Recursive impedance inversion of ground-penetrating radar data in stochastic media

The travel time and amplitude of ground-penetrating radar （GPR） waves are closely related to medium parameters such as water content, porosity, and dielectric permittivity. However, conventional estimation methods, which are mostly based on wave velocity, are not suitable for real complex media because of limited resolution. Impedance inversion uses the reflection coefficient of radar waves to directly calculate GPR impedance and other parameters of subsurface media. We construct a 3D multiscale stochastic medium model and use the mixed Gaussian and exponential autocorrelation function to describe the distribution of parameters in real subsurface media. We introduce an elliptical Gaussian function to describe local random anomalies. The tapering function is also introduced to reduce calculation errors caused by the numerical simulation of discrete grids. We derive the impedance inversion workflow and test the calculation precision in complex media. Finally, we use impedance inversion to process GPR field data in a polluted site in Mongolia. The inversion results were constrained using borehole data and validated by resistivity data.

Exploring Soil Layers and Water Tables with Ground-Penetrating Radar

Ground-penetrating radar (GPR) has been used predominantly for environments with low electrical conductivity like freshwater aquifers, glaciers, or dry sandy soils. The objective of the present study was to explore its application for mapping in subsurface agricultural soils to a depth of several meters. For a loamy sand and a clayey site on the North China Plain, clay inclusions in the sand were detected; the thickness, inclination, and continuity of the confining clay and silt layers was assessed; and a local water table was mapped. Direct sampling (soil coring and profiling) in the top meter and independent measurement of the water table were utilized to confirm the findings. Also, effective estimates of the dielectric number for the site with the dielectric number of moist clayey soils depending strongly on frequency were obtained. Thus, important properties of soils, like the arrangement and type of layers and in particular their continuity and inclination, could be explored with moderate efforts for rather large areas to help find optimal locations for the time-consuming and expensive measurements which would be necessary to detail a model of the subsurface.

Reverse-Time Migration from Rugged Topography to Image Ground-Penetrating Radar Data in Complex Environments

In ground-penetrating radar （GPR） imaging, it is common for the depth of investigation to be on the same order as the variability in surface topography, In such cases, migration fails when it is carried out from a datum after the application of elevation statics, We introduce a reverse-time migration （RTM） algorithm based on the second-order decoupled form of Maxwell＇s equations, which requires computation of only the electric field, The wavefield extrapolation is computed directly from the acquisition surface without the need for datuming, In a synthetic case study, the algorithm significantly improves image accuracy over a processing sequence in which migration is performed after elevation statics, In addition, we acquired a field dataset at the Coral Pink Sand Dunes （CPSD） in Utah, USA, The data were acquired over rugged topography and have the complex internal stratigraphy of multiply eroded, modern, and ancient eolian deposits, The RTM algorithm significantly improves radar depth images in this challenging environment,

Englacial hydrological characteristics of a typical continental-type glacier in China as detected by ground-penetrating radar

The englacial structures and ice thickness of the Laohugou No. 12 （L12） Glacier in the Qilian Mountains, China, were retrieved from ground-penetrating radar （GPR） profile dzta acquired in August of 2007. Here the interpretation of a typical GPR image is validated using two-dimensional, Finite-Difference Time-Domain （FDTD） numerical modeling. Data analyses revealed many en- glacial characteristics, such as temperate ice, crevasses, and cavities at the position of convergence between the eastern and west- ern glacial branches of L12, and at an altitude between 4,600 and 4,750 m a.s.1, on the east branch. Combining ice thickness, en- glacial structures, subglacial topography, and surface flow velocities of this glacier, we analyzed the reasons for the distribution of temperate ice. The results show that greater englacial water content is associated with englacial crevassing and surface moulins, which allow water to be channeled to the temperate ice aquifer beneath the surface cold ice layer. Analysis of air temperature data shows that as more meltwater imports into the ice body, this has a great effect on water conservation and dynamics conditions. With climate warming, and under the influence of crevasses, subglacial structures, and ice thickness, ice thickness reduction on the L12 east branch is more rapid than that on the west branch.

