Ground Penetrating Radar Data Research Articles

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task

The S-transform is a fundamental time–frequency (T-F) domain analysis method in ground penetrating radar (GPR) data processing and can be used for identifying targets, denoising, extracting thin layers, and high-resolution imaging. However, the S-transform spectrum experiences energy leakage near the instantaneous frequency. This phenomenon causes frequency components to erroneously spread over a wider range, impacting the accuracy and precision of GPR data processing. Synchrosqueezing is an effective method to prevent spectrum leakage. In this work, we introduce the synchrosqueezing generalized phase-shifting S-transform (SS-GPST). Initially, it resolves the compatibility issue between the S-transform and the synchrosqueezing strategy through phase-shifting. Subsequently, the SS-GPST accomplishes spectral energy focusing and resolution enhancement via a generalized parameter and synchrosqueezing. A synthetic signal test shows that the SS-GPST excels over other methods at focusing degree, spectral resolution, and signal reconstruction accuracy and speed. In actual GPR tunnel detection data processing, we assess the adaptability of the SS-GPST from three aspects: spectral energy distribution, thin layer identification, and data denoising. The results indicate: (1) compared to other methods, the SS-GPST accurately expresses spectral components with a strong focusing degree and fewer interference components; (2) high-frequency slices of the SS-GPST accurately detect the top and bottom interfaces of a 3.0–3.5 cm reinforcement protection layer; and (3) due to fewer interference components in the SS-GPST spectrum, reconstructing GPR profiles through the SS-GPST inverse transform is an efficient denoising technique. The SS-GPST demonstrates adaptability to different data processing purposes, offers high-resolution T-F spectra, and shows potential to supersede the S-transform.

Automatic segmentation of radar data from the Chang’E-4 mission using unsupervised machine learning: A data-driven interpretation approach

Chang’E-3, Chang’E-4 and Chang’E-5 were the first planetary missions with in-situ ground-penetrating radar (GPR) in their scientific payloads. Apart from the Chang’E missions, in-situ GPR has also been used in the Martian missions Tianwen-1 and Perseverance, establishing GPR as a mainstream and pivotal tool in the new era of planetary exploration. Despite its widespread use in planetary science, interpreting GPR data from the Chang’E-4 mission is still challenging, with varying and ambiguous interpretations offered by different researchers. This is primarily due to the complexity and heterogeneity of the Lunar subsurface, which results to a difficult to interpret radagram with low signal-to-clutter ratio. Clutter masks potential targets and makes interpretation a laborious and highly subjective process. To tackle this, we interpret the GPR data from Chang’E-4 mission using a novel processing paradigm based on un-supervised machine learning. A set of statistical attributes is initially calculated locally for different sliding windows in the investigated radagram. Subsequently, these statistical features are clustered into groups using un-supervised machine learning. The segmented radagram highlights boundaries between different formations, and reveals hidden structures previously unseen using typical processing approaches. The proposed framework is fully automatic and objective, facilitating processing, and increasing the overall reliability of the final interpretation.

The ground penetrating radar response recognition of hidden dam defects using multi-output convolutional neural network

The Ground Penetrating Radar (GPR) data interpretation of dam defects detection by manual process is a heavy task. Although the convolutional neural network (CNN) is applicable to detect road defects with the help of other auxiliary instruments and techniques, it is still a challenge for the dam defect detection by deep learning. To overcome this problem, a multi-output CNN model for GPR response recognition of dam defects is proposed. The training dataset and test dataset are produced from a large number of GPR responses with different types of dam defects forward modeling by GPRMax3.0 software. The results show that the highest recognition accuracy of the training dataset and test dataset is about 98% and 97%, respectively. To verify the effectiveness, the new proposed method is used to recognize the hidden GPR responses of dam defects in the real GPR data. The results show that the types and locations of dam defects in the real GPR data can be recognized and classified automatically, efficiently and accurately.

Underground Diagnosis Based on GPR and Learning in the Model Space.

