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Advanced Ground-Penetrating Radar (GPR) Technologies and Applications

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 9099

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Special Issue Editors

Prof. Dr. Xuan Feng

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Guest Editor

College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Interests: research on the theory; technology, and application of ground penetrating radar
Special Issues, Collections and Topics in MDPI journals

Dr. Haoqiu Zhou

E-Mail Website
Guest Editor

College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Interests: signal processing; ground penetrating radar; planetary science
Special Issues, Collections and Topics in MDPI journals

Dr. Zejun Dong

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Guest Editor

College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Interests: numerical simulation of ground penetrating radar; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

GPR utilizes electromagnetic waves to detect subsurface structures and is a highly efficient shallow geophysical exploration technique. It relies on the differences in electrical parameters of underground media, and analyzes and deduces their structural and physical characteristics based on kinematic and kinetic features such as the amplitude, waveform, and frequency of the echo. Compared with other geophysical methods, GPR is fast and convenient, simple to operate, has a high detection resolution, and performs non-destructive detection. It is often used for fine inscriptions on underground structures and the detection and identification of targets, with a wide range of application scenarios, such as geological surveys, planetary exploration, archaeology, civil engineering and architecture, agriculture, environment, and security.

This Special Issue aims to collect and present advanced research to promote GPR technologies and applications in the field of remote sensing.

The topics of this Special Issue include, but are not limited to, the following:

Data processing;
Environment and agriculture;
Modeling and inversion;
Archeology;
Earth and planetary applications;
Civil engineering and geotechnical applications;
City utility and security application.

Prof. Dr. Xuan Feng
Dr. Haoqiu Zhou
Dr. Zejun Dong
Guest Editors

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Keywords

GPR
signal processing
modeling
inversion
application

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Published Papers (9 papers)

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Research

24 pages, 8465 KiB

Open AccessArticle

Harris Hawks Optimization for Soil Water Content Estimation in Ground-Penetrating Radar Waveform Inversion

by Hanqing Qiao, Minghe Zhang and Maksim Bano

Remote Sens. 2025, 17(8), 1436; https://doi.org/10.3390/rs17081436 - 17 Apr 2025

Viewed by 172

Abstract

Ground-penetrating radar (GPR) has emerged as a promising technology for estimating the soil water content (SWC) in the vadose zone. However, most current studies focus on partial GPR data, such as travel-time or amplitude, to achieve SWC estimation. Full waveform inversion (FWI) can produce more accurate results than inversion based solely on travel-time. However, it is subject to local minima when using a local optimization algorithm. In this paper, we propose a novel and powerful GPR waveform inversion scheme based on Harris hawks optimization (HHO) algorithm. The proposed strategy is tested on synthetic data, as well as on field experimental data. To further validate our approach, the results of the HHO algorithm are also compared with those of partial swarm optimization (PSO) and grey wolf optimizer (GWO). The inversion results from both synthetic and real experimental data demonstrate that the proposed inversion scheme can efficiently invert both SWC and layer thicknesses, thus achieving very fast convergence. These findings further confirm that the HHO algorithm can be effectively applied for the quantitative interpretation of GPR data. Full article

(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)

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25 pages, 4306 KiB

Open AccessArticle

Adaptive Variational Mode Decomposition and Principal Component Analysis-Based Denoising Scheme for Borehole Radar Data

by Ding Yang, Cheng Guo, Raffaele Persico, Yajie Liu, Handing Liu, Changjin Bai, Chao Lian and Qing Zhao

Remote Sens. 2025, 17(3), 525; https://doi.org/10.3390/rs17030525 - 3 Feb 2025

Viewed by 828

Abstract

To address the significant impact of noise on the target detection performance of borehole radar (BHR), a key type of ground-penetrating radar (GPR), a denoising scheme based on the whale optimization algorithm (WOA) for adaptive variational mode decomposition (VMD) and multiscale principal component analysis (MSPCA) is proposed. This study initially conducts the modal decomposition of BHR data using an improved adaptive VMD method based on the WOA; it then automatically selects modes meeting specific frequency band standards. The correlation coefficients between these modes and the original signal are computed, discarding weakly correlated modes before signal reconstruction. Finally, MSPCA further suppresses noise, yielding denoised BHR data. Simulations show that the proposed scheme increases the signal-to-noise ratio by 17.964 dB or higher, surpassing the more established denoising techniques of robust principal component analysis (RPCA), MSPCA, and empirical mode decomposition (EMD), and obtains the most favorable results in terms of the RMSE and MSE metrics. The experimental results demonstrate that the proposed scheme more effectively suppresses vertical and random noise signals in BHR data. Both the numerical simulations and experimental results confirm the effectiveness of this scheme in noise reduction for BHR data. Full article

(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)