Modified Method for Estimating Organic Carbon Density in Discontinuous Karst Soil Using Ground-Penetrating Radar and Geostatistics

The conventional method which assumes the soil distribution is continuous was unsuitable for estimating soil organic carbon density(SOCD) in Karst areas because of its discontinuous soil distribution. The accurate estimation of SOCD in Karst areas is essential for carbon sequestration assessment in China. In this study, a modified method,which considers the vertical proportion of soil area in the profile when calculating the SOCD, was developed to estimate the SOCD in a typical Karst peak-cluster depression area in southwest China. In the modified method, ground-penetrating radar(GPR) technology was used to detect the distribution and thickness of soil. The accuracy of the method was confirmed through comparison with the data obtained using a validation method, in which the soil thickness was measured by excavation. In comparison with the conventional method and average-soil-depth method,the SOCD estimated using the GPR method showed the minimum relative error with respect to that obtained using the validation method. At a regional scale, the average SOCDs at depths of 0-20 cm and 0-100 cm, which were interpolated by ordinary kriging,were 1.49(ranging from 0.03-5.65) and 2.26(0.09-11.60) kgm-2based on GPR method in our study area(covering 393.6 hm2), respectively. Therefore, the modified method can be applied on the accurate estimation of SOCD in discontinuous soil areas such as Karst regions.

An Algorithm for Estimating the Object Depth Accurately by Ground-Penetrating Radar

Ground penetrating radar (GPR) is a remote sensing technique used to obtain information on subsurface features from data collected over the surface. We propose an automatic algorithm for estimating object depth using f-k migration and velocity scanning methods in a homogeneous medium. To improve the accuracy of the algorithm, the formula used to calculate the GPR valid lateral aperture is also presented. Experimental results show that the relative estimating error of depth is as low as 5% in a homogeneous medium.

Modeling tree root diameter and biomass by ground-penetrating radar

Roots play a key role in ecosystem functioning as they transfer water and nutrients from soil to plants. Traditional methods for measuring roots are labor-intensive and destructive in nature, which limits quantitative and repeatable assessments in long- term research. Ground-penetrating radar （GPR） provides a non-destructive method to measure plant roots. Based on the superiority of GPR with 2 GHz frequency, we developed a new, practical method to estimate root biomass. First, average root matter density was measured by collecting a small number of root samples. Second, under controlled, experimental conditions in a sandy area, a root diameter estimation model base on GPR was developed from which root diameter was estimated. Third, root volume was calculated using the estimated root diameter and assuming the shape of roots to be cylindrical. Finally, root biomass was estimated by averaging root matter density and root volume. Results of this study suggest the following： （1） the density of coarse roots with diameters greater than 0.5 cm is relatively uniform; （2） a new wave shape parameter, AT, extracted from profile data of 2 GHz frequency antenna is independent of root depth, thus enabling the construction of a root diameter estimation model with high accuracy; and （3） results of a field experiment demonstrated the GPR-based method to be feasible and effective in estimating biomass of coarse roots. These findings are helpful for improving GPR-based root diameter and biomass estimation models and suggest the potential of GPR data in studying root systems.

Coarse root spatial distribution determined using a ground-penetrating radar technique in a subtropical evergreen broad-leaved forest,China

Coarse roots play a critical role in forest ecosystems and both abiotic and biotic factors affect their spatial distribution.To some extent,coarse root density may reflect the quantity of root biomass and biotic competition in forests.However,using traditional methods(e.g.,excavation)to study coarse roots is challenging,because those methods are time-consuming and laborious.Furthermore,these destructive methods cannot be repeated in the same forests.Therefore,the discovery of non-destructive methods for root studies will be very significant.In this study,we used a ground-penetrating radar technique to detect the coarse root density of three habitats(ridge,slope and valley)and the dominant tree species(Castanopsis eyrei and Schima superba)in a subtropical forest.We found that(i)the mean of coarse root density for these three habitats was 88.04roots m–2,with roots being mainly distributed at depths of 0–40 cm.Coarse root densities were lower in deeper soils and in areas far from the trunk.(ii)Coarse root densities differed significantly among the three habitats studied here with slope habitat having the lowest coarse root density.Compared with S.superba,C.eyrei had more roots distributed in deeper soils.Furthermore,coarse roots with a diameter>3 cm occurred more frequently in the valleys,compared with root densities in ridge and slope habitats,and most coarse roots occurred at soil depths of 20–40 cm.(iii)The coarse root density correlated negatively with tree species richness at soil depths of 40–60 cm.The abundances of the dominant species,such as C.eyrei,Cyclobalanopsis glauca,Pinus massoniana,had significant impacts on coarse root density.(iv)The soil depth of 0–40 cm was the"basic distribution layer"for coarse roots since the majority of coarse roots were found in this soil layer with an average root density of 84.18 roots m–2,which had no significant linear relationships with topography,tree species richness,rarefied tree species richness and tree density.Significant relationships between coarse root density and these factors were found at the soil depth of40–60 cm,which was the"potential distribution layer"for coarse root distribution.