Ground Penetrating Radar (GPR) has been widely used in pipeline detection and underground diagnosis. In practical applications, the characteristics of the GPR data of the detected area and the likely underground anomalous structures could be rarely acknowledged before fully analyzing the obtained GPR data, causing challenges to identify the underground structures or anomalies automatically. In this article, a GPR B-scan image diagnosis method based on learning in the model space is proposed. The idea of learning in the model space is to use models fitted on parts of data as more stable and parsimonious representations of the data. For the GPR image, 2-Direction Echo State Network (2D-ESN) is proposed to fit the image segments through the next item prediction. By building the connections between the points on the image in both the horizontal and vertical directions, the 2D-ESN regards the GPR image segment as a whole and could effectively capture the dynamic characteristics of the GPR image. And then, semi-supervised and supervised learning methods could be further implemented on the 2D-ESN models for underground diagnosis. Experiments on real-world datasets are conducted, and the results demonstrate the effectiveness of the proposed model.

Identifying Ice Wedges in Ground Penetrating Radar data from the CRREL Permafrost Tunnel, Fox Alaska

The warming of Alaska’s permafrost has been leading to thawing within its discontinuous permafrost. This can cause ice wedges to melt resulting in thermokarst formations such as sink holes and landslides which are causing damage to Alaskan infrastructure. To avoid developing further infrastructure in areas containing ice wedges, it is necessary to improve methods of detecting subsurface ground ice. Ground Penetrating Radar (GPR) is a nonintrusive remote sensing method of locating and characterizing permafrost and other subsurface features that are not evident on a cut face or surface. When applied to the detection of ice wedges at the CRREL (Cold Regions Research and Engineering Laboratory) permafrost tunnel in Fox, Alaska, a few identifying features were observed which corresponded with prior research. We first used GPR reflections of areas along the tunnel walls holding known ice wedges to verify the identifying features of an ice wedge within GPR data. We utilized these reflections as test data to identify possible ice wedges in locations where they were not observed on the tunnel walls.

Multi-scale ground penetrating radar full waveform inversion with hybrid Tikhonov and total-variation regularization for different geometric structure

Abstract Full waveform inversion (FWI) is a high-resolution technique to estimate the parameters of dielectric permittivity (ϵ) and electrical conductivity (σ) and identify the structure of the subsurface for Ground Penetrating Radar (GPR) application. However, permittivity and conductivity parameters can be coupled in bi-parameter GPR inversion. This coupling effect leads to the crosstalk in the FWI result. To solve this problem, we propose a novel approach to use the multi-scale FWI with the hybrid regularization method, which combines Tikhonov and total-variation (TV) regularizers that simultaneously invert the ϵ and the σ parameters, which improve the inversion accuracy and reduce the crosstalk effect. The multi-scale strategy uses the Wiener filtering to process the GPR data in different frequency ranges. Then, the low frequencies signal updates the bottom part and subsequently increases the frequencies to invert for the shallow areas. The Tikhonov regularization stabilizes the reconstruction of the smoothly varying background part. In contrast, Total Variation (TV) regularization can recover the large contrasts associated with the LNAPL model. The new Tikhonov-TV (TT) regularization can mitigate the crosstalk caused by the parameter coupling effect. Numerical tests with typical GPR models demonstrate that the proposed multi-scale TT-FWI strategy can effectively eliminate the crosstalk and improve the reconstruction accuracy when the model parameters have a different structure.

3D ground-penetrating radar data analysis and interpretation using attributes based on the gradient structure tensor

In near-surface geophysics, ground-penetrating radar (GPR) surveys are routinely employed in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. Thanks to recent technical developments in GPR instrumentation and antenna design, 3D surveys comprising several 100.000 traces can be performed daily. Especially in complex environments such as sedimentary systems, analyzing and interpreting the resulting GPR volumes is a time-consuming and laborious task that is still largely performed manually. In the last decades, several data attributes have been proposed to guide and improve such tasks and assure a higher degree of reproducibility in the resulting interpretations. Many of these attributes have been developed in image processing or computer vision and are routinely used, for example, in reflection seismic data interpretation. Especially in sedimentary systems, variations in the subsurface are accompanied by variations of GPR reflections in terms of amplitudes, continuity, and geometry in view of dip angle and direction. A promising tool to analyze such structural features is known as the gradient structure tensor (GST). Up to today, the application of the GST approach is limited to a few 2D GPR examples. Thus, we take up the basic idea of GST analysis and introduce and evaluate the corresponding attributes to analyze 3D GPR data. We apply the proposed GST approach to one synthetic and two field data sets imaging diverse sedimentary structures. Our results demonstrate that the proposed set of GST-based attributes can be efficiently computed in 3D and that these attributes represent versatile measures to address different typical interpretation tasks and, thus, help for an efficient, reproducible, and more objective interpretation of 3D GPR data.

