Multi-channel Ground Penetrating Radar Research Articles

Integration of satellite and aerial images with multichannel GPR surveys in the archaeological area of Augusta Bagiennorum for an improved description of the urban setting

The archaeological area of Augusta Bagiennorum (NW Italy) is a well-known archaeological site, extensively investigated since the end of the 19th century. Previous archaeological studies in the area allowed a preliminary definition of the urban setting through several direct soundings. However, the current knowledge of the site needs to be more complete due to the limited extension of previous archaeological surveys. To obtain a more complete description of the urban setting, we analysed aerial and satellite images and multichannel Ground Penetrating Radar (GPR) surveys, which are two remote-sensing technologies often used in this field. Aerial images showed vegetation cropmark patterns in different areas, depending on the time of the day, year, and camera acquisition setup. The GPR surveys locally confirmed the presence of buried structures in correspondence with the cropmarks, and provided a more detailed view of the underground features. The GPR was also helpful in adding Ground Control Points for georeferencing hot-air balloon images. The combined view of aerial and GPR images allowed us to confirm the overall extent of the archaeological area, as reported in past archaeological reports. However, some of the observed structures were located outside the previously known extent of the town. The orientation of some suggests that the streets of Augusta Bagiennorum are not always arranged in a perpendicular pattern, typical of the Roman orthogonal town planning. The adopted methodological approach could be a valuable tool in similar case studies, since it provides multiscale information composed of both overall views and detailed images.

Probing multilayer road structures using the GPR “Terrazond”

Abstract The paper describes a method for obtaining information about the thickness of road pavement layers using the “Terrazond” multichannel ground penetrating radar. The method includes the use of signal pre-processing to eliminate common-mode interference on radar data of the road surface. The results of applying the methodology to data obtained during sounding of a road test site are presented. Using the example of a radar data of a test section, it is shown that three iterations of the proposed filtering contrastively highlight three layers of the road structure. The results of automated construction of the boundaries of road layers in the GeoReader program using machine learning technology are shown.

Development of a Background Filtering Algorithm to Improve the Accuracy of Determining Underground Cavities Using Multi-Channel Ground-Penetrating Radar and Deep Learning

In the process of using multi-channel ground-penetrating radar (GPR) for underground cavity exploration, the acquired 3D data include reflection data from underground cavities or various underground objects (structures). Reflection data from unspecified structures can interfere with the identification process of underground cavities. This study aims to identify underground cavities using a C-GAN model with an applied ResBlock technique. This deep learning model demonstrates excellent performance in the image domain and can automatically classify the presence of cavities by analyzing 3D GPR data, including reflection waveforms (A-scan), cross-sectional views (B-scan), and plan views (C-scan) measured from the ground under roads. To maximize the performance of the C-GAN model, a background filtering algorithm (BFA) was developed and applied to enhance the visibility and clarity of underground cavities. To verify the performance of the developed BFA, 3D data collected from roads in Seoul, Republic of Korea, using 3D GPR equipment were transformed, and the C-GAN model was applied. As a result, it was confirmed that the recall, an indicator of cavity prediction, improved by approximately 1.15 times compared to when the BFA was not applied. This signifies the verification of the effectiveness of the BFA. This study developed a special algorithm to distinguish underground cavities. This means that in the future, not only the advancement of separate equipment and systems but also the development of specific algorithms can contribute to the cavity exploration process.

Multiview Multistatic vs. Multimonostatic Three-Dimensional Ground-Penetrating Radar Imaging: A Comparison

The availability of multichannel ground-penetrating radar systems capable of gathering multiview, multistatic, multifrequency data provides novel chances to improve subsurface imaging results. However, customized data processing techniques and smart choices of the measurement setup are needed to find a trade-off between image quality and acquisition time. In this paper, we adopt a Born Approximation-based full 3D approach, which can manage multiview-multistatic, multifrequency data and faces the imaging as a linear inverse scattering problem. The inverse problem is solved by exploiting the truncated singular value decomposition as a regularization scheme. The paper presents a theoretical study aimed at assessing how the reconstruction capabilities of the imaging approach depend on the adopted measurement configuration. In detail, the performance achievable in the standard case of multimonostatic, multifrequency data is compared with that provided by a multiview-multistatic, multifrequency configuration, where the data are gathered by considering a progressively increasing number of transmitting antennas. The comparison of the achievable imaging performance is carried out by exploiting the spectral content and the point spread function, which are general tools to foresee the achievable reconstruction capabilities. Reconstruction results related to a numerical experiment based on full-wave data are also provided.

