Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition

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30 September 2026

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30 November 2026

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Topic Information

Dear Colleagues,

Building on the success of the first edition, the 2nd edition of this Topic aims to further advance and showcase high-quality research in the field of ground penetrating radar (GPR). We continue to invite researchers from diverse disciplines within the journals’ scope to submit innovative papers that highlight the latest developments and applications in their respective areas or to encourage relevant experts and colleagues to contribute. This Topic encompasses, but is not limited to, the following:

• GPR Theory.
• Applications of GPR in Various Fields:
• Architecture;
• Engineering;
• Geology;
• Archaeology and Cultural Heritage;
• Environment;
• Forensic Science;
• Geosciences;
• Water Management.
• GPR Technology Development:
• Design, realisation, and testing of GPR systems and antennas;
• GPR data processing and analysis;
• New data processing algorithms.
• Methodologies and Innovations:
• Modelling and inversion methods for GPR;
• Combined use of GPR and other remote sensing techniques;
• AI and GPR;
• Drone GPR.

We welcome both original research articles and comprehensive review papers. Researchers are also encouraged to submit short proposals for Topic feature papers to our Editorial Office (topics@mdpi.com) prior to submission. Join us in advancing the field of GPR by contributing to this new edition, where cutting-edge research and interdisciplinary collaboration come together.

Dr. Pier Matteo Barone
Dr. Alastair Ruffell
Dr. Vincenzo Sapia
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Forensic Sciences forensicsci	-	2.9	2021	22.2 Days	CHF 1200	Submit
Geosciences geosciences	2.1	5.1	2011	23.6 Days	CHF 1800	Submit
Heritage heritage	1.9	3.7	2018	19.9 Days	CHF 1800	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.3 Days	CHF 2700	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit

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Published Papers (11 papers)

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28 pages, 8165 KB  

Open AccessArticle

Research on the Application of Time-Frequency Characteristics of GPR in Railway Mud Pumping Intelligent Detection

by Wenxing Shi, Shilei Wang, Feng Yang, Chi Zhang, Fanruo Li and Suping Peng

Remote Sens. 2026, 18(9), 1393; https://doi.org/10.3390/rs18091393 - 30 Apr 2026

Viewed by 258

Abstract

Ground penetrating radar (GPR), as an efficient non-destructive testing technique, plays a crucial role in the structural condition assessment and defect identification of railway ballast. Typical defects such as mud pumping generally exhibit characteristics in B-scan images including weak reflections, blurred boundaries, and [...] Read more.

Ground penetrating radar (GPR), as an efficient non-destructive testing technique, plays a crucial role in the structural condition assessment and defect identification of railway ballast. Typical defects such as mud pumping generally exhibit characteristics in B-scan images including weak reflections, blurred boundaries, and irregular structures, which pose significant challenges for stable detection and precise localization using existing methods that rely primarily on spatial feature modeling. Most current deep learning approaches focus on modeling spatial or temporal information, while lacking effective utilization of frequency-domain features, thereby limiting their discriminative capability under complex electromagnetic environments. To address these issues, this paper proposes a single-stage object detection framework, termed YOLO-DGW, based on time-frequency collaborative modeling. Built upon YOLOv8, the proposed method introduces a structure-aware spatial enhancement module to improve the representation of continuous GPR echo structures. Meanwhile, frequency-domain information is incorporated as a modulation prior to guide spatial feature learning, enhancing the model’s sensitivity to weak reflections and complex-shaped targets. In addition, A-CIoU loss function is designed to improve localization accuracy and stability for defect regions of varying scales. Experimental results demonstrate that YOLO-DGW achieves an F1-score of 63.06% and an AP@0.50 of 62.07%, representing improvements of approximately 7.41% and 2.8%, respectively, over the strongest baseline method. Compared with several mainstream object detection models, the proposed approach exhibits superior performance in both detection accuracy and cross-region generalization capability. These findings indicate that integrating frequency-domain information into spatial feature learning through a modulation mechanism can effectively enhance the model’s ability to discriminate weak-reflection anomalies, providing a novel time-frequency collaborative modeling paradigm for railway GPR defect detection. Full article

(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition)

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20 pages, 20358 KB  

Open AccessArticle

A Physics-Guided Quantitative GPR Framework for Detecting Hanging Sleepers in Ballasted Railway Tracks

by Wen Yang, Jie Gao and Zhi Xu

Sensors 2026, 26(6), 1905; https://doi.org/10.3390/s26061905 - 18 Mar 2026

Viewed by 367

Abstract

Sleeper voids, or hanging sleepers, in ballasted railway tracks threaten structural safety and serviceability. This study proposes a physics-guided quantitative ground-penetrating radar (GPR) framework for detecting hanging sleepers using high-frequency antennas ($f\ge 1.5$ GHz). The framework integrates signal post-processing, sleeper-region localization, [...] Read more.

