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3 pages, 193 KiB

Open AccessEditorial

Special Issue on Ground Penetrating Radar: Theory, Methods, and Applications

by Deshan Feng, Yuxin Liu, Bin Zhang and Xun Wang

Appl. Sci. 2023, 13(17), 9847; https://doi.org/10.3390/app13179847 - 31 Aug 2023

Cited by 2 | Viewed by 2546

Abstract

Ground penetrating radar (GPR), geophysics exploring technology, could non-destructively acquire high-precision information about the shallow subsurface [...] Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR): Theory, Methods and Applications)

Research

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15 pages, 9146 KiB

Open AccessArticle

Research on Intelligent Recognition Method of Ground Penetrating Radar Images Based on SAHI

by Ruimin Chen, Ligang Cao, Congde Lu and Lei Liu

Appl. Sci. 2024, 14(18), 8470; https://doi.org/10.3390/app14188470 - 20 Sep 2024

Viewed by 453

Abstract

Deep learning techniques have flourished in recent years and have shown great potential in ground-penetrating radar (GPR) data interpretation. However, obtaining sufficient training data is a great challenge. This paper proposes an intelligent recognition method based on slicing-aided hyper inference (SAHI) for GPR [...] Read more.

Deep learning techniques have flourished in recent years and have shown great potential in ground-penetrating radar (GPR) data interpretation. However, obtaining sufficient training data is a great challenge. This paper proposes an intelligent recognition method based on slicing-aided hyper inference (SAHI) for GPR images. Firstly, for the problem of insufficient samples of GPR images with structural loose distresses, data augmentation is carried out based on deep convolutional generative adversarial networks (DCGAN). Since distress features occupy fewer pixels on the original image, to allow the model to pay greater attention to the distress features, it is necessary to crop the original images centered on the distress labeling boxes first, and then input the cropped images into the model for training. Then, the YOLOv5 model is used for distress detection and the SAHI framework is used in the training and inference stages. The experimental results show that the detection accuracy is improved by 5.3% after adding the DCGAN-generated images, which verifies the effectiveness of the DCGAN-generated images. The detection accuracy is improved by 10.8% after using the SAHI framework in the training and inference stages, which indicates that SAHI is a key part of improving detection performance, as it significantly improves the ability to recognize distress. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR): Theory, Methods and Applications)

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16 pages, 8512 KiB

Open AccessArticle

Vital Views into Drone-Based GPR Application: Precise Mapping of Soil-to-Rock Boundaries and Ground Water Level for Foundation Engineering and Site-Specific Response

by Michael Frid and Vladimir Frid

Appl. Sci. 2024, 14(17), 7889; https://doi.org/10.3390/app14177889 - 5 Sep 2024

Viewed by 675

Abstract

The primary objective of this case study is to evaluate the effectiveness of drone-based ground penetrating radar (GPR) in detecting and mapping underground water levels and soil-to-rock boundaries. This knowledge is crucial for accurate structural engineering analyses, including foundation engineering and site-specific response [...] Read more.

The primary objective of this case study is to evaluate the effectiveness of drone-based ground penetrating radar (GPR) in detecting and mapping underground water levels and soil-to-rock boundaries. This knowledge is crucial for accurate structural engineering analyses, including foundation engineering and site-specific response evaluations. The paper also considers drone-based GPR to overcome common urban obstacles, topographic variations, and environmental factors by simply flying over them, offering a promising solution to these challenges. The research utilized drone-based GPR equipped with an unshielded 150 MHz dipole antenna and employed filtering procedures to diminish the effect of above-ground obstacles on the interpretation of our results. The study unequivocally demonstrated the feasibility and effectiveness of drone-based GPR in these applications, reassuring the civil engineering community. The findings of this study significantly advance our understanding of drone-based GPR technology for mapping disturbed soil boundaries and water table levels in foundation engineering and site response applications and provide valuable recommendations for optimizing its performance in complicated terrains, thereby inspiring and guiding future research and practice in this field. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR): Theory, Methods and Applications)