The 1980 Aparejo Glacier catastrophic detachment: new insights and current status

The catastrophic detachment of Aparejo Glacier (one of the three known cases in the Andes) took place on 1 March 1980 and resulted in the removal of an ice volume initially estimated to be 7.2 Mm3, which originally was 1.0 km long and covered an area of 0.2 km2. The event caused the sudden mobilization of the sliding mass 3.7 km down valley at an estimated speed of 110 km/h, causing remarkable geomorphological changes, including the obliteration of most of the glacier. 40 years after the event, we analyze new evidence: 3 ground surveys carried out in 2015 and 2016; DEMs and glacier outlines compiled from orthorectified aerial imagery pre-and post-event; GNSS data; Ground Penetrating Radar (GPR) data; a terrestrial LiDAR scan survey of 2020, together with detailed interviews with 2 direct witnesses of the event, terrestrial and helicopter-borne photographs acquired 12 days after the sudden detachment. The combined interpretation of these new data, allow us to make a more precise estimation of the pre-detachment glacier volume, 12.9 ± 0.6 × 106 m3 and the detached ice volume of 11.7 ± 0.6 × 106 m3 (90% of the total volume of the glacier). We also show that in the 40-year period Aparejo Glacier has recovered 12.4% of the original glacier volume, with a mean ice thickness of 19.5 m and a maximum of 40 m according to GPR data, being preserved within the same basin as the detached glacier. In recent years, the glacier has shown a mean elevation change of −3.7 ± 1.2 m during the 2015–2020 period, with maximum thinning values greater than 8 m, which are probably caused by enhanced ablation due to climate warming and reduced precipitation during the current megadrought which started in 2010 and has lasted more than 1 decade. We conclude that under the projected scenarios of climate warming and reduced precipitation for central Chile, the risk associated to a new detachment of Aparejo Glacier is unlikely.

The last remnants of the Italian Apennine glaciers: 3-D modeling of the Lower Calderone Glacieret by the IDW spatial interpolation of GPR data

Abstract We present a quantitative ground-penetrating radar (GPR) analysis of the Lower Calderone Glacieret to highlight the recent evolution of one of the southernmost glacial systems in Europe. The Upper and Lower Calderone Glacierets are the last two perennial ice bodies in the Apennine Mountains (Italy), and their continuous monitoring is important for glaciology, hydrology and climate science. We applied a proprietary auto-picking algorithm to track reflections accurately and objectively within three pseudo-3-D GPR data sets that were acquired over the Lower Glacieret in different years. After the time-to-depth conversion, the basal reflections were projected onto the normal versors of a 3-D topographic model of the survey area, at the different GPR trace positions. We then applied an Adjusted Inverse-Distance Weighted Spatial Interpolation method to extrapolate the ice-bedrock interface within the areas not directly covered by the GPR profiles and compare it with the topographic surface to recover the glacieret volume. In this paper, we critically examine the accuracy of the reconstructed models, including possible issues related to the challenging survey areas, such as local artifacts in the interpolated interface caused by irregular GPR coverage. We further discuss the various advantages of the implemented procedure with respect to more traditional glacier monitoring techniques.

Angle-corrected GPR hyperbolic fitting models for improved parameter estimation

Ground-penetrating radar (GPR) is commonly employed as a non-destructive technique for detecting subsurface cylindrical objects such as pipes, cables, rebars, and tree roots. Hyperbolic features generated in GPR radargrams are often used to estimate key parameters like burial depth, object radius, and electromagnetic wave velocity. However, traditional approaches frequently rely on the assumption that GPR traverses are perpendicular to the alignment of target—an assumption or a negligence that is not always valid in real-world scenarios. To address this limitation, a novel method is introduced for simultaneously estimating the orientation and burial depth of pipes, as well as wave velocity, from the hyperbolic patterns observed in GPR data. The method innovates by incorporating an angle correction index into the classical hyperbolic fitting model. This modified model is then formulated as an optimization problem, which is solved using a hybrid approach combining the Multi-Verse Optimizer (MVO) and Gradient Descent (GD) algorithms. A unique index, termed the “C-value,” is introduced to quantitatively analyse the influence of oblique angles on the hyperbolic fitting models. Two distinct fitting models are validated through both simulation and field experiments. The study also scrutinizes the impact of varying pipe radius and burial depth on the accuracy of parameter estimation at different pipe orientation. The methodology presented herein enables the simultaneous estimation of burial depth, wave velocity, and pipe orientation directly from hyperbolic fitting—a significant advancement, as orientation is typically assumed as a known input yet is often challenging to ascertain obtain in practical field situations.