Sleeper voids, or hanging sleepers, in ballasted railway tracks threaten structural safety and serviceability. This study proposes a physics-guided quantitative ground-penetrating radar (GPR) framework for detecting hanging sleepers using high-frequency antennas ($f\ge 1.5$ GHz). The framework integrates signal post-processing, sleeper-region localization, time-domain peak searching with polarity consideration, and continuous wavelet transform (CWT) as auxiliary verification. By exploiting the physical geometric relationship between the sleeper and ballast interfaces, the method quantitatively estimates their elevation difference and identifies hanging sleepers according to engineering criteria. Spatial continuity constraints are further introduced to reduce false detections. Validation through gprMax simulations and field experiments demonstrates effective detection and severity assessments, providing a physically interpretable solution for automated railway inspection. Full article

(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition)

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25 pages, 12794 KB  

Open AccessArticle

Numerical Simulation Analysis of Ground-Penetrating-Radar-Based Advanced Detection Ahead of the Perfect and Irregular Tunnel Face

by Hao Li, Yanqing Wu and Liang Du

Geosciences 2026, 16(3), 99; https://doi.org/10.3390/geosciences16030099 - 27 Feb 2026

Viewed by 555

Abstract

When examining ground-penetrating radar (GPR)-based advanced detection ahead of the tunnel face for tunnel constructions, existing numerical forward simulations have not effectively accounted for the actual orientation of the strata and the conditions, limiting their theoretical guidance. In this study, we classify tunnel [...] Read more.

When examining ground-penetrating radar (GPR)-based advanced detection ahead of the tunnel face for tunnel constructions, existing numerical forward simulations have not effectively accounted for the actual orientation of the strata and the conditions, limiting their theoretical guidance. In this study, we classify tunnel boring through strata attitudes into horizontal, vertical, positively inclined, reverse inclined, and other anomalous structures. We also consider tunnel faces with different planarity (perfectly smooth or irregular). Using the finite-difference time-domain method with a generalized perfectly matched layer, we simulated 21 forward models for GPR-based advanced detection ahead of the tunnel face. The comparative simulation results indicate that the superposition of reflections from different directions at irregular tunnel faces, lithological interfaces, complicated numerical forward models of typical target geological bodies, making it difficult to distinguish the reflection signals of target geological bodies, and the signal strength in numerical forward modeling profiles with antenna touch with tunnel face is significantly stronger than those without such touch. The flatness of the tunnel face and the close proximity between the antenna and tunnel face are the keys to obtain high-quality original data. These research findings will contribute to improving instruments, data processing, and geologic interpretation in future. Full article

(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition)

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23 pages, 37102 KB  

Open AccessArticle

Structural Insights from Non-Destructive Surveys: Moisture, Roof Structure and Subsoil Variability in Santa Maria del Pi

by Vega Perez-Gracia, Oriol Caselles, Jose Ramón Gonzalez Drigo, Viviana Sossa and Jaume Clapes

Geosciences 2026, 16(3), 95; https://doi.org/10.3390/geosciences16030095 - 25 Feb 2026

Viewed by 423

Abstract

Preventive conservation of historic buildings is crucial to avoid extensive damage, yet assessments are often reactive. Following mortar detachment at the Basilica of Santa María del Pi, this paper presents a diagnosis using Non-Destructive Testing (NDT). The study employed Horizontal-to-Vertical Spectral Ratio (HVSR) [...] Read more.

Preventive conservation of historic buildings is crucial to avoid extensive damage, yet assessments are often reactive. Following mortar detachment at the Basilica of Santa María del Pi, this paper presents a diagnosis using Non-Destructive Testing (NDT). The study employed Horizontal-to-Vertical Spectral Ratio (HVSR) for subsoil analysis and Ground Penetrating Radar (GPR) for superstructure inspection. HVSR analysis differentiated fill material from compacted ground, revealing that most of the basilica rests on infilled soil, except the northern corner, suggesting differential settlement risks. Concurrently, GPR survey of vaults and roofs identified internal structures, specifically zones lightened with hollow ceramics, and mapped high-moisture anomalies via wave amplitude and velocity analysis. The study concludes that these methods are complementary, addressing distinct spatial domains. Integrating subsoil characterization with superstructure analysis provided a comprehensive diagnosis essential for long-term maintenance and preservation. Full article

(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition)

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23 pages, 8188 KB  

Open AccessArticle

Enhanced Pix2pixGAN with Spatial-Channel Attention for Underground Medium Inversion from GPR

by Sicheng Yang, Liangshuai Guo, Yahan Yang and Hongxia Ye

Remote Sens. 2026, 18(3), 448; https://doi.org/10.3390/rs18030448 - 1 Feb 2026

Viewed by 571

Abstract

Ground penetrating radar (GPR) data inversion, especially in parallel-layered homogeneous media with multiple subsurface targets, still faces challenges in accurately reconstructing geometric structures due to weak reflections and complex target–medium interactions. To address these limitations, this paper proposes a novel multi-scale inversion framework [...] Read more.