The 3D localization of subsurface pipes from Ground Penetrating Radar images using edge detection and point cloud segmentation

This research introduces an innovative algorithm using Ground Penetrating Radar (GPR) for inspecting underground infrastructure non-destructively, surpassing R-CNN methods by combining signal processing with computer vision techniques including hyperbola correlation, point cloud segmentation and noise reduction based on linearity, density and echo patterns. Utilizing on-vehicle multi-channel GPR and without training, it maps pipes in 3D with high precision on both experimental fields and real roads. The method demonstrated remarkable accuracy, with an average error of 0.1175 m in locating pipes, outperforming deep learning methods requiring numerous training data. The ablation study and sensitivity analysis on hyperparameters further prove the improvement from the proposed method and its robustness. These outcomes offer notable improvements in infrastructure monitoring, highlighting the potential for automated underground utility detection.

Automatic Object Detection in Radargrams of Multi-Antenna GPR Systems Based on Simulation Data for Railway Infrastructure Analysis

Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth—accurate real-world data used to validate machine learning models—thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations.

Management of cavities under flexible pavement road network in metropolitan area: Detection, evaluation, and rehabilitation

This study evaluated a management program of cavities under flexible pavement road network in Seoul Metropolitan City. Consequently, directions for improvement were suggested. The underground cavities beneath the pavement layer were scanned using a vehicle-type multichannel ground penetrating radar (GPR). In addition, the size information including overburden thickness, cavity height, and lengths in longitudinal and transverse directions of the cavities were estimated and compared with the volume of cavities measured via a digital cavity imaging (DCI) system. Moreover, the change in the cavity dimensions with time was monitored. Through field investigation, the cavity volume near the asphalt pavement surface was observed to be smaller than the cavity located in the deep. For certain locations where the cavity existed, pavement cracks on the asphalt surface were identified. Thus, the crack width and depth were measured for the selected 14 locations and compared with the dimensions of the cavities. The results demonstrated that the depth of crack rather than the width exhibited a meaningful correlation with the cavity size. Additionally, the cavities under the flexible pavement layer were repaired using a low-strength filling repair material. Further, the elastic modulus on the pavement surface above a cavity was assessed using a light weight deflectometer (LWD) before and after the rehabilitation process and compared with the value of elastic modulus on the normal surface with no cavity but near the repair work. The results demonstrated that although the rehabilitation process increased the elastic modulus of the pavement layer, it could not be fully recovered owing to the characteristics of the repair material. Furthermore, the rehabilitation process was found to be more effective as the size of cavity was small.

Drone applications in hydrogeophysics: Recent examples and a vision for the future

The recent boom of commercially available drone technology capable of supporting geophysical imaging systems has led to several practitioners adopting the platforms to collect geophysical data. This has led to significant opportunities for studying hydrologic systems, as these platforms enable more efficient data collection and data collection in difficult-to-access locations. Recent examples from snow and stream studies show the viability of these platforms for studying complex systems in difficult locations and the added value of data provided by the technology. Small unoccupied aerial system (sUAS) ground-penetrating radar (GPR) surveys conducted over water bodies and snow have proven beneficial for determining bathymetry in dangerous swift water and depth of snowpack in mountainous regions. Challenges arise due to decoupling of the GPR antenna and the ground surface, which limits signal penetration. Additionally, multifrequency or multichannel GPR imaging remains a challenge due to the payload limitations of an sUAS. Drone-mounted thermal infrared data collection enables the generation of thermal orthomosaic images with unprecedented detail and spatial range for quantifying groundwater fluxes to surface water bodies and reduces issues with infrared reflection common in ground-based studies. Several drone-mounted electromagnetic studies also show utility in efficient, high-resolution mapping of near-surface soil conductivity. Overall, the use of drone platforms for studying hydrologic systems remains a frontier in science applications that needs further refinement before wide-scale adoption. The preliminary studies presented here hint that the development of robust geophysical data acquisition tools based on UAS will lead to an explosion of applications to near-surface hydrologic problems.

Precise estimation of subsurface moisture content based on laboratory measurement and 3D GPR field survey

The Sponge City construction currently undergoing in China aims to enhance the moisture capacity of urban pedosphere, and subsurface moisture content (SMC) is proposed to evaluate the effect of improvement. Different ground-penetrating radar (GPR) techniques have been used in previous studies to estimate moisture content, however, very few has assessed the potential of combing 2D common offset (CO) GPR with 3D multichannel GPR to resolve SMC estimates at the complex filed scale. In this paper, modified velocity analysis of in-situ 3D GPR common-midpoint (CMP) datasets was used together with 2D GPR laboratory calibration procedure to investigate spatial moisture variation and the impact of surface pavement and soil heterogeneity on subsurface water dynamics. Precise and reliable moisture information under two field sites (i.e., sponge pavement site and asphalt pavement site) was obtained, which further allowed us to quantitative characterize SMC distribution and its associated dynamics within the shallow vadose zone, and most importantly to investigate the coupling mechanism between subsurface water fluctuations with surface pavement conditions and subsoil moisture capacity in urban near surface environment. Additionally, through contrastive analysis, the superior performance of novel sponge material to detain and transit rainwater was highlighted. Data and results presented in this research provide pioneer reference for Sponge City construction and other engineering applications.

