Ground Penetrating Radar Signal Research Articles

Bayesian optimization based extreme gradient boosting and GPR time-frequency features for the recognition of moisture damage in asphalt pavement

Moisture damage is one of the major defects in asphalt pavement, and will evolve into potholes in a short time which will affect traffic safety. Ground Penetrating Radar (GPR) is an effective non-destructive testing (NDT) method for detecting moisture damage but its data explanation replies on human experience and subjects to labor intensive. To address this issue, an automatic detection method based on extreme gradient boosting (XGBoost) combined with Bayesian hyper-parameter optimization (BHPO) was proposed to detect moisture damage area from GPR traces. High frequency GPR antenna with 2.3 GHz was used to detect the moisture damage area from simulation, laboratory and field tests, and moisture damage dataset with 7960 traces was collected. Thirty time-frequency parameters were extracted from each GPR trace, normalized to unify the three data source, and then optimized into 12 sensitive parameters by feature importance method. These 12 parameters were used to build the recognition model with XGBoost, and the model tuning parameters were optimized by BHPO. To obtain optimization model, random forest (RF) and artificial neural network (ANN) were also trained with BHPO, and compared with XGBoost model. The results indicate that performance of XGBoost model with BHPO achieves the highest accuracy and lowest time cost both in moisture damage and normal trace classification, the accuracies for moisture damage are XGBoost (96.9%) > ANN (95.6%) > RF (95.4%), respectively, and normal are XGBoost (96.5%) > RF (96.1%) and ANN (96.0%), respectively. On this basis, field tests were conducted by core samples, which verified the correct result of XGBoost model. Our method provides a swift and accurate method to locate subsurface targets directly from GPR signals.

Evaluation of the variation of the GPR frequency spectra created by the activities of earthworms

The analysis and utilization of frequency spectrum in emission signals from ground penetrating radar (GPR) are essential for investigating soil characteristics and enhancing urban management. This research focuses on assessing the effectiveness of earthworm activities in improving rainwater management in regions with clay soil, where water infiltration is challenging. Specifically, we examined the impact of two earthworm species, Lumbricus terrestris and Aporrectodea caliginosa, as well as a combination of both, on the permeability of clay loam soil. To evaluate earthworm activities, a gravity-driven film flow setup with nine tanks was employed. Furthermore, we suggest utilizing the Discrete Fourier transform to analyse the frequency spectra of different data types (traces, transects, and the average of tanks) and comprehend their influence on earthworm behavior. To investigate the relationship between fluctuations in infiltration velocity and the frequency spectrum signal over time and activity position, a correlation analysis was performed using linear regression models. The goodness of fit was assessed based on the coefficient of determination (R2). For the earthworm species L1, L2, and L12, the R2 values were determined as 0.8858, 0.847, and 0.9493, respectively. These results demonstrate that the frequency domain was derived from variations in ground penetrating radar signals, and a significant correlation exists between infiltration velocity and spectra influenced by earthworm activity.

Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning

Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute to the complexity of signal interpretation. To address these challenges, this study fully considers the unique advantages of convolutional neural networks (CNNs) in accurately identifying underground rock formations and lithological structures, particularly their powerful feature extraction capabilities. Deep learning models possess the ability to automatically extract complex signal features from radar data, while also demonstrating excellent generalization performance, enabling them to handle data from various geological conditions. Moreover, deep learning can efficiently process large-scale data, thereby improving the accuracy and efficiency of identification. In our research, we utilized deep neural networks to process GPR signals, using radar images as inputs and generating structure-related information associated with rock formations and lithological structures as outputs. Through training and learning, we successfully established an effective mapping relationship between radar images and lithological label signals. The results from synthetic data indicate a rock block identification success rate exceeding 88%, with a satisfactory continuity identification of lithological structures. Transferring the network to measured data, the trained model exhibits excellent performance in predicting data collected from the field, further enhancing the geological interpretation and analysis. Therefore, through the results obtained from synthetic and measured data, we can demonstrate the effectiveness and feasibility of this research method.