Ground penetrating radar (GPR) data inversion, especially in parallel-layered homogeneous media with multiple subsurface targets, still faces challenges in accurately reconstructing geometric structures due to weak reflections and complex target–medium interactions. To address these limitations, this paper proposes a novel multi-scale inversion framework named GPRGAN-SCSE (Ground Penetrating Radar Generative Adversarial Network with Spatial-Channel Squeeze and Excitation). Built upon the Pix2Pix Generative Adversarial Network (Pix2PixGAN), the proposed model incorporates a Spatial-Channel Squeeze and Excitation (SCSE) module into a residual U-Net generator to adaptively enhance target features embedded in layered media. Furthermore, a tri-scale discriminator ensemble is designed to enforce structural consistency and suppress layer-induced artifacts. The network is optimized using a composite loss integrating adversarial loss, L1 loss, and gradient difference loss to jointly improve structural continuity and boundary sharpness. Experiments conducted on a simulation dataset of parallel-layered homogeneous media with multiple targets demonstrate that GPRGAN-SCSE substantially outperforms existing inversion networks. The proposed method reduces the MAE by 63.8% and achieves a Structural Similarity Index (SSIM) of 99.96%, effectively improving the clarity of subsurface edges and the fidelity of geometric contours. These results confirm that the proposed framework provides a robust and high-precision solution for non-destructive subsurface imaging under layered media conditions. Full article

(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition)

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23 pages, 60825 KB  

Open AccessArticle

A Compact Aperture-Slot Antipodal Vivaldi Antenna for GPR Systems

by Feng Shen, Ninghe Yang, Chao Xia, Tong Wan and Jiaheng Kang

Sensors 2026, 26(3), 810; https://doi.org/10.3390/s26030810 - 26 Jan 2026

Cited by 1 | Viewed by 887

Abstract

Compact antennas with ultra-wideband operation and stable radiation are essential for portable and airborne ground-penetrating radar (GPR), yet miniaturization in the sub 3 GHz region is strongly constrained by the wavelength-driven aperture requirement and often leads to impedance discontinuity and radiation instability. This [...] Read more.

Compact antennas with ultra-wideband operation and stable radiation are essential for portable and airborne ground-penetrating radar (GPR), yet miniaturization in the sub 3 GHz region is strongly constrained by the wavelength-driven aperture requirement and often leads to impedance discontinuity and radiation instability. This paper presents a compact aperture-slot antipodal Vivaldi antenna (AS-AVA) designed under a radiation stability-driven co-design strategy, where the miniaturization features are organized along the energy propagation path from the feed to the flared aperture. The proposed structure combines (i) aperture-slot current-path engineering with controlled meandering to extend the low-frequency edge, (ii) four tilted rectangular slots near the aperture to restrain excessive edge currents and suppress sidelobes, and (iii) back-loaded parasitic patches for coupling-based impedance refinement to eliminate residual mismatch pockets. A fabricated prototype on FR-4 (thickness 1.93 mm) occupies $111.15\times 156.82$ mm² and achieves a measured $S_{11}$ below $-10$ dB from 0.63 to 2.03 GHz (fractional bandwidth 105.26%). The measured realized gain increases from 2.1 to 7.5 dBi across the operating band, with stable far-field radiation patterns; the group delay measured over 0.6–2.1 GHz remains within 4–8 ns, indicating good time-domain fidelity for stepped-frequency continuous-wave (SFCW) operation. Finally, the antenna pair is integrated into an SFCW-GPR testbed and validated in sandbox and outdoor experiments, where buried metallic targets and a subgrade void produce clear B-scan signatures after standard processing. These results confirm that the proposed AS-AVA provides a practical trade-off among miniaturization, broadband matching, and radiation robustness for compact sub 3 GHz GPR platforms. Full article

(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition)

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33 pages, 3316 KB  

Open AccessArticle

An Integrated GPR B-Scan Preprocessing Model Based on Image Enhancement for Detecting Subsurface Pipes

by Zhengyi Shi, Fanruo Li, Hanchao Ma, Hong Huang, Le Wu and Maohua Zhong

Sensors 2025, 25(23), 7202; https://doi.org/10.3390/s25237202 - 25 Nov 2025

Viewed by 857

Abstract

Ground-penetrating radar (GPR) has been proven effective for detecting subsurface pipes in a nondestructive way, typically with manual processing and decision-making. However, existing automatic models for segmenting the target hyperbolas often lack generalization across different pipe radii, varying subsurface media, and complex field [...] Read more.

