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Applied Sciences
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Ground Penetrating Radar (GPR): Theory, Methods and Applications
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Ground Penetrating Radar (GPR): Theory, Methods and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 10 October 2024 | Viewed by 5807

Special Issue Editors

Prof. Dr. Deshan Feng

E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: GPR theory and method; geophysical data processing; forward simulation and inversion imaging
Dr. Xun Wang

E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: electromagnetic waves simulation; full waveform inversion; GPR data processing
Dr. Bin Zhang

E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: inverse problems; optimization methods in computational electromagnetic; non-destructive evaluation

Special Issue Information

Dear Colleagues,

As a high-precision detection technology, ground penetrating radar (GPR) can provide non-destructive, high-resolution information in near-surface geophysics. From traditional civil engineering to glaciology, planetary exploration, and so on, GPR has been used in many applications. The diversity of sensing objects necessitates increased precision and efficiency in GPR data processing methods.

This Special Issue aims to report the latest methods of simulation, inversion, and data processing or the most recent applications of GPR in various fields. This provides a way to improve the precision and efficiency of traditional GPR methods and promotes the development of GPR applications.

In particular, we invite researchers and practitioners from academia and industry to publish papers on novel or innovative aspects of advanced algorithms and GPR application. Some examples are listed below, but the topics are not restricted to these:

  • New techniques for numerical simulation of GPR with different physical quantities, scale domains, and resolutions.
  • Advances in numerical development, data processing methodologies, and applications of artificial intelligence in GPR.
  • Innovative cases of GPR in complex scenarios, including civil engineering, geotechnical engineering, glaciology, planetary exploration, etc.

Prof. Dr. Deshan Feng
Dr. Xun Wang
Dr. Bin Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ground penetrating radar (GPR)
  • GPR theory
  • numerical simulation
  • data processing and interpretation
  • inversion and imaging
  • GPR application

Published Papers (5 papers)

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Editorial

Jump to: Research

3 pages, 193 KiB  
Editorial
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://0-doi-org.brum.beds.ac.uk/10.3390/app13179847 - 31 Aug 2023
Cited by 2 | Viewed by 1296
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

Jump to: Editorial

19 pages, 6065 KiB  
Article
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://0-doi-org.brum.beds.ac.uk/10.3390/app14083521 - 22 Apr 2024
Viewed by 326
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  
Article
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://0-doi-org.brum.beds.ac.uk/10.3390/app14072721 - 24 Mar 2024
Viewed by 495
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  
Article
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://0-doi-org.brum.beds.ac.uk/10.3390/app131911107 - 09 Oct 2023
Viewed by 1785
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  
Article
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://0-doi-org.brum.beds.ac.uk/10.3390/app131810028 - 05 Sep 2023
Cited by 1 | Viewed by 795
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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