InSAR supported by geophysical and geotechnical information constrains two-dimensional motion of a railway embankment constructed on peat

Rail and road embankments constructed on peatlands are subjected to significant challenges due to the high compressibility of peat. They are, therefore, susceptible to considerable settlement following construction. Accurate and timely monitoring of such embankments is important in order to enable proactive intervention strategies and to avoid failures. Multi-temporal Synthetic Aperture RADAR Interferometry (MT-InSAR) techniques applied to satellite-derived SAR data have been shown to be effective for remotely measuring and monitoring the deformation of civil engineering infrastructure. This paper presents a comprehensive approach, involving MT-InSAR supported by local geophysical and geotechnical information, for the detection and investigation of the movement of a railway embankment constructed on peatland. The Small Baselines approach, implemented in StaMPS (referred to as StaMPS-SB), was performed to measure long-term deformation of the embankment in vertical, horizontal east-west and satellite line-of-sight (LOS) directions. In this regard, a Sentinel-1 A/1B dataset composed of 362 descending images, acquired from 2015 to 2022, was analysed to measure LOS displacement. Also, 282 Sentinel-1 A/1B images of both ascending and descending tracks, collected from 2017 to 2022, were analysed to obtain vertical, horizontal and LOS displacements. According to the results, the maximum ground motion rate along the LOS direction was −29 mm/year for the period from 2015 to 2022. However, from 2017 to 2022, this value was about −25 mm/year and − 22 mm/year for descending and ascending tracks, respectively. During this period, the maximum motion rate obtained was −23.1 mm/year in the vertical direction. Moreover, horizontal movement up to −10 mm/year was observed in the westward direction and + 7 mm/year in the eastern direction. We used in-situ data to support corroborate and improve our interpretation of the InSAR results. In this context, peat thickness profiles, records of tamping activities and interpretations of the railway ballast base from ground penetrating radar data, were employed. The in-situ data revealed that the embankment section exhibiting the greatest vertical motion detected by InSAR corresponds closely to the thickest section of railway ballast, the zone of greatest number of tamping activities and the deepest section of peat. As such, this case study highlights how remote detection of embankment motion, coupled with targeted in-situ investigation, can underpin proactive intervention to improve safety.

Automatic Rebar Picking for Corrosion Assessment of RC Bridge Decks with Ground-Penetrating Radar Data

Automatic Rebar Picking for Corrosion Assessment of RC Bridge Decks with Ground-Penetrating Radar Data

Detection and delineation of cracks and voids in concrete structures using the ground penetrating radar technique

The accurate detection of early-age cracking and voids in concrete structures is vital for assessing their health and preventing deterioration, ultimately saving lives and resources. While Ground Penetrating Radar (GPR) offers a widely used method for concrete inspection and defect detection, its accuracy is subject to various limitations influenced by factors such as software, defect dimensions, moisture, and the analysis and interpretation of data. This study presents comprehensive techniques for detecting, delineating, and analyzing hidden cracks and voids in concrete using GPR, validated through laboratory testing and real-world applications. Introducing “DefectInspectoPy,” Python software, aims to address limitations in traditional GPR data processing software and defect detection methods. Results demonstrate the effectiveness of the proposed methods in accurately locating, analyzing, and delineating defects, aiding in determining coring locations for concrete structures. These findings have significant implications for enhancing safety, reducing maintenance costs, and preserving concrete infrastructure integrity.

3D imaging and temporal evolution recognition of concrete internal defects based on GPR

Periodic inspections and recognition of temporal evolution for concrete internal defects are important for the long-term operation of civil engineering infrastructures. This study proposes a method integrating convolutional neural network (CNN) with maximum intensity projection (MIP) for 3D imaging and temporal evolution recognition of concrete internal defects. The proposed method utilizes MIP to process multiple ground penetrating radar (GPR) B-scans and generates 2D projected GPR B-scans containing spatial information. SegNet coupled with the Lovász softmax loss function is introduced to reconstruct 2D defects from projected GPR B-scans. Subsequently, 3D imaging result is reconstructed from 2D imaging results by 3D reconstruction module. Finally, the defect change extraction module realizes recognition of defect changes based on 3D imaging results at different time. The superiority of proposed method is validated based on both synthetic and real GPR data, which presents better recognition results and less time consumption than existing 3D CNN-based method.