Detection of air- and water-filled cavities beneath concrete plate using electromagnetic and acoustic waves

Underground excavation and tunneling may result in cavities filled with air or water. This study aims to detect cavities beneath underground concrete walls or tunnel linings and assess their risks by estimating the cavity thickness and width using electromagnetic and acoustic waves. In this study, air- and water-filled cavities are simulated, and nondestructive testing methods such as ground penetrating radar (GPR), time domain reflectometry (TDR), and microphones are used to investigate the thickness and width of a cavity beneath a reinforced concrete plate. The results show that GPR signals converted from temporal to spatial scales, combined with TDR measurements, can be used to accurately examine the cavity thickness. However, the examination of the cavity width based on GPR signals remains challenging because of the strong parabolic patterns caused by steel bars. Therefore, this study employs flexibility, which is the ratio of the particle velocity of the structure to the load signal according to the frequency. The flexibility of the concrete plate calculated using microphone signals increases in the cavity section for both air- and water-filled cavities because the flexural vibration mode prevails at the cavity–concrete plate interface. The characteristics of the microphone signals analyzed in the context of flexibility enabled the prediction of the cavity width beneath the concrete plate in both air- and water-filled cavities. GPR and TDR signatures combined with microphone signals can be used for the detection and risk assessment of cavities beneath underground structures when subjected to variations in the groundwater level.

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.

Studying GPR's direct and reflected waves

AbstractAs the transmitter and receiver (Tx and Rx, respectively) are located in close proximity during a typical ground‐penetrating radar (GPR) survey, the powerful signal generated by the Tx and which is then recorded by the Rx at various time delays, can be saturated at early times (i.e., this is the direct wave (DW) signal reaching the Rx). This often causes the masking of shallow targets, complicating data interpretation. In this study, our aim is to examine the spatial distribution of the electromagnetic signals around the Tx, attempting to locate areas where the DW becomes minimum, whereas the signal strength from subsurface targets (i.e., reflected wave – RW) remains ideally unchanged. The position of these local minima in the DW signal could give rise to advantageous Tx–Rx configurations, where clear reflections from subsurface targets lying at shallow depths can be obtained with the least possible involvement of the DW. To perform such a study, we carried out static field measurements over a flat lying reflector as well as numerical simulations in a reflection, common‐offset mode around a transmitting antenna. In the field, we also collected wide‐angle reflection–refraction data to determine the GPR wave velocity in the uppermost layer. GPR signals were recorded by the Rx around the Tx in three concentric circles of various radii (i.e., varying the Tx/Rx separation), using a specific angular step and varying the Tx/Rx polarization each time. The synthetic data were produced using a three‐dimensional finite‐difference time‐domain modelling tool. Field and numerically simulated data were analysed and compared to study the behaviour of both the DW and RW events around the Tx when changing the Tx/Rx distance, their respective angular position, as well as their relative polarization/orientation.

Numerical modelling of bridge deck reinforcement corrosion based on analysis of GPR data

AbstractThis study explores the impact of corrosion on Ground Penetrating Radar (GPR) responses through practical experiments and numerical modelling, focusing on rebar diameter reduction, corrosion product layer thickness, crack formation and corrosion product filling in vertical and transverse crack. Practical experiments involved GPR testing of reinforced concrete slab. By analyzing B-scans we identify areas with moderate and severe corrosion. Numerical modelling using the Finite Difference Time Domain (FDTD) Method to model GPR signal propagation in a concrete bridge deck with corrosion is applied. Key finding includes a significant 26.70% increase in reflected wave amplitude when corrosion product filling in vertical crack increased by 400%, highlighting its extensive effect on signal GPR propagation. Reduced rebar diameter led to a 9.79% amplitude decrease and a 0.06 ns arrival time delay. Increased corrosion product layer thickness primarily affected arrival time with a 0.06 ns extension but significantly amplified GPR signal amplitude. These findings offer insights for improving GPR based corrosion detection and assessment methods, leading to more robust systems for concrete bridge deck inspection and maintenance. This paper contributes to understanding how corrosion affects the signal that is detected by GPR. This information can be used to improve the way that we manage and assess corrosion in concrete bridge deck.

Development of a method for identifying cracks in permafrost rocks based on differentiation of GPR data

Relevance. The presence of cracks significantly affects the physical and mechanical properties of rocks, which must be taken into account when planning mining operations and building mining structures. In the conditions of the distribution of permafrost rocks, characteristic of the North-East of Russia, the study of fracturing is possible using the ground penetrating radar method, which is used to assess the structure of rock masses of placer deposits. The criteria for identifying cracks based on the characteristics of GPR wave fields are currently known, and the main problem preventing the full use of the GPR method for studying cracks in subsurface layers of rocks is the significant complexity of processing and interpreting GPR measurement data. The purpose of the work is to develop a method for processing the results of ground penetrating radar measurements to identify cracks in frozen rocks. Methods. Physical and computer modeling of GPR measurements of a rock mass with a fracture, differential calculus methods for analyzing the GPR wave field structure model. Results and their scope. As a result of the research, based on an early developed model of the structure of the GPR wave field, the use of the differentiation operation for identifying cracks in permafrost rocks was justified. Special cases of rectilinear inclined and horizontal axes of in-phase of ground penetrating radar signals are considered and corresponding formulas are obtained for them. The theoretical results obtained were tested on computer and physical modeling data, as well as on field measurement data. An analysis of the features of the signals after differentiation was carried out, and the features of the original radargrams were noted, which may interfere with the reliable interpretation of the results obtained. Conclusions. In the practice of processing data from ground penetrating radar measurements, the results of the research will make it possible to more quickly identify zones of increased fracturing in rocks. In the future, it is planned to develop an algorithm and software that implements it for mapping rock fractures using ground penetrating radar data.

