Ground Penetrating Radar Signal Research Articles

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.

Processing GPR Surveys in Civil Engineering to Locate Buried Structures in Highly Conductive Subsoils

Many studies have illustrated the great benefit of ground-penetrating radar (GPR) in civil engineering. However, in some cases, this geophysical survey method does not produce the desired results due to the electromagnetic characteristics of the subsoil. This study presents the results obtained in two locations near Linares (southern Spain), evaluating the detection of structures buried in conductive host materials (0.02 S/m in site 1 and 0.015 S/m in site 2) characterized by strong signal attenuation. Accounting for the study depth, which was 1.5 m, a 500 MHz shielded GPR antenna was used at both sites. At the first site, a controlled experiment was planned, and it consisted of burying three linear elements. An iron pipe, a PVC pipe, and a series of precast blocks were buried at a depth of 0.5 m in a subsoil composed of highly conductive clayey facies. To eliminate additional multiples caused by other superficial structures and increasing the high-frequency content, the predictive deconvolution flow was applied. In the 3D processing, the cover surfaces technique was used. Once the acquired GPR signals was analyzed and the optimal processing flow established, a second site in which different infrastructures in a conductive host medium formed by marly facies was explored. The 2D flow and 3D processing applied in this work allows to detect and see the continuity of some structures not visible for the default processing.

Unpaved road characterization during rainfall scenario: Electromagnetic wave and cone penetration assessment

This study aims to examine the effect of rainfall on integrity assessment on unpaved roads using electromagnetic waves and cone penetration. An unpaved road testbed is prepared with sandy soil in densely- and loosely-packed conditions. Site investigations using a time domain reflectometry (TDR), ground penetrating radar (GPR), and dynamic cone penetrometer (DCP) are performed on the testbed under before and after rainfall scenarios, and an additional laboratory compaction test using the soil samples is conducted. The results show that the spatially scaled GPR signal converted by the TDR signal can accurately estimate the interface depth. In addition, rainfall may reduce the accuracy of GPR signal polarity-based analyses for anomaly detection on unpaved roads. This study reveals that TDR, GPR, and DCP with simple laboratory compaction tests and physics-inspired effective stress-depth models can be a powerful tool for underground mapping when subjected to rainfall season.

Rapid Identification and Location of Defects behind Tunnel Lining Based on Ground-Penetrating Radar

Ground-penetrating radar (GPR) is a mainstream tool to detect defects behind tunnel linings, but the difficulty in interpreting GPR signals limits its application. This paper proposes an intelligent way to differentiate the defects behind tunnel linings by means of a model test and feature parameter analysis, to achieve an efficient and accurate analysis of GPR signals. The model test reveals the differences between defect and non-defect GPR data in amplitude, signal entropy, standard deviation, and other feature parameters. The amplitude of the defect data was on average six times as large as that of non-defect data, and the signal entropy was about 1.2 times larger. The two feature parameters lay the basis for defect detection by GPR. On this basis, a GPR-based automatic identification method was proposed, and the program was compiled on MATLAB. The program achieved a defect recognition rate as high as 96% through a field test at a rapid speed. The program is highly accurate and feasible for detecting defects behind tunnel linings with the aid of GPR.

Automatic Life Detection Based on Efficient Features of Ground-Penetrating Rescue Radar Signals.

Good feature engineering is a prerequisite for accurate classification, especially in challenging scenarios such as detecting the breathing of living persons trapped under building rubble using bioradar. Unlike monitoring patients' breathing through the air, the measuring conditions of a rescue bioradar are very complex. The ultimate goal of search and rescue is to determine the presence of a living person, which requires extracting representative features that can distinguish measurements with the presence of a person and without. To address this challenge, we conducted a bioradar test scenario under laboratory conditions and decomposed the radar signal into different range intervals to derive multiple virtual scenes from the real one. We then extracted physical and statistical quantitative features that represent a measurement, aiming to find those features that are robust to the complexity of rescue-radar measuring conditions, including different rubble sites, breathing rates, signal strengths, and short-duration disturbances. To this end, we utilized two methods, Analysis of Variance (ANOVA), and Minimum Redundancy Maximum Relevance (MRMR), to analyze the significance of the extracted features. We then trained the classification model using a linear kernel support vector machine (SVM). As the main result of this work, we identified an optimal feature set of four features based on the feature ranking and the improvement in the classification accuracy of the SVM model. These four features are related to four different physical quantities and independent from different rubble sites.