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22 pages, 15259 KiB

Open AccessArticle

ROI-Binarized Hyperbolic Region Segmentation and Characterization by Using Deep Residual Convolutional Neural Network with Skip Connection for GPR Imaging

by Hua Zhang, Qianwei Dai, Deshan Feng, Xun Wang and Bin Zhang

Appl. Sci. 2024, 14(11), 4689; https://doi.org/10.3390/app14114689 - 29 May 2024

Viewed by 523

Abstract

Ground Penetrating Radar (GPR) is a non-destructive geophysical technique utilizing electromagnetic pulses to detect subsurface material properties. The analysis of regions of interest (ROIs) in GPR images often entails the identification of hyperbolic reflection regions of underground targets through accurate segmentation, a crucial [...] Read more.

Ground Penetrating Radar (GPR) is a non-destructive geophysical technique utilizing electromagnetic pulses to detect subsurface material properties. The analysis of regions of interest (ROIs) in GPR images often entails the identification of hyperbolic reflection regions of underground targets through accurate segmentation, a crucial preprocessing step. Currently, this represents a research gap. In the hyperbolic reflection region, manual segmentation not only demands professional expertise but is also time-consuming and error-prone. Automatic segmentation can aid in accurately determining the location and depth of the reflection region, thereby enhancing data interpretation and analysis. This study presents a deep residual Convolutional Neural Network (Res-CNN) that integrates skip connections within an encoder-decoder framework for ROI-binarized hyperbolic segmentation. The proposed framework includes designed downsampling and upsampling modules that facilitate feature computation sharing between these two modules through skip connections within network blocks. In the evaluation of both simple and complex models, our method attained PSNR, SSIM, and FSIM values of 57.1894, 0.9933, and 0.9336, and 58.4759, 0.9958, and 0.9677, respectively. Compared to traditional segmentation methods, the proposed approach demonstrated clearer segmentation results, enabling intelligent and effective identification of the ROI region containing abnormal hyperbolic reflection waves in GPR images. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR): Theory, Methods and Applications)

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19 pages, 6065 KiB

Open AccessArticle

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

by Lukas Lahnsteiner, David Größbacher, Martin Bürger and Gerald Zauner

Appl. Sci. 2024, 14(8), 3521; https://doi.org/10.3390/app14083521 - 22 Apr 2024

Viewed by 967

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR): Theory, Methods and Applications)

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16 pages, 7114 KiB

Open AccessArticle

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

by Zenglun Guan and Wanli Liu

Appl. Sci. 2024, 14(7), 2721; https://doi.org/10.3390/app14072721 - 24 Mar 2024

Viewed by 908

Abstract

Top-coal structure detection is an important basis for realizing effective mining in fully mechanized cave faces. However, the top-coal structure is very complex and often contains multi-layer gangues, which seriously influence the level of effective mining. For these reasons, this paper proposes a [...] Read more.

Top-coal structure detection is an important basis for realizing effective mining in fully mechanized cave faces. However, the top-coal structure is very complex and often contains multi-layer gangues, which seriously influence the level of effective mining. For these reasons, this paper proposes a novel multi-frequency ground-penetrating radar (GPR) data-fusing method through a joint sliding window and wavelet transform weighting method to accurately detect the top-coal structure. It possesses the advantages of both high resolution and great detection depth, and it can also integrate multi-frequency GPR data into one composite profile to interpret the internal structure information of top coal in detail. The detection procedure is implemented following several steps: First of all, the multi-frequency GPR data are preprocessed and aligned through a band-pass filter and a zero offset elimination method to establish their spatial correspondences. Secondly, the proposed method is used to determine the time-varying weight values of each frequency GPR signal according to the wavelet energy proportion within the sliding window; also, the edge detection algorithm is introduced to improve the fusion efficiency of the wavelet transform so as to realize the effective fusion of the multi-frequency GPR data. Thirdly, a reflection intensity model of multi-frequency GPR signals traveling in the top-coal is established by using the stratified identification method, and then, the detailed top-coal structure can be inversely interpreted. Finally, the quantitative evaluation criteria, information entropy (IE), space–frequency (SF) and Laplacian gradient (LG), are used to evaluate the multi-frequency GPR data fusion’s effectiveness in laboratory and field environments. The experimental results show that, compared with the genetic, time-varying and wavelet transform fusion method, the fusion performance of the presented method possesses higher values in the IE, SF and LG evaluation criteria, and it also has both the merits of high resolution and great detection depth. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR): Theory, Methods and Applications)