Ground-penetrating radar (GPR) has been proven effective for detecting subsurface pipes in a nondestructive way, typically with manual processing and decision-making. However, existing automatic models for segmenting the target hyperbolas often lack generalization across different pipe radii, varying subsurface media, and complex field conditions. This is especially reflected in B-scans with diverse or small-scale hyperbolas, often accompanied by cluttered and irregular noise. In this paper, an automatic preprocessing model is proposed to enhance the interpretation of B-scans under challenging conditions. The model includes a ground reflection removal algorithm (GRRA), the data gravitational force enhancement (DGFE) method, and a global–local thresholding technique consisting of dilation-based local thresholding and segmentation (DLTS). First, a frequency-domain filter based on the fast Fourier transform and a spatial filter are applied to the raw B-scan to remove obstructive ground reflection strips. Owing to the minimal intensity differences among the target hyperbola, multiples, and background, the DGFE approach is introduced to amplify the main body of the hyperbola, distinguishing it from the noise. Finally, the target hyperbola is extracted from the grayscale image by an integrated thresholding approach. The approach initially employs global thresholding to eliminate all information except for part of the hyperbola, followed by DLTS, which uses a dilation operation with local thresholding to fully segment the hyperbola. The proposed model is evaluated on two distinct datasets and compared with several state-of-the-art methods. The results demonstrate its effectiveness, particularly in terms of cross-dataset generalization. Full article

(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition)

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16 pages, 3183 KB  

Open AccessCase Report

A Multidisciplinary Approach to Crime Scene Investigation: A Cold Case Study and Proposal for Standardized Procedures in Buried Cadaver Searches over Large Areas

by Pier Matteo Barone and Enrico Di Luise

Forensic Sci. 2025, 5(3), 34; https://doi.org/10.3390/forensicsci5030034 - 1 Aug 2025

Cited by 2 | Viewed by 5040

Abstract

This case report presents a multidisciplinary forensic investigation into a cold case involving a missing person in Italy, likely linked to a homicide that occurred in 2008. The investigation applied a standardized protocol integrating satellite imagery analysis, site reconnaissance, vegetation clearance, ground-penetrating radar [...] Read more.

This case report presents a multidisciplinary forensic investigation into a cold case involving a missing person in Italy, likely linked to a homicide that occurred in 2008. The investigation applied a standardized protocol integrating satellite imagery analysis, site reconnaissance, vegetation clearance, ground-penetrating radar (GPR), and cadaver dog (K9) deployment. A dedicated decision tree guided each phase, allowing for efficient allocation of resources and minimizing investigative delays. Although no human remains were recovered, the case demonstrates the practical utility and operational robustness of a structured, evidence-based model that supports decision-making even in the absence of positive findings. The approach highlights the relevance of “negative” results, which, when derived through scientifically validated procedures, offer substantial value by excluding burial scenarios with a high degree of reliability. This case is particularly significant in the Italian forensic context, where the adoption of standardized search protocols remains limited, especially in complex outdoor environments. The integration of geophysical, remote sensing, and canine methodologies—rooted in forensic geoarchaeology—provides a replicable framework that enhances both investigative effectiveness and the evidentiary admissibility of findings in court. The protocol illustrated in this study supports the consistent evaluation of large and morphologically complex areas, reduces the risk of interpretive error, and reinforces the transparency and scientific rigor expected in judicial settings. As such, it offers a model for improving forensic search strategies in both national and international contexts, particularly in long-standing or high-profile missing persons cases. Full article

(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition)

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19 pages, 3825 KB  

Open AccessArticle

A Semi-Supervised Attention-Temporal Ensembling Method for Ground Penetrating Radar Target Recognition

by Li Liu, Dajiang Yu, Xiping Zhang, Hang Xu, Jingxia Li, Lijun Zhou and Bingjie Wang

Sensors 2025, 25(10), 3138; https://doi.org/10.3390/s25103138 - 15 May 2025

Cited by 2 | Viewed by 1450

Abstract

Ground penetrating radar (GPR) is an effective and efficient nondestructive testing technology for subsurface investigations. Deep learning-based methods have been successfully used in automatic underground target recognition. However, these methods are mostly based on supervised learning, requiring large amounts of labeled training data [...] Read more.