Quantifying the impact of parameters of chloride-induced reinforcement corrosion on the GPR signal

The sensitivity of the high-frequency ground penetrating radar (GPR) signal to changes within the reinforced concrete (RC) makes it a valuable tool for corrosion assessment of RC structures. The most important parameters in chloride-induced corrosion are moisture content, chloride content, and reinforcement diameter loss. The objective of this laboratory study was to investigate and quantify the influence of these parameters on the GPR signal. Of particular interest was to determine which of the observed parameters the GPR is most sensitive to. Furthermore, particular emphasis was placed on understanding the influence of corrosion products, which has not met the consensus in the existing literature. The parameters were studied on a total of forty-two specimens where the reflector of the GPR waves was the reinforcement embedded in the concrete specimen. Based on the experimental data, values of normalized amplitude related to the evaluation of corrosion by GPR were proposed.

Super-resolution reconstruction method of ground penetrating radar signals based on wavelet theory and its application in reverse time migration

The finite difference time domain (FDTD) method was used to solve the Maxwell’s equation to obtain the reverse time migration (RTM) of ground penetrating radar (GPR) signals, namely, the FDTD-RTM. In order to ensure that the correct numerical solution of iterative calculation was achieved, it was necessary to obtain high-resolution signals, which greatly limits the applicability of FDTD-RTM in engineering. Based on the characteristics of wavelet multi-resolution analysis, this study proposed a super-resolution signal reconstruction method to improve signal resolution, with the view to completely solving the problem of FDTD-RTM limitation caused by insufficient signal sampling using GPR. The results of electromagnetic simulation showed that the signals reconstructed by the above method were highly similar to the signals sampled with the same resolution. On this basis, reverse time migration electromagnetic simulation and physical model tests were designed. The results of both experiments showed that the under-sampled GPR signals could achieve FDTD-RTM following super-resolution reconstruction, and that the migration imaging results of the target were basically consistent with the design scheme. The signal super-resolution reconstruction method based on the wavelet theory was thus shown to successfully achieve the overall application of FDTD-RTM in GPR signal analysis.

The Possibility of Using Terrestrial-Based Ground Penetrating Radar (GPR) Technology for Supplying 3rd Dimension Information for A Search and Recovery Mission for Landslide Victims

This paper highlights the possibility of using GPR for providing third-dimension (depth) information to facilitate a landslide search and recovery (SAR) mission in Malaysia. The study was based on an actual use case during the 2022 landslide tragedy that occurred at the Father’s Organic Farm, Batang Kali. Two sets of MALA RAMAC X3M with shielded antennas (250Mhz and 500 Mhz) were used to survey a 1m x 1m profile interval at a 30m x 20m and 8m x 6m grid areas in Sector B on the 18th and 19th December 2022. Grid line profiles 2211-A, 2212-A, and 2213-A detected by the 250Mhz antenna showed suspicious reflection patterns. The pattern's amplitude contrast in relation to the soil background and the consistency with the average Malaysian adult stature were considered as the most likely locations of landslide victims. The location of the reflection was viewed with greater accuracy and clarity utilising time slice y-cut on 3D processing in the Reflex3DScan ReflexW module. On 21st December 2022, a victim and his two dogs were recovered by the SAR team near the suspected GPR line profiles at sector B. The suspected GPR signal reflection corroborated with the proximity where the victim was found according to the special SAR victim location map published by authorities. Since access to ground zero post excavation was restricted, on-site validation of the suspected profiles was not possible. Nonetheless, because hyperbolas were detectable at lower frequency with the maximum depth of around 8m, this paper concludes that using terrestrial-based GPR as a search and recovery alternative for buried landslide victims is still feasible. The challenge would be having a skilled operator to detect a hyperbola or abnormality in a time-critical scenario. The study also concluded that terrestrial-based GPR would, at the very least, provide first responders with situational awareness by narrowing down the SAR potential locations, excavation depths and reducing time for searching and recovering victims, as concurred by the Batang Kali SAR team. Article Details How to Cite Halim, N., Abdullah, N., Ghazali, M., &amp; Hassan, H. (2023). The Possibility of Using Terrestrial-Based Ground Penetrating Radar (GPR) Technology for Supplying 3rd Dimension Information for A Search and Recovery Mission for Landslide Victims. International Journal of Geoinformatics, 19(5). https://doi.org/10.52939/ijg.v19i5.2669

