Ground Penetrating Radar Research Articles

Geophysical Mapping of Cemented Subsoils for Agricultural Development in Southern Peru

Cemented subsoils, commonly referred to as caliche, pose a regular challenge for agricultural development in arid and semi-arid regions like coastal southern Peru. These subsurface features restrict root penetration, limit water infiltration and hinder essential soil processes, ultimately reducing crop yields and agricultural productivity. Accurate and efficient mapping of caliche is important for optimizing land-use planning and implementing sustainable agricultural practices. This study presents the application of near-surface geophysical techniques for mapping caliche deposits in the context of agricultural development at the future Majes II site in the Arequipa region of southern Peru. Specifically, we employed high-frequency ground-penetrating radar (GPR) and frequency-domain electromagnetics (FDEM) at a testbed on the Majes II site to evaluate their ability to delineate the extent, thickness, and depth of caliche within the local geology. GPR offers high-resolution imaging, effectively capturing sharp contrasts between caliche and surrounding materials, providing detailed information on the thickness (approximately 0.4 m) and the depth (up to 1.5 m) of the caliche layers. FDEM provides valuable insights into the presence of caliche at a faster rate of data acquisition and processing, enabling rapid assessment of the extent of caliche deposits, although with the tradeoff of lower resolution and depth information. We demonstrate that these two geophysical methods can be used separately or in an integrated manner for collaborative interpretation at the Majes II site to inform land management decisions, including identifying areas with favorable conditions for crop production and implementing targeted interventions to mitigate the adverse effects of caliche on agricultural productivity.

Atypical holocene ctenomyid rodent burrows related to eolian dune deposits from central Argentina

This contribution aims to interpret the producers and the paleoenvironmental context of Holocene rodent burrows (and associated ichnofossils) from the Late Pleistocene-Holocene Meaucó Formation, central La Pampa Province, Argentina. Fieldwork included a detailed sedimentary logging, a ground penetrating radar survey and documentation of the fossil burrow systems. The Meaucó Formation rest on erosive unconformity over the Upper Miocene-Lower Pliocene Cerro Azul Formation and display a vertical change from eolian dunes that become sparser up section (as imaged by ground penetrating radar) and were capped by a paleosol. The paleosol is covered by recent eolian dune and interdune deposits. The fossil rodent burrows occur up to 1.7 m below the paleosol and share several morphologic features with modern Ctenomyidae burrows, except the high slope of entrance and internal ramps, and the maximum depth of burrows. To explain these differences, it is proposed that the burrows were excavated by ctenomyids in ancient sloping surfaces of eolian dunes, which resulted in increased inclination and apparent maximum depth of the systems. This is an example of influence of environmental conditions on the architecture of rodent burrows, and can be of help when interpreting other similar fossil examples. The paleosol community associated with the fossil vertebrate burrows also included other organisms recorded by their trace fossils: horses (Hippipeda isp.), a scrubby vegetation (rhizoliths), coleoptera or spiders (Skolithos linearis), cicada nymphs (Feoichnus challa) and ants (Daimoniobarax isp.).

Fusing Ground-Penetrating Radar Images for Improving Image Characteristics Fidelity

The analysis of ground-penetrating radar (GPR) data is of vital importance for detecting various subsurface features that might manifest as hyperbolic peaks, which are indicators of a buried object or grayscale variation in the case of contrast in the soil texture. This method focuses on identifying exaggerated patterns through a series of image-processing steps. Two GPR images are initially read and preprocessed by extracting channels, flipping, and resizing. Then, specific regions of interest (ROIs) are cropped, and the Fourier transform is further applied to turn them into the frequency domain. With the help of their frequency signatures, these patterns are extracted from the images, and binary masks are constructed to obtain features of interest. These masked images were reconstructed and merged to make hyperbolic features visible. Finally, Local Binary Pattern (LBP) analysis is used to emphasize these hyperbolic peaks, thereby facilitating their recognition across the whole image. The proposed approach improves the detection of performance subsurface features in GPR data; hence, it is an important tool for geophysical surveys and other related applications. The results prove the high performance of the proposed procedure in improving GPR image characteristics.

