Integration of multiple geophysical datasets to classify archaeological responses

Author(s): Timothy De Smet

Year: 2017


North American archaeologists are increasing using multiple near-surface geophysical techniques at archaeological sites to locate features of interest. Examining different physical properties in the subsurface has greatly improved archaeological interpretations; however, these data are often examined in a subjective site specific fashion (notable exceptions are the pioneering work of Kvamme and Ernenwein). This research seeks to quantitatively integrate magnetic gradiometry, frequency-domain electromagnetic-induction (magnetic susceptibility and apparent conductivity), and ground-penetrating radar data from the Magnolia Valley site, Rutherford County, Tennessee. Moreover, this research examines the effects of novel processing techniques (including reduction-to-the-pole, attribute analysis, and Hilbert transform) on the correlation structure of the data in order to improve subsequent classification via supervised and unsupervised learning. A short term goal of these data, or information, fusion techniques is the ability to statistically predict archaeological geophysical responses based upon geophysics and limited archaeological testing. The long term goal of this research program is the stewardship and preservation of the archaeological record, where archaeogeophysics can be used as a standalone method to answer fundamental anthropological research questions about human behavior, social organization, and cultural change through time – without costly and destructive excavation.

Cite this Record

Integration of multiple geophysical datasets to classify archaeological responses. Timothy De Smet. Presented at The 81st Annual Meeting of the Society for American Archaeology, Vancouver, British Columbia. 2017 ( tDAR id: 430565)

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Geographic Keywords
North America - Southeast

Spatial Coverage

min long: -91.274; min lat: 24.847 ; max long: -72.642; max lat: 36.386 ;

Record Identifiers

Abstract Id(s): 16033