Ground penetrating radar (GPR) is an effective and efficient nondestructive testing technology for subsurface investigations. Deep learning-based methods have been successfully used in automatic underground target recognition. However, these methods are mostly based on supervised learning, requiring large amounts of labeled training data to guarantee high accuracy and generalization ability, which is a challenge in GPR fields due to time-consuming and labor-intensive data annotation work. To alleviate the demand for abundant labeled data, a semi-supervised deep learning method named attention–temporal ensembling (Attention-TE) is proposed for underground target recognition using GPR B-scan images. This method integrates a semi-supervised temporal ensembling architecture with a triplet attention module to enhance the classification performance. Experimental results of laboratory and field data demonstrate that the proposed method can automatically recognize underground targets with an average accuracy of above 90% using less than 30% of labeled data in the training dataset. Ablation experimental results verify the efficiency of the triplet attention module. Moreover, comparative experimental results validate that the proposed Attention-TE algorithm outperforms the supervised method based on transfer learning and four semi-supervised state-of-the-art methods. Full article

(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition)

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13 pages, 6578 KB  

Open AccessArticle

A Circularly Polarized Broadband Composite Spiral Antenna for Ground Penetrating Radar

by Hai Liu, Shangyang Zhang, Pei Wu, Xu Meng, Junyong Zhou and Yanliang Du

Sensors 2025, 25(6), 1890; https://doi.org/10.3390/s25061890 - 18 Mar 2025

Viewed by 3100

Abstract

To enhance the capability of a ground penetrating radar (GPR) in subsurface target identification and improve its polarization sensitivity in detecting underground linear objects, a circularly polarized broadband composite spiral antenna was designed. This antenna integrates equiangular spiral and Archimedean spiral structures, achieving [...] Read more.

To enhance the capability of a ground penetrating radar (GPR) in subsurface target identification and improve its polarization sensitivity in detecting underground linear objects, a circularly polarized broadband composite spiral antenna was designed. This antenna integrates equiangular spiral and Archimedean spiral structures, achieving a wideband coverage of 1–5 GHz with stable circular polarization characteristics. The antenna employs an exponentially tapered microstrip balun for impedance matching and a metallic-backed cavity filled with absorbing materials to enhance its directivity. Experimental results demonstrate excellent radiation performance and stable circular polarization characteristics, with the axial ratio consistently below 3 dB throughout the operating frequency band, highlighting its suitability for polarimetric GPR systems. Furthermore, a 3D GPR measurement using the designed antenna validates its improved capacity for detecting subsurface linear objects, compared to the conventional linearly polarized bowtie antenna. Full article

(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition)

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19 pages, 6020 KB  

Open AccessArticle

Numerical Simulation Study on the Impact of Blind Zones in Ground Penetrating Radar

by Wentian Wang, Wei Du, Siyuan Cheng and Jia Zhuo

Sensors 2025, 25(4), 1252; https://doi.org/10.3390/s25041252 - 18 Feb 2025

Cited by 3 | Viewed by 1376

Abstract

Ground-penetrating radar (GPR) is an effective geophysical method for rapid and non-destructive detection. Directional borehole radar is the application of GPR in a borehole, which can determine the depth, orientation, and distance of the target from the borehole. The borehole radar azimuth recognition [...] Read more.

Ground-penetrating radar (GPR) is an effective geophysical method for rapid and non-destructive detection. Directional borehole radar is the application of GPR in a borehole, which can determine the depth, orientation, and distance of the target from the borehole. The borehole radar azimuth recognition algorithm is based on the assumption of far-field plane waves. Therefore, in the near-field area where the target is closer to the borehole, the electromagnetic waves reflected by the target cannot be regarded as plane waves but will have a certain curvature. The plane wave assumption is not valid in this area, so the azimuth recognition algorithm will have significant errors, forming blind zones for directional borehole radar detection. This article uses the finite-difference time-domain (FDTD) algorithm to numerically simulate how blind zones affect directional borehole radar systems, identify the impact patterns, and minimize them. After calculation and numerical simulation verification, it has been found that when the center frequency of the antenna is 1 GHz, within 2 m of the target from the borehole, there is a significant error in azimuth recognition, which can be defined as the near-field region. Similarly, through numerical simulation verification, the optimal antenna center frequency is between 600 MHz and 1100 MHz. Oil-based mud is superior to water-based mud. The optimal antenna center frequency decreases as the target distance increases. Full article

(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications, 2nd Edition)

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