Characterization of leakage signatures in buried water pipes by ground penetrating radar(GPR) and instantaneous frequency analysis

The aging and complexity of underground water pipes pose significant challenges to modern society, necessitating long-term monitoring and maintenance to prevent the socioeconomic costs. So, effective nondestructive and geophysical methods are needed to localize the possible leakage areas before any rational opening up is conducted. In this study, we propose a workflow to generate instantaneous frequency slices (IFS) from raw ground penetrating radar (GPR) data. Characterizing the horizontal and vertical patterns of water leakage using IFS revealed two key mechanisms—reflections due to the dry and wet interface and absorption due to water distributed in soil—that significantly influence instantaneous frequency. A well-designed real site (Q-Leak), in collaboration with the Water Supplies Department (WSD) of the government, was conducted to replicate typical leakage scenarios, validating the effectiveness of our IFS-based approach. The results of proposed method demonstrate that IFS is a reliable approach for characterizing leakages in buried water pipes, effectively addressing several challenges associated with amplitude slice analysis. Our proposed methodology enriches the toolkit available for large-scale, future-oriented water leakage detection.

Enhancement of penetration of electromagnetic waves by field focusing applied to GPR detection

Ground penetrating radar (GPR) is limited by challenges such as signal attenuation and inadequate target detection in lossy dielectrics, prompting research on data processing algorithms to enhance detection capability. In this study, we propose a method for enhancing GPR detection capability based on electromagnetic field focusing (FF). The method involves calculating the phase difference attributable to the range difference between each antenna element and the focal point and compensating its feed phase accordingly. This approach effectively concentrates the energy of electromagnetic waves at the target area. Tapered slot antennas operating within the 2.5–8 GHz frequency band were employed for transmitting and receiving, thus facilitating algorithm verification and target detection. The synthetic aperture radar algorithm executed pulse compression and range migration on the received data, thereby intensifying the signal at the target object’s position and achieving double focusing. Notably, this study uniquely employs FF to enhance the penetrability of electromagnetic waves and augment the detection capability of GPR. Both simulation and experimental results demonstrated the effectiveness of the proposed method in improving the GPR imaging quality.

Observations of Igneous Subsurface Stratigraphy during the Jezero Crater Floor Rapid Traverse from the RIMFAX Ground-penetrating Radar

Perseverance traversed the eastern, northern, and western margins of the Séítah formation inlier on the rover’s western fan front approach. Mapping the stratigraphy and extent of the Máaz and Séítah formations is key to understanding the depositional history and timing of crater floor resurfacing events. Perseverance's rapid progress across the Jezero crater floor between the Octavia E. Butler landing site and the western fan front resulted in limited contextual images of the deposits from the Navigation Camera and Mast Camera Zoom. By combining the limited surface images with continuous subsurface sounding by the Radar Imager for Mars’ Subsurface Experiment (RIMFAX) ground-penetrating radar, Jezero crater floor stratigraphy was inferred along this rapid traverse. We produced the first subsurface map of the Máaz formation thickness and elevation of the buried Séítah formation for 2.3 km of the rapid traverse. Three distinct reflector packets were observed in RIMFAX profiles interspersed with regions of low-radar reflectivity. We interpret these reflector packets with increasing depth to be the Roubion member of the Máaz formation (covered in places with regolith), the Rochette member, and the Séítah formation. We found a median permittivity of 9.0 and bulk density of 3.2 g cm−3 from hyperbola fits to RIMFAX profiles, which suggests a mafic composition for Máaz and Séítah. The low-radar reflectivity regions within each reflector packet could indicate potential depositional hiatuses where low-density material like sediment or regolith could have accumulated between successive Máaz formation lava flows and the Séítah formation at depth.

The influence of interlayer bonding conditions on the propagation laws of reflective cracks in semi-rigid base pavement based on the DEM and GPR

Reflection cracks represent the predominant internal distress in semi-rigid base asphalt pavements and often accompanied by additional secondary issues that detrimentally affect the pavement structural bearing capacity. The propagation and deterioration of reflective cracks are affected by many factors, for example the interlayer bonding conditions. It is crucial to identify the propagation and deterioration of reflective cracks to prolong the life of pavement structure. The purpose of this article is to explore the influence of interlayer bonding conditions on the propagation laws of reflective cracks. This work employs the discrete element method(DEM) to explore the propagation patterns of reflection cracks accompanied different bonding conditions between the surface layer and base layer subjected to moving load. The results of discrete element simulation reveal a three-phase progression laws when the reflective cracks expand from the base layer to the pavement surface，involving rapid propagation, stable propagation, and failure process. With the interlayer bond strength decreases, expansion of reflective crack is significantly accelerated. Meanwhile, once the critical bond strength threshold is reached, complete debonding will occur between the surface and base layers. The results are ultimately verified through the core sampling from practical engineering using three-dimensional (3D) ground penetrating radar(GPR) detection, along with the radar image recognition of reflective cracks and poor interlayer bonding. The results will serve as a foundation for researching the mechanism and treatment measures for two internal pavement distress, namely reflection cracks and poor interlayer bonding.