Finding Mesolithic Sites: A Multichannel Ground-Penetrating Radar (GPR) Investigation at the Ancient Lake Duvensee

The shift to the early Holocene in northern Europe is strongly associated with major environmental and climatic changes that influenced hunter-gatherers’ activities and occupation during the Mesolithic period. The ancient lake Duvensee (10,000–6500 cal. BCE) has been studied for almost a century, providing archaeological sites consisting of bark mats and hazelnut-roasting hearths situated on small sand banks deposited by the glacier. No method is yet available to locate these features before excavation. Therefore, a key method for understanding the living conditions of hunter-gatherer groups is to reconstruct the paleoenvironment with a focus on the identification of areas that could possibly host Mesolithic camps and well-preserved archaeological artefacts. We performed a 16-channel MALÅ Imaging Radar Array (MIRA) system survey aimed at understanding the landscape surrounding the find spot Duvensee WP10, located in a hitherto uninvestigated part of the bog. Using an integrated approach of high-resolution ground radar mapping and targeted excavations enabled us to derive a 3D spatio-temporal landscape reconstruction of the investigated sector, including paleo-bathymetry, stratigraphy, and shorelines around the Mesolithic camps. Additionally, we detected previously unknown islands as potential areas for yet unknown dwelling sites. We found that the growth rates of the islands were in the order of approximately 0.3 m2/yr to 0.7 m2/yr between the late Preboreal and the Subboreal stages. The ground-penetrating radar surveying performed excellently in all aspects of near-surface landscape reconstruction as well as in identifying potential dwellings; however, the direct identification of small-scale artefacts, such as fireplaces, was not successful because of their similarity to natural structures.

A Study of the Application and the Limitations of GPR Investigation on Underground Survey of the Korean Expressways

In this study, the applications and the limitations of the Ground-penetrating radar (GPR) investigation have been addressed with the main objective of improving the efficient GPR application of subsurface surveys on Korean expressways. The depth of investigation and detection performance of anomalous objects have been studied using two different types of multichannel GPR on the Korean Expressway Corporation’s nondestructive testing testbed for subsurface detection. Based on the field survey, it was found that utilizing the plane view by depth, cross-sectional and longitudinal profile data of the multichannel GPR simultaneously, analysis and evaluation of the GPR signals are more efficient and practical. Although there was a difference in the frequency of use, the precision difference between two GPR is almost similar in the investigation depth and detection performance of the pavement subsurface anomaly. Under an asphalt concrete standard pavement section, the effective depth of cavity detection is 1–1.5 m, while detection under concrete pavement is less than 1.0 m. In addition, there is still a need to calibrate depths using field cores when constructing a 3D underground facility map.

Technique for Detecting Subsurface Cavities of Urban Road Using Multichannel Ground-penetrating Radar Equipment

Technique for Detecting Subsurface Cavities of Urban Road Using Multichannel Ground-penetrating Radar Equipment

Georeferencing of Multi-Channel GPR—Accuracy and Efficiency of Mapping of Underground Utility Networks

Due to the capabilities of non-destructive testing of inaccessible objects, GPR (Ground Penetrating Radar) is used in geology, archeology, forensics and increasingly also in engineering tasks. The wide range of applications of the GPR method has been provided by the use of advanced technological solutions by equipment manufacturers, including multi-channel units. The acquisition of data along several profiles simultaneously allows time to be saved and quasi-continuous information to be collected about the subsurface situation. One of the most important aspects of data acquisition systems, including GPR, is the appropriate methodology and accuracy of the geoposition. This publication aims to discuss the results of GPR measurements carried out using the multi-channel Leica Stream C GPR (IDS GeoRadar Srl, Pisa, Italy). The significant results of the test measurement were presented the idea of which was to determine the achievable accuracy depending on the georeferencing method using a GNSS (Global Navigation Satellite System) receiver, also supported by time synchronization PPS (Pulse Per Second) and a total station. Methodology optimization was also an important aspect of the discussed issue, i.e., the effect of dynamic changes in motion trajectory on the positioning accuracy of echograms and their vectorization products was also examined. The standard algorithms developed for the dedicated software were used for post-processing of the coordinates and filtration of echograms, while the vectorization was done manually. The obtained results provided the basis for the confrontation of the material collected in urban conditions with the available cartographic data in terms of the possibility of verifying the actual location of underground utilities. The urban character of the area limited the possibility of the movement of Leica Stream C due to the large size of the instrument, however, it created the opportunity for additional analyses, including the accuracy of different location variants around high-rise buildings or the agreement of the amplitude distribution at the intersection of perpendicular profiles.

