GPR Data Interpretation Approaches in Archaeological Prospection

Abstract

This article focuses on the possible drawbacks and pitfalls in the GPR data interpretation process commonly followed by most GPR practitioners in archaeological prospection. Standard processing techniques aim to remove some noise, enhance reflections of the subsurface. Next, one has to calculate the instantaneous envelope and produce C-scans which are 2D amplitude maps showing high reflectivity surfaces. These amplitude maps are mainly used for data interpretation and provide a good insight into the subsurface but cannot fully describe it. The main limitations are discussed while studies aiming to overcome them are reviewed. These studies involve integrated interpretation approaches using both B-scans and C-scans, attribute analysis, fusion approaches, and recent attempts to automatically interpret C-scans using Deep Learning (DL) algorithms. To contribute to the automatic interpretation of GPR data using DL, an application of Convolutional Neural Networks (CNNs) to classify GPR data is also presented and discussed.

GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection

“…As has been demonstrated by Manataki [17], AI-based processing could also contribute to noise removal, contributing to signal noise separation and the improvement of the resolution of images.…”

“…Additionally, via transfer learning approach, Manataki et.al. demonstrated the use of deep learning (DL) algorithms through the AlexNet architecture for the automatic interpretation of C-scans [17]. They applied CNN for classification purposes divided into three categories, namely unidentified geophysical anomalies/geological features, potential archaeological features, and noise, mainly in stripe form.…”

“…Thus, reliable data annotation carried out by experts in this topic and optimum balanced data augmentation are recommended to reach consistent and accurate results. The proposed annotated data and model for segmentation on time slices [14] and for image classification [17] tasks could be helpful in future studies of AI-based analysis of C-scans. Additionally, the dataset from Verdonck's work [56] and Green's study [16] could be useful for the automated processing of B-scans.…”

“…It has experimented with geospatial data/images (satellite, aerial, lidar), texts, categorical tableau data, point clouds, and other datasets. For instance, one can consider some indicative examples such as the work that has been done on bone classification [1], remote sensing archaeology [2][3][4][5][6][7][8][9][10][11][12], geophysical prospection [13][14][15][16][17], detection of objects in paintings [18], classification of pottery [19], and the 3D reconstruction of heritage buildings [20]. The main reason behind this growing trend, which has been noticed in the last five years in all scientific domains, underlies the nuisance generated when dealing with multivariate analysis of high-volume datasets, which are challenging to process and interpret.…”

From Its Core to the Niche: Insights from GPR Applications

“…Regarding the use of advanced processing such as machine/deep leaning in this field of application, only a few articles are found in the current body of published literature [110,111].…”