Electromagnetic property selection for GPR modelling in corrosive concrete environments

Understanding the mechanisms that alter the ground penetrating radar (GPR) electromagnetic wave propagation as a result of reinforcement corrosion is pivotal for accurate assessment of the corrosion of reinforced concrete (RC) structures. Nevertheless, the behaviour of the GPR signal during the complex corrosion process is not thoroughly understood. In this study, finite-difference time-domain (FDTD) modelling was used to analyse the effects of corrosion-related parameters, i.e., moisture, chlorides, and corrosion products, on the electric field strength. This study aims to expand the database on numerical simulations of GPR signal behaviour in corrosive environments. It also addresses the knowledge gap in modelling the frequency-dependent properties of concrete and iron oxides. Modelling approach adopted in the study was validated with experimental data obtained on laboratory specimens that correspond to the numerical models in terms of geometry and condition.

Using Ground Penetrating Radar (GPR) to Predict Log Moisture Content of Commercially Important Canadian Softwoods

The non-destructive testing of wood fibre properties is crucial for informing forest management decisions and achieving optimal resource utilization. Moisture content (MC) is an important indicator of wood freshness and may reveal the presence of wood degradation. However, efficient methods are still needed to better monitor this property along the forest–wood value chain. The objective of the study was to develop prediction models to evaluate log MC based on the propagation of ground penetrating radar (GPR) signals. A total of 165 trees representing four species (black spruce (Picea mariana (Mill.) B.S.P.), white spruce (Picea glauca (Moench) Voss), red spruce (Picea rubens Sarg.), and balsam fir (Abies balsamea (L.) Mill.)) were harvested in two regions of the province of Quebec. GPR signals were acquired in the green (fresh) state and at three subsequent drying stages. Partial least squares regression (PLSR) and locally weighted PLSR (LWPLSR) were employed to establish relationships between GPR signals (antenna frequency: 1.6 GHz) and log properties. The models were fitted on three calibration sets containing four drying stages and different species mixes. The LWPLSR models performed better than the PLSR models for predicting log MC, with a lower root mean square error (RMSEp range: 10.8%–20.2% vs. 13.0%–20.5%) and a higher R2p (0.63–0.87 vs. 0.62–0.82). Spruce-only models performed considerably better than fir-only models while multi-species models were in-between. Despite the complex anisotropy of wood and the physics of wave propagation, the GPR technology can be successfully used to estimate log moisture content, but the GPR-based MC models should be calibrated for each specific type of wood material.

Simulation and Modelling of Tree Roots using GPR and Numerical Analysis

Traditional methods to check tree roots are complicated due to their destructive nature and limited quantitative assessments in long-term research. Therefore, this study aims to understand the synergistic use of the Ground Penetrating Radar (GPR) to obtain accurate information on tree root health. For this purpose, geophysical and surveying techniques are used. In the study, image data of tree roots were obtained using IDS Duo GPR under normal conditions. To obtain the most accurate results in GPRMax, the simulation used the greatest contrast dielectric value of soil and roots. Then, an analysis is conducted to compare synthetic and real data. Modelling is crucial to understand electromagnetic wave propagation and interaction with tree roots. First, the synthetic hyperbola’s shape is compared with the real root’s hyperbola. Second, roots with increasing diameters were simulated and the time interval associated with each diameter was determined to produce a regression line model. Finally, depending on the real-time interval and the collected data, the regression model is utilised to estimate the true diameter. The study found the following: (1) the results show that the high dielectric value of the detected roots, real and synthetic hyperbola, have similar amplitude and tail; (2) the findings demonstrate that the estimation model is good with an average error of ±8 mm under ideal conditions and ±20 mm under normal conditions. The estimation variation is strongly influenced by soil moisture. The GPR resolution and signal deteriorate when the high soil moisture content is high. As a result, this study could provide vital insight for more effective assessment of tree roots and serve as an important reference for researchers seeking to expand on present findings.