Automatic modelling of urban subsurface with ground-penetrating radar using multi-agent classification method

The subsurface of urban cities is becoming increasingly congested.In-time records of subsur-face structures are of vital importance for the maintenance and management of urban infrastructure beneath or above the ground.Ground-penetrating radar(GPR)is a nondestructive testing method that can survey and image the subsurface without excava-tion.However,the interpretation of GPR relies on the operator’s experience.An automatic workflow was proposed for recognizing and classifying subsurface structures with GPR using computer vision and machine learning techniques.The workflow comprises three stages:first,full-cover GPR measurements are processed to form the C-scans;second,the abnormal areas are extracted from the full-cover C-scans with coefficient of variation-active contour model(CV-ACM);finally,the extracted segments are recognized and classified from the corresponding B-scans with aggregate channel feature(ACF)to produce a semantic map.The selected computer vision methods were validated by a controlled test in the laboratory,and the entire workflow was evaluated with a real,on-site case study.The results of the controlled and on-site case were both promising.This study establishes the necessity of a full-cover 3D GPR survey,illustrating the feasibility of integrating advanced computer vision techniques to analyze a large amount of 3D GPR survey data,and paves the way for automating subsurface modeling with GPR.

Estimation of glacier ice storage in western China constrained by field ground-penetrating Radar surveys

Recent decades have witnessed the accelerated retreat of alpine glaciers,which likely portends a decline in the glacial ice melt on which large downstream populations rely for freshwater.Thus,estimating water storage in alpine glaciers is critical for predicting the trend of glacier melting.This study compiled a rich set of ice thickness observations for glaciers of varying sizes in western China.We here presented a first-order assessment of the various errors involved in interpreting ice thickness from ground-penetrating radar(GPR)observations.An empirical Bayesian kriging(EBK)method was used for ice thickness interpolation and volume estimation.We then established a new volume-scaling law specific to western China glaciers based on these 36 volume-area pairs.And together with the Second Chinese Glacier Inventory(SCGI),we obtain a regional volume estimate of 4451±298 km^(3).By comparison,we believed that this result is the most reliable estimate of the total ice storage in western China and more reliably predicts gross glacier melting.However,our results show that the method of glacier division can strongly affect the total volume estimation,which previous studies ignored.This emphasizes the need for more surveyed glaciers data and more accurate glacier inventory to improve the evaluation of the climate impact on glacier melting water resources and to help ensure the future survival of these alpine glaciers.

FDTD analysis of ground-penetrating radar antennas with shields and absorbers

One of the most critical hardware components of a ground-penetrating radar(GPR)is the antenna system.Important parameters of antennas,such as antenna bandwidth,radiation waveform and cross coupling determine the GPR system performance.The modified TEM horn antenna with distributed resistor load is presented in this paper,and the radiation properties of the antenna with the shields and absorbers are studied through the three-dimensional finite-difference time-domain(FDTD)scheme.Simulations show that the direct signal coupled from the transmitter is decreased by means of the shields and absorbers.Therefore,using the antenna in the GPR system can improve the signal-to-clutter ratio and the dynamic range of the system.

Measurement of soil water content using ground-penetrating radar: a review of current methods

Soil water content(SWC)is a crucial parameter in ecology,agriculture,hydrology,and engineering studies.Research on non-invasive monitoring of SWC has been a long-lasting topic in these fields.Ground penetrating radar(GPR),a non-destructive geophysical technique,has the advantages of high resolution,deep detection depth,and high efficiency in SWC measurements at medium scale.It has been successfully applied in field investigations.This paper summarizes the recent progress in developing GPR-based SWC measurement methods,including reflected wave,ground wave,surface reflection,borehole GPR,full waveform inversion,average envelope amplitude,and frequency shift methods.The principles,advantages,limitations,and applications of these methods are described in detail.A comprehensive technical framework,which comprises the seven methods,is proposed to understand their principles and applicability.Two key procedures,namely,data acquisition and data processing,are emphasized as crucial to method applications.The suitable methods that will satisfy diverse application demands and field conditions are recommended.Future development,potential applications,and advances in hardware and data processing techniques are also highlighted.