Application of Georadar Method to Investigate shallow Artifacts for Al-Maabid Area in Babylon Archaeological City

Babylon, one of the most influential civilizations in Mesopotamia, significantly contributed to human history with its rich culture, advanced legal systems, and architectural marvels, serving as the capital of the Neo-Babylonian empire. This study aims to discover ancient Babylonian civilizational remains and artifacts, including artifacts and walls from an ancient Babylonian house near the Ishtar temple. A geophysical survey was carried out using ground penetrating radar (GPR), yielding promising preliminary results. It contributed to the Department of Antiquities' excavation campaign for potential antiquities in Babylon Governorate, which was regarded as a preliminary step before beginning extensive excavations. Accurate GPR results were displayed as radargrams, maps, and 3D VI visualization diagrams, providing a precise subsurface view of buried archaeological remains.

Morphology and internal structure of small-scale washovers formed in the coastal zone of the semi-enclosed tideless basin, Gulf of Gdańsk, Baltic Sea

This study explores the morphological features and internal structure of small-scale washovers along the south-eastern Baltic Sea coast, providing insights into these most widespread yet often neglected deposits in the recent research of geomorphological and sedimentary record of storm surges. A 15-year-long record of morphological changes of the coast was acquired from regional orthophotos to analyse their geometry and spatial characteristics. Sedimentological analyses comprising a description of deposits, grain size and shape analyses, and Ground Penetrating Radar profiling were undertaken to investigate the internal structure of washovers. The formation of washovers appeared to be correlated with the average winter NAO (North Atlantic Oscillation) index. The study revealed different scaling relationships of selected spatial parameters in two coastal settings and extended previously hypothesized relationships of length-area, area-volume, and length-volume. The internal structure of washovers is defined by low-angle planar cross-stratification and horizontal stratification, both disrupted by small troughs. Grain-size data indicated extremely short transport of dune and beach sediments, lately deposited in the form of washovers. The study proves that in all domains, the geomorphological characteristics of washovers are scalable, despite the different coastal settings, and even small-scale washovers fit the existing development models.

Accurate gain method for ground-penetrating radar signals based on stationary wavelet packet transform

In this study, we propose an accurate gain method for ground-penetrating radar (GPR) signals based on the characteristics of refined time-frequency analysis and translation invariance offered by the Stationary Wavelet Packet Transform (SWPT), combined with the conventional signal gain approach. This method aims to address the issue of low signal resolution resulting from the direct gain processing of GPR signals with a low signal-to-noise ratio (SNR). Specifically, the GPR signals are initially decomposed into appropriate wavelet packet coefficients using SWPT, wherein only those coefficients with high SNR undergo gain processing, followed by reconstruction of the signals through SWPT. By employing accurate gain processing on low SNR GPR signals acquired during concrete crack detection tests, we have confirmed that the proposed method effectively distinguishes the target reflected signals from most noise, thereby achieving accurate amplification of the desired reflected signals and significantly enhancing the GPR signals resolution under low SNR conditions.

Large-Depth Ground-Penetrating Radar for Investigating Active Faults: The Case of the 2017 Casamicciola Fault System, Ischia Island (Italy)

We conducted large-depth Ground-Penetrating Radar investigations of the seismogenic Casamicciola fault system at the volcanic island of Ischia, with the aim of constraining the source characteristics of this active and capable fault system. On 21 August 2017, a shallow (hypocentral depth of 1.2 km), moderate (Md = 4.0) earthquake hit the island, causing severe damage and two fatalities. This was the first damaging earthquake recorded on the volcanic island of Ischia from the beginning of the instrumental era. Our survey was performed using the Loza low-frequency (15–25 MHz) GPR system calibrated by TDEM results. The data highlighted variations in the electromagnetic signal due to the presence of contacts, i.e., faults down to a depth larger than 100 m below the surface. These signal variations match with the position of the synthetic and antithetic active fault system bordering the Casamicciola Holocene graben. Our study highlights the importance of employing large-depth Ground-Penetrating Radar geophysical techniques for investigating active fault systems not only in their shallower parts, but also down to a few hundred meters’ depth, providing a contribution to the knowledge of seismic hazard studies on the island of Ischia and elsewhere.