Three-dimensional convolutional neural network–based underground object classification using three-dimensional ground penetrating radar data

Ground-penetrating radar is a typical sensor system for analyzing underground facilities such as pipelines and rebars. The technique also can be used to detect an underground cavity, which is a potential sign of urban sinkholes. Multichannel ground-penetrating radar devices are widely used to detect underground cavities thanks to the capacity of informative three-dimensional data. Nevertheless, the three-dimensional ground-penetrating radar data interpretation is unclear and complicated when recognizing underground cavities because similar ground-penetrating radar data reflected from different underground objects are often mixed with the cavities. As it is prevalently known that the deep learning algorithm-based techniques are powerful at image classification, deep learning-based techniques for underground object detection techniques using two-dimensional GPR (ground-penetrating radar) radargrams have been researched upon in recent years. However, spatial information of underground objects can be characterized better in three-dimensional ground-penetrating radar voxel data than in two-dimensional ground-penetrating radar images. Therefore, in this study, a novel underground object classification technique is proposed by applying deep three-dimensional convolutional neural network on three-dimensional ground-penetrating radar data. First, a deep convolutional neural network architecture was developed using three-dimensional convolutional networks for recognizing spatial underground objects such as, pipe, cavity, manhole, and subsoil. The framework of applying the three-dimensional convolutional neural network into three-dimensional ground-penetrating radar data was then proposed and experimentally validated using real three-dimensional ground-penetrating radar data. In order to do that, three-dimensional ground-penetrating radar block data were used to train the developed three-dimensional convolutional neural network and to classify unclassified three-dimensional ground-penetrating radar data collected from urban roads in Seoul, South Korea. The validation results revealed that four underground objects (pipe, cavity, manhole, and subsoil) are successfully classified, and the average classification accuracy was 97%. In addition, a false alarm was rarely indicated.

Multi-Channel Ground-Penetrating Radar Array Surveys of the Iron Age and Medieval Ringfort Bårby on the Island of Öland, Sweden

As a part of the project “The Big Five”, large-scale multi-channel ground-penetrating radar surveys were carried out at Bårby ringfort (Swedish: borg), Öland, Sweden. The surveys were carried out using a MALÅ Imaging Radar Array (MIRA) system and aimed at mapping possible buried Iron Age and Medieval remains through the interior in order to better understand the purpose of the fort during its periods of use. An additional goal was to evaluate the impact of earlier farming on the preservation of the archaeological remains. The data provided clear evidence of well-preserved Iron Age and Medieval buildings inside the fort. The size and the pattern of the Iron Age houses suggest close similarities with, for example, the previously excavated fort at Eketorp on Öland. Given the presence of a substantial cultural layer together with a large number of artefacts recovered during a metal detection survey, it is suggested that Bårby borg’s primary function during the Iron Age was as a fortified village. The Medieval houses partly cover some of the Iron Age buildings. They are placed in a U-shape with an open square in the middle facing the edge of a limestone cliff. As in the case of Eketorp, it is suggested that the activities during Medieval times changed, but the precise purpose of the Medieval Bårby settlement is still a question open for debate. Future targeted archaeological investigations are needed in order to better understand its purpose. Rescue excavations may also be necessary, as the western steep cliff ledge is eroding and the well-preserved archaeological remains are at risk of being destroyed.

Simultaneous multichannel multi‐offset ground‐penetrating radar measurements for soil characterization