Imaging tree root systems using ground penetrating radar (GPR) data in Brazil

Trees sequester carbon dioxide from the atmosphere through photosynthesis, storing it in branches, stems, and roots, where the belowground carbon fraction, approximately ¼ of the total amount, exhibits significant interspecies root biomass variability. Estimating the amount of carbon stored in tree roots of different species is key to understanding an important aspect of climate change and exploring how natural forests, urban tree planting policies, and reforestation projects might help to address it. In this context, one of the most prominent Non-Destructive Testing methods capable of estimating the diameter and length of roots at different depths is ground penetrating radar (GPR). It has been widely used for geological, archaeological, and geotechnical studies due to its accuracy in locating buried material in different contexts, although standards for the correct management of datasets related to belowground root systems are still been developed. This paper reports a GPR signal processing flow to estimate the root diameter of three species of tropical forest trees, and to demonstrate the method’s viability, a dataset was collected in a study area with a 900 MHz shielded antenna. A multi-stage data processing flow is then presented, including raw data, file format conversion, zero-time adjustment, background removal, signal gain, Stolt FK migration, and time-to-depth conversion with hyperbolic adjustment velocity. The resulting data were converted from true amplitude data to a trace envelope. High amplitudes on the envelope section, with lateral continuity in parallel sections, were interpreted as roots. However, the interpretation of outcomes encounters notable complexities, primarily attributable to the intricate nature of subsurface root architectures, the soil matrix characterized by significant clay content, and the co-occurrence of buried materials proximate to the arboreal subjects. Consequently, amplitudes discerned within ground penetrating radar (GPR) 2D sections necessitate cautious interpretation, as they are not immediately indicative of subsurface roots. To overcome this difficulty, this study used direct measurements of the roots in the field, to confirm the GPR data. Despite these complexities, the study demonstrates GPR’s efficacy, particularly in the uppermost soil layer-a pivotal carbon reservoir with a 96% correlation (R2) between GPR-derived coarse-root diameter estimates and field measurements.

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

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.

Review of Ground Penetrating Radar Applications for Bridge Infrastructures

Infrastructure bridges play a crucial role in fostering economic and social development. However, the adverse effects of natural hazard and weather degradation, coupled with escalating rates of traffic, pose a significant threat. The resultant strain on the structure can lead to undue stress, elevating the risk of a critical asset failure. Hence, non-destructive testing (NDT) has become indispensable in the surveillance of bridge infrastructure. Its primary objectives include ensuring safety, optimizing structural integrity, minimizing repair costs, and extending the lifespan of bridges. NDT techniques can be applied to both existing and newly constructed bridge structures. However, it is crucial to recognize that each NDT method comes with its own set of advantages and limitations tailored to specific tasks. No single method can provide an effective and unequivocal diagnosis on its own. Among the various NDT methods, Ground Penetrating Radar (GPR) has emerged as one of the most widely employed techniques for monitoring bridges. In fact, recent technical regulations now mandate the use of GPR for bridge monitoring and characterization, underscoring its significance in ensuring the structural health and longevity of these critical infrastructures. Ground Penetrating Radar (GPR) stands out as one of the most highly recommended non-destructive methods, offering an efficient and timely assessment of the structural conditions of infrastructure. Recognizing the pivotal role of non-destructive testing (NDT) in this context, this paper aims to elucidate recent scientific endeavors related to the application of GPR in bridge engineering structures. The exploration will commence with a focus on studies conducted both at the model level within laboratory settings and on real cases. Subsequently, the discussion will extend to encompass the characterization and monitoring of the bridge’s main elements: slab, beam, and pillar. By delving into these scientific experiences, this paper intends to provide valuable insights into the efficacy and applicability of GPR in assessing and ensuring the structural integrity of bridges. This paper provides a concise survey of the existing literature on the application of Ground Penetrating Radar (GPR) in the assessment of bridges and viaducts constructed with masonry and reinforced concrete, taking into account papers of journal articles and proceedings available on open databases. Various approaches employed in both laboratory and field settings will be explored and juxtaposed. Additionally, this paper delves into discussions on novel processing and visualization approaches, shedding light on advancements in techniques for interpreting GPR data in the context of bridge and viaduct evaluations.