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18 pages, 6204 KiB

Open AccessArticle

Two-Stage GPR Image Inversion Method Based on Multi-Scale Dilated Convolution and Hybrid Attention Gate

by Mingze Wu, Qinghua Liu and Shan Ouyang

Remote Sens. 2025, 17(2), 322; https://doi.org/10.3390/rs17020322 - 17 Jan 2025

Viewed by 708

Abstract

Ground penetrating radar (GPR) image inversion is of great significance for interpreting GPR data. In practical applications, the complexity and nonuniformity of underground structures bring noise and clutter interference, making GPR inversion problems more challenging. To address these issues, this study proposes a two-stage GPR image inversion network called MHInvNet based on multi-scale dilated convolution (MSDC) and hybrid attention gate (HAG). This method first denoises the B-scan through the first network MHInvNet1, then combines the denoised B-scan from MHInvNet1 with the undenoised B-scan as input to the second network MHInvNet2 for inversion to reconstruct the distribution of the permittivity of underground targets. To further enhance network performance, the MSDC and HAG modules are simultaneously introduced to both networks. Experimental results from simulated and actual measurement data show that MHInvNet can accurately invert the position, shape, size, and permittivity of underground targets. A comparison with existing methods demonstrates the superior inversion performance of MHInvNet. Full article

(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)

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22 pages, 17029 KiB

Open AccessArticle

Cross-Line Fusion of Ground Penetrating Radar for Full-Space Localization of External Defects in Drainage Pipelines

by Yuanjin Fang, Feng Yang, Xu Qiao, Maoxuan Xu, Liang Fang, Jialin Liu and Fanruo Li

Remote Sens. 2025, 17(2), 194; https://doi.org/10.3390/rs17020194 - 8 Jan 2025

Viewed by 723

Abstract

Drainage pipelines face significant threats to underground safety due to external defects. Ground Penetrating Radar (GPR) is a primary tool for detecting such defects from within the pipeline. However, existing methods are limited to single or multiple axial scan lines, which cannot provide the precise spatial coordinates of the defects. To address this limitation, this study introduces a novel GPR-based drainage pipeline inspection robot system integrated with multiple sensors. The system incorporates MEMS-IMU, encoder modules, and ultrasonic ranging modules to control the GPR antenna for axial and circumferential scanning. A novel Cross-Line Fusion of GPR (CLF-GPR) method is introduced to integrate axial and circumferential scan data for the precise localization of external pipeline defects. Laboratory simulations were performed to assess the effectiveness of the proposed technology and method, while its practical applicability was further validated through real-world drainage pipeline inspections. The results demonstrate that the proposed approach achieves axial positioning errors of less than 2.0 cm, spatial angular positioning errors below 2°, and depth coordinate errors within 2.3 cm. These findings indicate that the proposed approach is reliable and has the potential to support the transparency and digitalization of urban underground drainage networks. Full article

(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)

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25 pages, 12592 KiB

Open AccessArticle

Effect of the Outer Pipe on Reducing Direct Coupling of the Thin Borehole Radar Probe in Thick Water-Filled Borehole

by Satoshi Ebihara, Raiki Masui, Koki Koyama, Yuki Tsujikawa and Yuto Nishida

Remote Sens. 2025, 17(1), 100; https://doi.org/10.3390/rs17010100 - 30 Dec 2024

Viewed by 616

Abstract

We propose an outer pipe to reduce a direct wave in a thin single-hole borehole radar probe in a thick water-filled borehole. The outer pipe replaces the medium, such as water inside the borehole, with low-permittivity materials, such as air and plastics. According to numerical calculations, the cylindrical water layer makes the direct wave from the transmitting loop antenna to the receiving one have significant power and narrow frequency bandwidth. This is caused by the low attenuation of the TE₀₁ surface wave when there is a cylindrical water layer. The MoM analysis showed that wearing the outer pipe on the radar probe decreased the direct wave’s power more than the reflected wave from the subsurface objects, improving the detection of that reflected wave. We realized the radar system with the outer pipe by attaching the two acrylic pipes with different diameters. With this outer pie, we conducted field experiments to estimate the position of metal ore near the borehole in skarn with the loop antenna array type borehole radar. The direct wave having oscillation prevented the detection of the reflected wave from the sphalerite vein in the time domain without the outer pipe. However, attaching the outer pipe highlighted that reflected wave. Full article

(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)

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24 pages, 14942 KiB

Open AccessArticle

The Ground-Penetrating Radar Image Matching Method Based on Central Dense Structure Context Features

by Jie Xu, Qifeng Lai, Dongyan Wei, Xinchun Ji, Ge Shen and Hong Yuan

Remote Sens. 2024, 16(22), 4291; https://doi.org/10.3390/rs16224291 - 18 Nov 2024