AbstractFor vadose zone studies, it is essential to characterize the soil heterogeneity. However, manual soil coring is time consuming and lacks spatial coverage. Ground‐penetrating radar (GPR) has a high potential to map these parameters. However, with conventional common‐offset profile (COP) measurements, soil layer changes are only detected as a function of time, and no exact determination of velocities, and thus permittivity, is possible. For velocity estimation, time‐consuming point‐scale common midpoint (CMP) or wide‐angle reflection and refraction (WARR) measurements are necessary. Recently, a novel simultaneous multi‐offset multichannel (SiMoc) GPR system was released, enabling rapid profiling with virtually continuous acquisition of WARR gathers. For this system, we developed a new processing approach. First, time shifts caused by the different cables and receivers were eliminated by a novel calibration method. In the obtained CMP gathers, groundwave and (when present) reflection velocities were determined with an automated semblance approach. The obtained velocity can be converted to permittivity and soil water content. We tested SiMoc GPR with a synthetic study and time‐lapse field measurements. In the synthetic study, the accuracy of velocity and layer thickness were within 0.02 m ns−1 and 2 cm. The SiMoc field results (spatial sampling of 5 cm) are consistent with coarse sampled single‐channel data (spatial sampling of 10 m). Soil water content changes over the different measurement days were in agreement with nearby installed sensors (one per hectare). Overall, SiMoc GPR is a powerful tool for fast imaging of spatially highly resolved permittivity, and soil water content at a large scale.

Airborne Multi-Channel Ground Penetrating Radar for Improvised Explosive Devices and Landmine Detection

An improved Ground Penetrating Radar (GPR) system on board an Unmanned Aerial Vehicle (UAV) is presented in this contribution. The system has been designed for the detection and imaging of buried targets and, in particular, landmines and Improvised Explosive Devices (IEDs). Resting on the hardware and architecture of a previous aerial platform, in the proposed system the scanning area is increased and the detection capabilities are improved. These improvements are achieved by employing two receiving antennas and new processing techniques that increase the Signal-to-Clutter Ratio of the GPR images. Besides, parameters affecting the GPR image resolution, such as the flight speed and the amount of measurements that can be processed together using Synthetic Aperture Radar (SAR) techniques, are also studied. The developed system exhibits several advantages: safety and faster scanning speeds, together with the capability to detect both metallic and non-metallic targets, as shown in the examples presented in this contribution.

Triplanar Imaging of 3-D GPR Data for Deep-Learning-Based Underground Object Detection

This article proposes a deep-learning-based underground object classification technique that uses triplanar ground-penetrating radar images consisting of B-, C-, and D-scan images. Although multichannel ground-penetrating radar (GPR) systems provide three-dimensional (3-D) information about underground objects, there is currently no suitable technique available for processing 3-D data as opposed to 2-D images. In this article, a triplanar deep convolutional neural network technique is proposed for use in processing 3-D GPR data for use in automatized underground object classification. The proposed method was validated experimentally using 3-D GPR road scanning data obtained from urban roads in Seoul, South Korea. In addition, the classification performance of the method was compared to that of a conventional method that uses only B-scan-images. The results of the validation and comparison tests reveal that the classification performance of the proposed technique is notably better than that of the conventional B-scan-image-based method and that its use results in decrease misclassification ratios.

Archaeological Prospection with Motorised Multichannel Ground-Penetrating Radar Arrays on Snow-Covered Areas in Norway

The technical advancements of the past decade have rendered motorised, high-resolution ground-penetrating radar (GPR) investigations increasingly popular for archaeological research and cultural heritage management in Norway. However, the agricultural use of most survey areas limits the time available for fieldwork in spring and autumn and thus reduces the method’s potential. An extension of the fieldwork period into the winter season would be desirable. The project “Arkeologi i veien?” aimed to develop practical solutions for efficient motorised GPR surveys on snow and to evaluate to what extent the thickness of the snow cover affects data quality. Four sites with known archaeological remains in the ground have been investigated under snowless conditions and with snow cover. The comparative data analysis showed that GPR surveys can result in useful data even on areas covered with one metre of snow. This study shows that different temperatures and resulting variable snow conditions can have a strong effect on the quality of the generated GPR data. The possibility for GPR measurements on snow offers the opportunity to extend fieldwork into the winter period without conflicting with the growing season; however, local weather and snow conditions have to be closely observed in order to obtain useful prospection data.

A novel 3D GPR image arrangement for deep learning-based underground object classification

ABSTRACT Ground-penetrating radar (GPR) is widely used for detecting buried underground object. Deep learning technique is recently being adopted into this field thanks to its powerful image classification capacity. However, it uses only GPR B-scan images, although multichannel GPR device can provide more informative three-dimensional (3D) data for underground object. In this study, a novel deep learning-based underground object classification method is proposed by using two-dimensional (2D) grid image which consists of several B-scan and C-scan images. Spatial information of an underground object can be well represented in the 2D grid image. The 2D grid images are then used to train deep convolutional neural networks. The proposed method is experimentally validated by field data collected from urban roads in Seoul, South Korea. The performance is also compared to a conventional method which uses only B-scan images. The proposed method successfully classifies cavity, pipe, manhole and subsoils background having very small false-positive errors.