A DL method to detect multi-type hidden objects in tunnel linings using a comprehensive GPR dataset

This study proposes an enhanced detection method for tunnel lining hidden objects based on the comprehensive Ground Penetrating Radar (GPR) dataset and an improved deep learning (DL) algorithm. To mitigate the issue of data scarcity, a tailored dataset consisting of six types of hidden objects was compiled, which contained 7426 on-site collected GPR images and 71,014 well-labeled objects. This bespoke dataset offered substantial support for algorithm training and validation, making it one of the most extensive datasets in the domain for tunnel lining GPR detection. An enhanced Faster R-CNN algorithm was then developed to effectively detect different types of hidden objects. Modifications were made in four key areas: Feature extraction module, Feature Pyramid Network (FPN), customized anchor scheme and modified loss function. This algorithm was subsequently trained and validated using the comprehensive dataset. Evaluation metrics demonstrated the performance of this approach, with a mean average Precision (mAP) of 81.2%. Results also proved particularly effective in detecting rebars, pipelines, and steel arches while demonstrating a shortfall in detecting voids and cavities due to the data limitation. The results underscore the potential of this enhanced algorithm for the automatic detection of diverse hidden objects in tunnel linings, thus stimulating further research in this vital field.

Identification of internal voids in pavement based on improved knowledge distillation technology

Investigating methods for the detection of internal voids within road structures is a critical measure to ensure the safety and integrity of roadway operations. The purpose of this research is to investigate on the identification method of internal voids in pavement based on improved knowledge distillation technology. Ground penetrating radar data in three dimensions were extensively collected to capture the internal voids present within roadways, and this data was subsequently validated through in-situ verification. The echo characteristics of ground penetrating radar for areas with road voids were analyzed, and a dataset containing 1700 images of these internal voids was established. A YOLOv8 model improvement method was proposed, and a model for the detection of internal road voids was constructed based on the improved YOLO v8 framework. To further refine the model's performance, a knowledge distillation method based on multiple guidance from teacher assistants was developed. A stochastic learning approach was integrated, resulting in the establishment of a model optimized by this stochastic learning scheme for the identification of internal road voids. The results demonstrate that the presence of overfitting during the training phase of the void identification model can restrict its performance within a certain domain. The proposed stochastic learning-optimized, multi-teacher assistant guided knowledge distillation model, adeptly harnesses the performance benefits of both the teacher and assistant models by means of knowledge transfer, consequently achieving a significant improvement in the detection of internal road voids.

Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

Abstract. Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data are available to evaluate model estimates of density in mountainous terrain. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne-lidar snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer-picking method that leverages the GPR reflection coherence and the distributed lidar–GPR-retrieved bulk density with machine learning. The root-mean-square error between the distributed estimates and in situ observations is 11 cm for depth, 27 kg m−3 for density, and 46 mm for SWE. The median relative uncertainty in distributed SWE is 13 %. Interactions between wind, terrain, and vegetation display corroborated controls on bulk density that show model and observation agreement. Knowledge of the spatial patterns and predictors of density is critical for the accurate assessment of SWE and essential snow research applications. The spatially continuous snow density and SWE estimated over approximately 16 km2 may serve as necessary calibration and validation for stepping prospective remote-sensing techniques toward broad-scale SWE retrieval.