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21 pages, 15663 KiB

Open AccessArticle

Application of the Ground Penetrating Radar (GPR) and Electromagnetic (EM34-3) Geophysical Tools and Sedimentology for the Evaluation of the Subsurface of Sites Earmarked for Aquaculture Ponds in the Amazon Region of Northern Brazil

by Ramon Wagner Torres Pena, Pedro Andrés Chira Oliva and Fernando Araújo Abrunhosa

Appl. Sci. 2023, 13(19), 11107; https://doi.org/10.3390/app131911107 - 9 Oct 2023

Cited by 3 | Viewed by 2482

Abstract

The present study evaluated the application of Ground Penetrating Radar and Electromagnetic Induction geophysical tools combined with sedimentology for the description of the subsurface of sites destined for the installation of ponds for an extensive freshwater fish farming system. Two areas with similar [...] Read more.

The present study evaluated the application of Ground Penetrating Radar and Electromagnetic Induction geophysical tools combined with sedimentology for the description of the subsurface of sites destined for the installation of ponds for an extensive freshwater fish farming system. Two areas with similar topographic characteristics (flat land near bodies of water) were investigated in the Amazon region of northern Brazil: Area 1—the future site of an aquaculture research center, and Area 2—an established fish farming operation. These tools performed well in the evaluation of the suitability of the terrain for the installation of aquaculture ponds. The application of these tools can, thus, be recommended for aquaculture projects, given that it provides advanced knowledge on the characteristics of the local soils, which is extremely important to guarantee the sustainability of any aquaculture operation. These data can help minimize the environmental impacts of the process, while maximizing the economic returns to the installation of an aquaculture operation. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR): Theory, Methods and Applications)

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16 pages, 6639 KiB

Open AccessArticle

Least Squares Reverse Time Migration of Ground Penetrating Radar Data Based on Modified Total Variation

by Qianwei Dai, Shaoqing Wang and Yi Lei

Appl. Sci. 2023, 13(18), 10028; https://doi.org/10.3390/app131810028 - 5 Sep 2023

Cited by 1 | Viewed by 1201

Abstract

As a fundamental part of ground penetrating radar (GPR) data processing, reverse time migration (RTM) can correctly position reflection waves and focusing diffraction waves on the proper spatial position. Least-squares reverse-time migration (LSRTM) is widely used in the seismic field for its ability [...] Read more.

As a fundamental part of ground penetrating radar (GPR) data processing, reverse time migration (RTM) can correctly position reflection waves and focusing diffraction waves on the proper spatial position. Least-squares reverse-time migration (LSRTM) is widely used in the seismic field for its ability to suppress artifacts and generate high-resolution images in comparison to conventional RTM. However, in the particular case of GPR detection methods, LSRTM is extremely susceptible to aliasing artifacts caused by under-sampling. In pursuit of enhanced precision in underground structure characterization, this paper presents the development of a new LSRTM based on modified total variation (MTV) regularization to improve imaging resolution. Initially, the objective function of LSRTM is derived by combining the Born approximation in 2-D transversal magnetic mode. Next, the adjoint equations and their gradients are solved using the Lagrange multiplier method. The objective function is then constrained by MTV regularization to ensure the precision and convergence of the LSRTM, which delivers a refined edge with reconstruction details. In the numerical experiments, in comparison to the conventional LSRTM method, the LSRTM-MTV algorithm demonstrated a 30.4% increase in computational speed and a 21.1% reduction in mean squared error (MSE). The outperformance of the proposed method is verified in detail through the image resolution and amplitude preservation in the test of synthetic data and laboratory data. Future research efforts will center on applying the proposed method to models featuring dispersive or anisotropic media that closely mimic real-world conditions and extending the application to various imaging techniques involving objective function minimization. Full article

(This article belongs to the Special Issue Ground Penetrating Radar (GPR): Theory, Methods and Applications)

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