Cited by 1 | Viewed by 1046

Abstract

Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time GPR images with pre-constructed reference GPR images. However, GPR images, due to their low resolution, cannot extract well-defined geometric features such as corners and lines. Thus, traditional visual image processing algorithms perform inadequately when applied to GPR image matching. To address this issue, this paper innovatively proposes a GPR image matching and localization method based on a novel feature descriptor, termed as central dense structure context (CDSC) features. The algorithm utilizes the strip-like elements in GPR images to improve the accuracy of GPR image matching. First, a CDSC feature descriptor is designed. By applying threshold segmentation and extremum point extraction to the GPR image, stratified strip-like elements and pseudo-corner points are obtained. The pseudo-corner points are treated as the centers, and the surrounding strip-like elements are described in context to form the GPR feature descriptors. Then, based on the feature description method, feature descriptors for both the real-time image and the reference image are calculated separately. By searching for the nearest matching point pairs and removing erroneous pairs, GPR image matching and localization are achieved. The proposed algorithm was evaluated on datasets collected from urban roads and railway tracks, achieving localization errors of 0.06 m (RMSE) and 1.22 m (RMSE), respectively. Compared to the traditional Speeded Up Robust Features (SURF) visual image matching algorithm, localization errors were reduced by 86.6% and 95.7% in urban road and railway track scenarios, respectively. Full article

(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)

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20 pages, 17200 KiB

Open AccessArticle

What Is Beyond Hyperbola Detection and Characterization in Ground-Penetrating Radar Data?—Implications from the Archaeological Site of Goting, Germany

by Tina Wunderlich, Bente S. Majchczack, Dennis Wilken, Martin Segschneider and Wolfgang Rabbel

Remote Sens. 2024, 16(21), 4080; https://doi.org/10.3390/rs16214080 - 31 Oct 2024

Cited by 1 | Viewed by 1104

Abstract

Hyperbolae in radargrams are caused by a variety of small subsurface objects. The analysis of their curvature enables the determination of propagation velocity in the subsurface, which is important for exact time-to-depth conversion and migration and also yields information on the water content of the soil. Using deep learning methods and fitting (DLF) algorithms, it is possible to automatically detect and analyze large numbers of hyperbola in 3D Ground-Penetrating Radar (GPR) datasets. As a result, a 3D velocity model can be established. Combining the hyperbola locations and the 3D velocity model with reflection depth sections and timeslices leads to improved archaeological interpretation due to (1) correct time-to-depth conversion through migration with the 3D velocity model, (2) creation of depthslices following the topography, (3) evaluation of the spatial distribution of hyperbolae, and (4) derivation of a 3D water content model of the site. In an exemplary study, we applied DLF to a 3D GPR dataset from the multi-phased (2nd to 12th century CE) archaeological site of Goting on the island of Föhr, Northern Germany. Using RetinaNet, we detected 38,490 hyperbolae in an area of 1.76 ha and created a 3D velocity model. The velocities ranged from approximately 0.12 m/ns at the surface to 0.07 m/ns at approx. 3 m depth in the vertical direction; in the lateral direction, the maximum velocity variation was ±0.048 m/ns. The 2D-migrated radargrams and subsequently created depthslices revealed the remains of a longhouse, which was not known beforehand and had not been visible in the unmigrated timeslices. We found hyperbola apex points aligned along linear strong reflections. They can be interpreted as stones contained in ditch fills. The hyperbola points help to differentiate between ditches and processing artifacts that have a similar appearance as the ditches in time-/depthslices. From the derived 3D water content model, we could identify the thickness of the archaeologically relevant layer across the whole site. The layer contains a lot of humus and has a high water retention capability, leading to a higher water content compared to the underlying glacial moraine sand, which is well-drained. Full article

(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)

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20 pages, 7804 KiB

Open AccessArticle

Study on the Identification Method of Planar Geological Structures in Coal Mines Using Ground-Penetrating Radar

by Jialin Liu, Xiaosong Tang, Feng Yang, Xu Qiao, Fanruo Li, Suping Peng, Xinxin Huang, Yuanjin Fang and Maoxuan Xu

Remote Sens. 2024, 16(21), 3990; https://doi.org/10.3390/rs16213990 - 27 Oct 2024

Cited by 2 | Viewed by 1323

Abstract

The underground detection environment in coal mines is complex, with numerous interference sources. Traditional ground-penetrating radar (GPR) methods suffer from limited detection range, high noise levels, and weak deep signals, making it extremely difficult to accurately identify geological structures without stable feature feedback. During research, it was found that the detection energy of the same target significantly changes with the antenna direction. Based on this phenomenon, this paper proposes a geological radar advanced detection method using spatial scanning. This method overcomes constraints imposed by the underground coal mine environment on detection equipment, enhancing both detection range and accuracy compared to traditional approaches. Experiments using this method revealed pea-shaped response characteristics of planar geological structures in radar images, and the mechanisms behind their formation were analyzed. Additionally, this paper studied the changes in response characteristics under changes in target inclination, providing a basis for understanding the spatial distribution of geological structures. Finally, application experiments in underground coal mine environments explored the practical potential of this method. Results indicate that, compared to drilling data, this method achieves identification accuracies of 91.88%, 90.42%, and 78.72% for the depth and spatial extent of geological structures, providing effective technical support for coal mining operations. Full article

(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)

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18 pages, 7717 KiB

Open AccessArticle

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

by Dae Wook Park, Han Eung Kim, Kicheol Lee and Jeongjun Park

Remote Sens. 2024, 16(18), 3454; https://doi.org/10.3390/rs16183454 - 18 Sep 2024

Viewed by 1074

Abstract

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. Full article

(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)

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