The Principle and Application of Ground Penetrating Radar in Highway Engineering

The Principle and Application of Ground Penetrating Radar in Highway Engineering

Recognition of cross-shore dynamics of longshore bars in upper-shoreface deposits of prograding sandy coastal barriers

ABSTRACT In the past decades it has become evident that the often cyclic cross-shore migration of longshore bars is significantly influenced by wave climate. This study demonstrates that this cyclic migration, whether landward or seaward, leads to the formation of low-angle seaward-inclined stratification (SIS) spanning the beach and upper shoreface in ground-penetrating radar (GPR) data from prograding sandy coastal barriers including strandplains. Previously documented radargrams of these systems are reinterpreted using this knowledge of cross-shore dynamics of longshore bars. Five distinct wave-climate-related radar architectures are identified. A notable observation is the prevalence of SIS as the dominant upper-shoreface to beach structure in most radargrams, despite its infrequently described occurrence from outcrop observations. To address this disparity, this paper also focuses on recognizing SIS in outcrop and core data. Compelling evidence of SIS was discovered in a late Messinian coastal barrier parasequence in SE Spain, and the associated sedimentary architecture is described in detail. It is hypothesized that this example holds generic significance, illustrated using core data from Middle Jurassic strata of the Norwegian continental shelf, and suggesting that the sedimentary architectures of numerous other ancient prograding barrier deposits may exhibit variations within a similar SIS framework.

Construction Environment Noise Suppression of Ground-Penetrating Radar Signals Based on an RG-DMSA Neural Network

Construction Environment Noise Suppression of Ground-Penetrating Radar Signals Based on an RG-DMSA Neural Network

Data-driven evaluation of building materials using Ground Penetrating Radar

Ground Penetrating Radar is a widely used technology in the nondestructive evaluation and monitoring of structures during repair and maintenance phases. Within this domain, a critical focus lies in assessing layered structures such as building envelopes. Evaluating attributes like thickness and material type in layered structures traditionally demands labor-intensive manual efforts involving calculations of dielectric properties and extensive signal processing. This study addresses these challenges by leveraging machine learning data-driven models that harness the entire recorded waveform, presenting a potential breakthrough for expediting diagnostic monitoring within the construction industry. We evaluate both supervised and unsupervised machine learning models for classification and regression tasks predicting the properties of common building materials. Experimental GPR A-scans serve as input for these models, with a thorough evaluation of both instance-based and parametric modeling approaches. A detection accuracy of 100% is achieved for identifying outlier scans, while material classification accuracy is 85%. Layer thickness predictions also had a high accuracy, with a typical error of 5%. Additionally, we explore the impact of feature learning and other preprocessing strategies on model performance. Our findings demonstrate the suitability of standard data-driven models for a spectrum of supervised learning tasks for layered structure diagnostics. However, we emphasize the importance of careful attention to the distribution of training data and its relevance to intended use in the field.

Novel application of ground penetrating radar for damage detection in thick FRP composites

This paper presents the first successful application of ground penetrating radar (GPR) to the inspection of thick (≥100 mm) fiber-reinforced composites. These thick composites are found in wind/tidal turbine blades and composite-hulled ships, where sufficient non-destructive testing (NDT) remains challenging. Polyester-glass specimens, ranging in thickness from 100 to 120 mm, were created with delamination-mimicking damage. Specimen thickness, damage depth location, antenna orientation and damage dryness were the test variables. Finite-difference time-domain simulations indicated the method’s feasibility, and experimental results confirmed these findings. GPR effectively detected and precisely located dry, in-plane damage, with increased detectability for water-filled damage due to the enhanced contrast of electrical properties that creates the damage response in the signal. This capability is particularly advantageous for marine composites, where extensive damage may lead to water ingress. In a comparison with an ultrasonic inspection, GPR proved superior for the thicker composites (≥100 mm). As the first successful application of GPR to composite structures, these findings significantly advance the field of NDT of these materials.

Detecting sources of water leakage in fractured limestone: Case study in Mokattam District, Egypt

ABSTRACT The paper investigates the sources of water leakage in Mokattam, Cairo, Egypt. It is considered a high-risk zone for landslides due to the presence of problematic soil, topography, and water conditions. The objective is to mitigate the negative impact of underground water on landslide disasters. The proposed methodology involves field visits, satellite image processing, Ground Penetration Radar scanning, chemical and isotope analysis of water, and evaluating the water and sanitary networks within the study area. The findings revealed that the underground water is formed because of the continuous leakage from the existing networks. Moreover, there was no evidence of any aquifers, particularly at Ain Mosa floating point. Thus, the accumulated water in the unsaturated zone is not sustainable for long-term development for its limited potential and poor quality. Consequently, immediate action plan is required to assess and upgrade the water and sanitary networks through the implementation of innovative technologies for such hydrogeologic conditions