Influence of the sand-clay ratio of the burial material of forensic targets on ground-penetrating radar (GPR) responses–comparison of dry and rainy season data

This study aims to assess the impact of the sand-clay ratio on the detection of simulated forensic targets resembling human burial evidence using ground-penetrating radar (GPR). To achieve this goal, we established a controlled site consisting of four experimental graves (SEPs), with each SEP representing a distinct burial scenario. The project’s objectives encompass evaluating how varying the clay soil content influences the detection of swine targets using 2D, quasi-3D, and 4D GPR imaging techniques. We observed signal attenuation in the GPR data during the rainy season and after reflection from larger targets, primarily due to the conductive behavior of the soft tissues within the targets. We achieved more precise results in an SEP that was 85% sand and 15% clay material, regardless of seasonality. We obtained better results in the dry season due to the greater penetrability of electromagnetic waves and more explicit reflections. In comparison, we achieved more precise results for SEP03, which was 70% sand and 30% clay material. In conclusion, controlled sites are invaluable tools for geophysical investigations, as they provide a controlled environment where we can meticulously adjust various parameters, leading to a more precise and insightful analysis of the results. By systematically manipulating factors such as the soil composition, target depth, and environmental conditions, researchers can effectively calibrate their instruments and methodologies, enhancing the accuracy and reliability of their findings. Therefore, controlled sites not only facilitate comprehensive data collection but also serve as essential platforms for refining and validating geophysical techniques, ultimately advancing the field of geophysical forensics.

Ground-penetrating radar investigation of regolith thickness on a periglacial alpine summit flat, Uinta Mountains, Utah, USA

ABSTRACT Summit flats are low-relief, gently sloping landforms common in periglacial mountain environments. Apart from at their edges where summit flats are truncated by glacial headwalls and at their crests where isolated tors are occasionally present, bedrock is typically mantled on a summit flat by a continuous layer of regolith. This study applied ground-penetrating radar (GPR) to survey the thickness of regolith on a summit flat in the Uinta Mountains (Utah, USA). More than 500 m of GPR data were collected along transects extending from the edge of the summit flat to the crest, as well as adjacent to a deep soil pit. Results indicate that the regolith thickness is quite variable, with a mean of 91 ± 38 cm when calculated with an appropriate radar velocity. Because the ground surface of the summit flat is notably smooth, the variability in thickness is a consequence of irregularities in the bedrock surface at depth, which is significantly rougher. Recognition that regolith thickness can vary considerably beneath an alpine summit flat has implications for soil formation, carbon storage, and the transmission and storage of shallow groundwater, as well as evolutionary models for periglacial mountain landscapes.

A Clutter Removal Method Based on the F-K Domain for Ground-Penetrating Radar in Complex Scenarios

Ground-penetrating radar (GPR) is a classic geophysical exploration method that utilizes the emission and reception of electromagnetic waves to non-destructively detect target objects in the target medium. It has been widely applied in various fields such as pipeline detection, cavity detection, and rebar detection. However, GPR systems are susceptible to environmental clutter interference, which poses challenges for data interpretation and subsequent processing. In this paper, the separability of clutter and target signal in the frequency-wavenumber (F-K) domain is validated through modeling, leading to the proposal of a comprehensive clutter removal method based on the F-K domain for complex scenarios. The direct coupling wave is initially eliminated by applying a peak matching mean subtraction filter, which avoids the artifacts. Subsequently, the F-K domain transformation is performed and surface clutter undulations are effectively removed using a method based on singular value decomposition and k-means clustering. Finally, an angle filter with Gaussian tapering at the edges is designed based on physical models to efficiently eliminate linear interference without undesired ringing interference. The commonly used clutter removal algorithms, including mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and traditional F-K filtering methods, are compared with the proposed algorithm on both the numerical simulated data and actual GPR data. The results from visual and quantitative analysis confirm that our proposed method is more effective than current commonly used clutter suppression algorithms. We have successfully enhanced the Signal-to-Clutter Ratio (SCR) of the GPR data, resulting in an Improvement Factor (IF) of 30.63 dB, 23.59 dB, and 30.60 dB for simulated data, experimental data, and TU1208 public data, respectively. The detection capability of buried targets is enhanced, thereby establishing a solid foundation for subsequent data interpretation and target identification.