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.

Multi-Frequency GPR Data Fusion through a Joint Sliding Window and Wavelet Transform-Weighting Method for Top-Coal Structure Detection

“…To quantitatively evaluate the multi-frequency GPR data fusion effectiveness in a laboratory environment, our proposed method was compared with the genetic [11], timevarying [14] and wavelet transform [16] fusion methods based on the IE, SF and LG evaluation criteria. The comparison results are provided in Table 1; it can be seen that the low-frequency GPR profile (900 MHz) has a higher value of IE and LG due to its greater penetration depth, while the high frequency (1500 MHz) possesses a higher value of SF because of its higher resolution.…”

“…Normally, according to the data collection type, it can be classified into three categories: one involves multi-channel GPR antennas placed at one acquisition site, the next involves multi-frequency antennas placed along the same line, and the last one involves the same antenna placed at different locations. Recently, several GPR data fusion methods have been presented and tested by many researchers: Zhao et al [11] presented an adaptively weighted fusion method for multi-frequency GPR data based on genetic algorithms. Xu et al [12] presented a fusion method for ground-penetrating radar data acquired at different center frequencies.…”

GPR multi-frequency data fusion algorithm based on regional energy characteristics

“…Bi et al [8] performed fusion experiments in both the time and frequency domains using conventional weighted fusion techniques and conducted comparative analyses. Zhao et al [9] introduced genetic algorithms to automatically obtain the optimal weight matrix and applied it to archaeological exploration. Some researchers have used the optimal spectral whitening (OSW) [10] technique based on wavelet analysis (e.g., Berlage wavelets [11] as well as Ormsby wavelets [12]) as a weighting factor for radar map synthesis techniques.…”

A methodology for the self‐training and self‐assessing of new GPR practitioners: Measuring diagnostic proficiency illustrated by a case study of a historic African‐American cemetery for unmarked graves

“…Much work has been done and detailed in the literature about the best practices for selecting antenna shape and frequency (Dick et al, 2017;Zhao et al, 2021), whether to collect 2D versus 2.5D versus 3D datasets (Sarris et al, 2018), visualization (Trinks & Hinterleitner, 2020;Yuan et al, 2018) and post-acquisition processing methods (Lu et al, 2020;Manataki et al, 2021) and whether to apply statistics-based classification schemes using textural attributes (Zhao et al, 2013(Zhao et al, , 2015 or hierarchical clustering with Gaussian mixture modelling (Bijamov et al, 2014).…”

“…Typically, the diagnostic proficiency inherent in the GPR technique (and other non‐invasive methods) towards successfully identifying unmarked graves is considered solely in terms of the technological development of hardware, deployment configuration and software. Much work has been done and detailed in the literature about the best practices for selecting antenna shape and frequency (Dick et al, 2017; Zhao et al, 2021), whether to collect 2D versus 2.5D versus 3D datasets (Sarris et al, 2018), visualization (Trinks & Hinterleitner, 2020; Yuan et al, 2018) and post‐acquisition processing methods (Lu et al, 2020; Manataki et al, 2021) and whether to apply statistics‐based classification schemes using textural attributes (Zhao et al, 2013, 2015) or hierarchical clustering with Gaussian mixture modelling (Bijamov et al, 2014).…”