Integrating FDEM data with K-Means clustering for improved archaeological site identification

The Frequency Domain Electromagnetic (FDEM) method is a cost-effective geophysical technique that simultaneously studies the electrical and magnetic properties of a medium, providing data as in-phase and out-of-phase components of the electromagnetic field. Although FDEM yields valuable insights, its results can be complex to interpret, and the two EM field components are normally only visually inspected to support findings from other techniques. This study aims to enhance FDEM data interpretation using an unsupervised learning technique. The proposed approach seeks to automate and expedite the interpretative phase. By applying the K-Means clustering algorithm, we divided the FDEM data into several clusters based on specific intervals of the in-phase and quadrature components, resulting in integrated maps of EM components. Combining these maps with geological and archaeological insights helped identifying areas of potential archaeological interest. This method was applied to the Torre Galli archaeological site in Calabria, Italy, known for its significance in Iron Age studies.

Based on comparisons with the findings of earlier excavations and results from a magnetic survey, the proposed procedure shows promise in improving the efficiency and accuracy of the FDEM method in identifying areas of archaeological interest. This suggests that automating the interpretation process could lead to a better cost management and time optimization in geophysical and archaeological studies.