Characterization of a leaky earth dam using integrated geophysical surveys

Early earth dams were mainly constructed using a soil core wall, which is susceptible to a variety of potential risks such as inhomogeneity, poor compactness, and leakage due to multiple fillings and biological damage. To ensure safe operation of the dam, cost-effective geophysical non-destructive testing techniques should be considered during routine maintenance. This paper presents a case study of an earth dam designed for water storage and flood control that experienced a leak problem. To provide a basis for future leakage control, we carried out 2D integrated geophysical surveys utilizing Electric Resistivity Tomography (ERT), Ground Penetrating Radar (GPR), and Surface Wave (SW) methods along the dam axis. In conjunction with the fast attenuation characteristic of GPR signals, the low resistivity anomalies recovered by the ERT provide evidence to reveal the leakage area within the dam structure. Moreover, the shear wave velocity variation retrieved by the SW method helps to determine the distribution of bedrock and soil layers. By integrating the results mentioned above, the interpretation outcome yields a comprehensive understanding of the internal condition of the earth dam. The successful investigation of the dam structure and the mapping of the leakage area demonstrate the effectiveness of integrated geophysical surveys in characterizing earth dams. It also suggests that it is essential to consider the difference between various geophysical results and the underlying reason when integrating them to characterize leaky earth dams.

Accuracy Assessment of GPR Data for Buried Objects with Different Pipes and Soil-Based Conditions

In terms of accuracy and speed, Ground Penetrating Radar (GPR) is the best approach for detecting and identifying underground utilities. This technology can precisely find a wide range of underground utilities, including both metallic and non-metallic materials. It analyses the ground by emitting a signal from an antenna at various frequencies of electromagnetic (EM) pulses. However, undesirable echoes caused by heterogeneous materials, such as the wide range of soil properties and utilities, are always present in these reflected signals. The site's soil composition has a direct influence on the accuracy of the GPR signal image. Thus, this study is carried out to evaluate the accuracy of GPR data for buried objects with different types of pipes between PVC and iron pipe in different soil characteristics: fine sand, topsoil and silt soil. The objective is to interpret the resolution of radargram images on different soil types due to different soil based characteristics and to evaluate the accuracy of depth values between GPR and conventional survey data sets for different pipes and soils using the RMSE formula. GPR Electronic TriVue with high frequency (1GHz) was employed, and the resolution of the resulting radargram image was post-processed in ReflexW software to yield promising depth results. Based on this research, the radargram obtained shows different textures that provides different presentations of each soil on the radargram image. Accuracy assessment from RMSE depth difference for Iron pipe depth for the three different soil types are: topsoil is 0.025 m, silt soil is 0.032 m, and fine sand is 0.087 m. While for PVC pipe topsoil is 0.035 m, silt soil is 0.038 m, and fine sand is 0.093 m. These differences show that iron pipe is more accurate compared with PVC in terms of tendency and fine sand is suitable soil in detection compared with topsoil and silt soil. In conclusion, the type of pipe play role in the choice of utility and soil properties (texture, moisture, and electrical conductivity) that impact the most on the accuracy assessment of GPR Data.

Ground Penetrating Radar to detect dinosaur bones within a Cretaceous hard limestone in Sicily

Ground Penetrating Radar (GPR) can be important in facilitating planning for the identification and extraction of buried vertebrate remains, particularly at sites that are difficult to access, where excavation would be costly and labor-intensive. This is the case at Grotta Lunga (northwestern Sicily), one of the southernmost European dinosaur sites, a key site for understanding the relationships between African and European dinosaurs. An incomplete theropod bone was discovered at this site in 2005, and to date there have been no further finds due to the difficulties of excavation in hard rock and in a cave environment. With the aim of exploring deeper into the rock volume around the bone, we tested GPR as a method to investigate the Cretaceous limestone at Grotta Lunga. Despite its potential, GPR has been rarely applied in paleontology and very seldom has led to the successful detection of buried dinosaur bones. Our analysis identified some remarkable GPR reflections on the cave wall, in the vicinity of the dinosaur bone, indicating the existence of reflective objects embedded in a position stratigraphically compatible with the dinosaur remains. Data processing was carried out to show the best result without compromising data quality. For this purpose we used the synthetic hyperbola method, Kirchhoff migration and Hilbert transform. The GPR signals detected at Grotta Lunga in the graphical form of hyperbolas are interpreted as the result of reflections produced by fossil bones embedded in the Cretaceous limestone investigated and indicate the presence of additional dinosaur remains. Our investigation provides useful information for planning future excavations.