Experimental Validation of Enhanced GPR Signals by A Broadband Metasurface

In this paper, the potential of a broadband metasurface for ground penetrating radar (GPR) signal enhancement is investigated by simulation and experiments. Simulation results show that the reflection at the air-MUT interface can be reduced from 35% to 5% over a broad frequency range (relative bandwidth up to 44%) when the broadband metasurface is in place. Measured reflectance is consistent with the simulation results. Meanwhile, the electric field strength measurement results demonstrate that the transmitted electromagnetic signals can be amplified when the reflection is reduced over the same frequency range. GPR experiments verified that clear hyperbolic signals emerged for commonly undetectable pipes when the high-frequency signals are enhanced. The proposed broadband metasurface can be an effective solution for the detection of nonmetallic inclusions in high-lossy media.

Estimation of Soil Moisture Content of Farmlands Based on AEA Method of GPR

We used ground penetrating radar (GPR) to estimate the soil moisture content and its spatial distribution of farmland under different tillage modes in this paper. GPR data are collected on the farmlands with no-tillage (NT) and deep ploughing (DP), respectively. The average envelope amplitude (AEA) of GPR signal is used to estimate the soil moisture content which is constrained by time domain reflectometry (TDR) measurement. The findings of the field test indicate that the error of the moisture content obtained by AEA method is in a reasonable range. In the shallow layer of farmland, the AEA method of GPR for detecting the soil moisture content is feasible and different tillage modes can affect the change of soil moisture content.

Application of Variational Mode decomposition in GPR Detection of Oil Contaminated Soil

In this article, Ground penetrating radar (GPR) is used to investigate the contaminated soil by oil pollutants in oilfield operation area. Time frequency analysis method is applied to GPR data to reveal the changes of GPR signal frequency components with time variation, which is very important for data interpretation. We use variational mode decomposition (VMD) for time frequency analysis. It can decompose radar signal into several quasi-orthogonal intrinsic mode functions (IMFs) with limited bandwidth, and has strong robustness and local decomposition ability. We apply the VMD on field data acquired in the area with waste liquid contaminated soil in oilfield. The decomposed IMFs from GPR data shows that the signals from the contaminated soil is characterized by the strong energy and low frequency. Then the instantaneous attribute analysis of the IMF is carried out to delineate the scope and thickness of contaminated soil. The experimental results show that the limited bandwidth IMF with specific sparsity obtained by VMD method is more conducive to the processing and interpretation of GPR data.

GPR Signal Analysis for the Exploration of Loose Zones in the Near Surface Underground

In the paper, non-standard signal and image processing applied for the GPR (Ground Penetrating Radar) data record for various antennae orientations was presented and discussed. The terrain surveys were carried out in the post-mining region in Poland where numerous sinkholes and subsidence areas were observed on the surface due to the former mining activity. The GPR surveys were conducted between two existing sinkholes and the aim of measurements was detection of loose zones in the ground created by suffosion process which caused the formation of the mentioned two sinkholes. In the paper, the Author proposed a new way of processing and analysis of radargrams based on three steps, i.e. 1D more advanced processing of signals/traces, 2D more advanced image processing of combined radarogram, visualisation and analysis of selected signal attributes.

Estimating annual CO2 emissions and building a leakage scenario in a tectonically quiescent area

Soil CO2 flux (FCO2) was intensively measured in a natural CO2 emission site located in a tectonically quiescent area, with topographic and geophysical investigation, to estimate annual emissions and causes of focused leaks. FCO2 varied between 6.5 and 2155.4 g/m2/d (median 76.1 g/m2/d; n = 67) within a 612 m2 area situated close to a CO2-rich groundwater well. The threshold value for geogenic FCO2 was determined to be 23 g/m2/d using a Q-Q plot. A FCO2 hotspot was delineated where FCO2 exceeded the threshold. Based on linear regression using air pressure and temperature, the annual CO2 emissions from the hotspot in 2019 were approximately 19 tons/y. According to the 2D electrical resistivity distribution, the subsurface around the well was highly weathered, showing low electrical resistivity (<2000 Ω-m) down to a depth of 50 m, whereas the top layer around the hotspot showed a relatively high electrical resistivity (> 500 Ω-m), implying a resistant unsaturated zone. High signal attenuation and discontinuous and dipping reflectors of ground-penetrating radar signals suggested gaseous CO2 transport through preferential pathways in the resistant near-surface. Based on these and previous hydrochemical results, the extremely high FCO2 in the narrow extent can be explained by a leakage scenario in which the frequent usage of CO2-rich water through the well enhances CO2 degassing, while defects in the well casing accelerate the upward movement of CO2 gas, which flows out through a preferential pathway in the resistant unsaturated zone. This study provides insights into focused leakages through faulty wells and near-surface preferential pathways. In addition, the quantification of annual CO2 emissions through point sources is applicable for assessing natural gas emissions produced by hotspots. The non-negligible emission rate in the hotspot (about 92 g/m2/d) emphasizes the need to quantify natural CO2 emissions in tectonically quiescent areas for risk assessments and carbon budgets.

