Multi-Frequency GPR Data Fusion with Genetic Algorithms for Archaeological Prospection

Abstract

Archaeological GPR data from antennas of different frequencies allow the identification of buried cultural heritage at different scales. Therefore, multi-frequency GPR systems are recommended for complicated subsurface archaeological conditions. GPR data fusion approaches, automatically or semi-automatically, can integrate data measurements from different frequency antennas, combine them into a single representation, and partially overcome the unavoidable trade-off between penetration and resolution. We propose an adaptively weighted fusion method for multi-frequency GPR data based on genetic algorithms (GAs). In order to evaluate the feasibility and the effectiveness of the strategy for archaeological prospection, we tested the procedure on GPR datasets acquired in two totally different archaeological conditions: rammed layers of an ancient wall, in Henan Province, China, and complex and elusive prehistoric archaeological features within a natural stratigraphic sequence on the volcanic Stromboli Island, Italy. The results demonstrate that the proposed strategy can maximize the information content of GPR profiles, enhancing the GPR interpretation possibilities in an automatic and objective way for different targets and in different subsurface conditions.