High-Throughput Canopy and Belowground Phenotyping of a Set of Peanut CSSLs Detects Lines with Increased Pod Weight and Foliar Disease Tolerance

We deployed field-based high-throughput phenotyping (HTP) techniques to acquire trait data for a subset of a peanut chromosome segment substitution line (CSSL) population. Sensors mounted on an unmanned aerial vehicle (UAV) were used to derive various vegetative indices as well as canopy temperatures. A combination of aerial imaging and manual scoring showed that CSSL 100, CSSL 84, CSSL 111, and CSSL 15 had remarkably low tomato spotted wilt virus (TSWV) incidence, a devastating disease in South Georgia, USA. The four lines also performed well under leaf spot pressure. The vegetative indices showed strong correlations of up to 0.94 with visual disease scores, indicating that aerial phenotyping is a reliable way of selecting under disease pressure. Since the yield components of peanut are below the soil surface, we deployed ground penetrating radar (GPR) technology to detect pods non-destructively. Moderate correlations of up to 0.5 between pod weight and data acquired from GPR signals were observed. Both the manually acquired pod data and GPR variables highlighted the three lines, CSSL 84, CSSL 100, and CSSL 111, as the best-performing lines, with pod weights comparable to the cultivated check Tifguard. Through the combined application of manual and HTP techniques, this study reinforces the premise that chromosome segments from peanut wild relatives may be a potential source of valuable agronomic traits.

Time-Lapse Ground Penetrating Radar (GPR) Imaging of Used Engine Oil Contamination.

Time-Lapse Ground Penetrating Radar (GPR) was employed to study used engine oil (UEO) contamination of sandy environment in laboratory setting. GPR is a near-surface geophysical methods that uses electromagnetic fields to provide image of the dielectric properties of earth materials to detect structures and changes in materials properties within the subsurface. This research aimed to detect, monitor and map the migration of UEO contaminant in sand. The results of this study revealed that the migration of the UEO contamination with a homogeneously laid sand is non-uniform. UEO plumes were identified as high amplitude signals with enhanced reflectivity. There was a progressive decrease in GPR signal amplitudes (reflection reduction) within the contaminated area of the tank with time. This attenuation of GPR signal amplitudes was interpreted as caused by the evaporation of some portion of the UEO in the vadose zone as a result of temperature increase in time and due to the occurrence of UEO biodegradation. The time-lapse GPR proved to be an effective technique for detecting, monitoring, and mapping UEO migration within sand tank in laboratory setting.

Inversion of reclaimed soil water content based on a combination of multi-attributes of ground penetrating radar signals

Rapid, accurate, and non-destructive acquisition of the distribution of reclaimed soil moisture information can provide data for the rapid monitoring of reclaimed soil in areas experiencing coal mining subsidence. However, the water content inversion methods based on ground penetrating radar (GPR) are mostly single-attribute analysis methods, which are easily affected by the soil structure. This paper proposes a multi-attribute joint analysis method, which can reduce the influence of complex soil structure on the prediction results. Surveys and soil sampling using GPR were performed on a subsided reclamation area in Huaibei City, Anhui Province, China. Correlation analysis of GPR attribute information and volumetric water content (VWC) showed that frequency peak (FP), average envelope amplitude (AEA), energy, instantaneous amplitude area, instantaneous frequency area, and average instantaneous frequency values were significantly related to the VWC of reclaimed soil. The applicability of different single-attribute analysis methods under the condition of reclaimed soil was compared. Results showed that the predictive effects of FP and AEA attributes were better than other attributes. Generally, the single-attribute analysis method was greatly affected by the structure of reclaimed soil, so the accuracy and reliability of this method need to be further optimized. The prediction results of the single- and multi-attribute joint analysis methods in the structure of reclaimed soil were compared and analyzed. This showed that the prediction accuracy and model reliability of the multi-attribute method are both higher than those of the single-attribute method. The multi-attribute method can overcome the problem of insufficient accuracy of water content detection of ground-seeking radar in soil with a complex structure. Finally, the multi-attribute method was used to obtain the water distribution information in the reclamation area. Results of this study provide new methods and ideas for the prediction of water content by GPR in soils with complex structure.