Cement pavement void detection algorithm based on GPR signal and continuous wavelet transform method

The dimension of the void area in pavement is crucial to its structural safety. However, there is no effective method to measure its geometric parameters. To address this issue, a void size extraction algorithm based on the continuous wavelet transform (CWT) method was proposed using ground-penetrating radar (GPR) signal. Firstly, the finite-difference time-domain (FDTD) method was used to investigate the GPR response of void areas with different shapes, sizes, and depths. Next, the GPR signal was processed using the CWT method, and a 3D image based on the CWT result was used to visualize the void area. Based on the differences between the void and normal pavement in the time and frequency domains, the signal with maximum energy from the CWT time–frequency result was extracted and combined to reconstruct the new B-scan image, where void areas have energy concentration phenomenon. Based on this, width and depth of void detection algorithm was proposed to recognize the void area. Finally, the detection algorithm was verified both in numerical model and physical lab model. The results indicated that the CWT time–frequency energy spectrum can be used to enhance the void feature, and the 3D CWT image can clearly visualize the void area with a highlighted energy area. After fully testing and validating in numerical and lab models, our proposed method achieved high accuracy in void width and depth detection, providing a precise method for estimating void dimension in pavement. This method can guide DOT departments to carry out pre-maintenance, thereby ensuring pavement safety.

An Approach for Easy Detection of Buried FRP Composite/Non-Metallic Pipes Using Ground-Penetrating Radar.

Pipelines remain the safest means of transporting natural gas and petroleum products. Nonetheless, the pipeline infrastructure in the US is facing major challenges, especially in terms of corrosion of steel/metallic pipes and excavation damage of onshore pipelines (leading to oil spills, explosions, and deaths). Corrosion of metallic pipelines can be avoided by using non-corrosive materials such as plastic pipes for low-pressure applications and glass-fiber-reinforced polymer (GFRP) composite pipes for transporting high-pressure oil and natural gas. However, buried non-metallic pipelines are not easily detectable, which can lead to increased excavation damage during construction and rehabilitation work. Alternative strategies for making buried non-metallic pipes easily locatable using ground-penetrating radar (GPR) were investigated in this study. Results from this study have shown that using carbon fabric or an aluminum foil overlay on non-metallic pipes before burying in soil significantly increases the reflected GPR signal amplitude, thereby making it easier to locate such pipelines. The reflected GPR signal amplitude for pipe sections with carbon fabric or aluminum foil overlays was found to have increased by a factor of up to 4.5 over the control samples. The results also highlight the importance of selecting the appropriate antenna frequency for GPR surveys, since wet silt loam soil and clay significantly reduce the penetration depths of the radar signals produced by the GPR antennae.

Non-destructive compaction quality evaluation of runway construction based on GPR data

ABSTRACT Assessing the compaction quality of rockfill materials is an essential link in the runway construction. However, traditional on-site limited sampling detection is not only time-consuming and labour-intensive, but also destructive. Common ground penetrating radar (GPR) calculates the relative permittivity of a material by measuring its thickness. Nonetheless, assessing the thickness of runway construction materials poses a significant challenge. To address this challenge, this paper focuses on the Hilbert -Huang transform (HHT) analysis of GPR signal of different compacting materials, and then replaces the traditional method of calculating the relative permittivity by measuring the thickness of materials. At the same time, taking the runway as an example, this work verified a crest factor (CF) index effectiveness in predicting the relative compaction of rockfill material through HHT analysis of GPR signals collected in the field, with an average error rate of 4.03%. Finally, Kriging interpolation method is used to estimate the compaction quality of any point, and the corresponding heat map of compaction quality evaluation is generated to determine the area of insufficient compaction in the construction process. Highlights The GPR method is used for non-destructive testing of the compaction quality of runway rockfill materials; Compaction quality evaluation by multiple indicators of GPR data signal processing. The influence of number of roller passes, materials mix ratios and different rockfill materials on the compaction quality of runway are analysed. The applicability of the GPR method is verified by runway construction tests.