, & Heathcote, 2019; Carey, Howard, Knight, Corcoran, & Heathcote, 2018). Indeed, precise reconstructions of the Late Glacial landscape and its successive changes are essential to determine new potential site locations as well as other archaeological research questions (see Bates, 2018).…”

Sampled to Death? The Rise and Fall of Probability Sampling in Archaeology

“…In site excavation, purposive sampling is typical, while selection of excavated contexts for detailed analysis often occurs with little or no explanation (e.g., Douglass et al 2008). Excavation directors understandably use expertise and experience or, at times, deposit models (sometimes based on purposive auger samples) to decide which parts of sites might best provide evidence relevant to their research questions (Carey et al 2019). However, at least one study outside my sample used spatial sampling to estimate the number of features (Welch 2013).…”

Assessing natural versus anthropogenic horizons through deposit modelling strategies at the Medieval Site of Santa Margarida (Martorell, Barcelona, Spain)

“…Distinguishing this geological interface between clayey and silty layers with extremely feeble human evidence (or none at all) required a more precise assessment of the geological environment features, for which some proposals have been made in the past. Existing literature on this topic (Artz, 2006; Carey et al, 2019; David et al, 2008, pp. 498–499; Kokinou, 2015, pp.…”