Ground-penetrating radar study of beach-ridge deposits in Huangqihai Lake, North China： the imprint of washover processes

Determining the origin of beach ridges in lacustrine basins can often be problematic. The sedimen- tary processes responsible for formation of beach ridges on the north shore of Huangqihai Lake were investigated by using ground penetrating radar （GPR）. A 400 MHz GPR antenna was used to achieve a high vertical resolution of 0.044）.08 m. The radar stratigraphy was then determined using principles of seismic stratigraphy. The radar facies （RF） were determined by analyzing internal configuration and continuity of reflections, as well as reflection termination patterns. The identified RF fall into three groups （inclined, horizontal and irregular）. The inclined group consists of RF that display inclined reflections. The horizontal group consists of RF that exhibit predominantly horizontal reflections. In the irregular group, the reflections are typically weak. RF with reflections with gently landward dips in the shore-normal profile are interpreted as washover sheet deposits. RF with steeply landward-dipping and imbricated reflections are interpreted as washover lobes. Washover sheets develop when overwash fails to enter a significant body of water and sedimentation takes place entirely on the relatively flattened topography. Washover lobe development occurs when overwash enters a region in which topography dips steeply landward, and sedimentation takes place on the surface of washover sheets or previous washover lobes. The beach-ridge deposits are interpreted as being formed entirely from vertically and laterally stacked washover sheets and washover lobes. They were formed by wave-dominated processes and secondary overwash processes supplemented by longshore currents.

Co-Sharing Waveform Design for Millimeter-Wave Radar Communication Systems

Millimeter-wave(mmWave)radar communication has emerged as an important technique for future wireless systems.However,the interference between the radar signal and communication data is the main issue that should be considered for the joint radar communication system.In this paper,a co-sharing waveform(CSW)is proposed to achieve communication and radar sensing simultaneously.To eliminate the co-interference between the communication and sensing signal,signal splitting and processing methods for communication data demodulation and radar signal processing are given respectively.Simulation results show that the bit error rate(BER)of CSW is close to that of the pure communication waveform.Moreover,the proposed CSW can achieve better performance than the existing waveforms in terms of range and velocity estimation.

Analysis of the joint detection capability of the SMILE satellite and EISCAT-3D radar

The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research.

A target parameter estimation method via atom-reconstruction in radar mainlobe jamming

Mainlobe jamming(MLJ)brings a big challenge for radar target detection,tracking,and identification.The suppression of MLJ is a hard task and an open problem in the electronic counter-counter measures(ECCM)field.Target parameters and target direction estimation is difficult in radar MLJ.A target parameter estimation method via atom-reconstruction in radar MLJ is proposed in this paper.The proposed method can suppress the MLJ and simultaneously provide high estimation accuracy of target range and angle.Precisely,the eigen-projection matrix processing(EMP)algorithm is adopted to suppress the MLJ,and the target range is estimated effectively through the beamforming and pulse compression.Then the target angle can be effectively estimated by the atom-reconstruction method.Without any prior knowledge,the MLJ can be canceled,and the angle estimation accuracy is well preserved.Furthermore,the proposed method does not have strict requirement for radar array construction,and it can be applied for linear array and planar array.Moreover,the proposed method can effectively estimate the target azimuth and elevation simultaneously when the target azimuth(or elevation)equals to the jamming azimuth(or elevation),because the MLJ is suppressed in spatial plane dimension.

Overview of radar detection methods for low altitude targets in marine environments

In this paper,a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented,focusing on the challenges posed by sea clutter and multipath scattering.The performance of the radar detection methods under sea clutter,multipath,and combined conditions is categorized and summarized,and future research directions are outlined to enhance radar detection performance for low-altitude targets in maritime environments.

Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar

Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental perception.However,existing algorithms based on discrete Fourier transform(DFT),multiple signal classification(MUSIC)and compressed sensing,etc.,cannot achieve both low complexity and high resolution simultaneously.This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar.Firstly,we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy.Secondly,we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data,which improves the efficiency of continuous target tracking.These two approaches have improved the algorithm efficiency by nearly 2-4 orders of magnitude without losing accuracy and resolution.Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.

Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

Probabilistic modeling of multifunction radars with autoregressive kernel mixture network

The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative applications.This paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains.Simulation results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.