Since the applications of the full-polarimetric ground penetrating radar (FP-GPR) technique have evolved from the detection of isolated targets to the interpretation of regional geologic structures, the study of the FP-GPR data fusion imaging method has become an urgent issue. Current data fusion methods for FP-GPR are all mathematical algorithm-based, aiming to obtain optimal weights for stacking. However, these stacking methods will produce wrong results if there are phase oppositions between different channels of FP-GPR data. Therefore, this article proposes a physical model-based fusion method called predictive rotation fusion (PRF). The new method aims to pursue special angles between the antenna and the surveying line for every scattering point in the subsurface, at which the amplitudes of acquired ground penetrating radar (GPR) data will be maximum. Besides, a new predictive filter is established and embedded into the PRF method to suppress the distortions and noises in the fusion results. The effectiveness of the proposed method is verified through laboratory experiments and its superiorities in fusion and denoising are also discussed by comparing it with a currently best weight-based fusion method. Finally, two real experiments are introduced to show the abilities of the PRF method in urban underground space imaging and field loess structure imaging.

Forests are vital components of the ecosystem of the Earth, regulating climate, conserving soil and water, and fostering biodiversity. Sustainable forest management offers crucial long-term benefits, but challenges such as disease threaten tree health. Disease-infected trees in both forests and urban areas pose risks to safety, property, and culture, leading to economic losses. Traditional inspection methods, such as resistance drilling, are invasive and laborious, while nondestructive techniques such as ground penetrating radar (GPR), offer promising alternatives. Although GPR shows potential, complexities such as tree structure and electromagnetic properties hinder accurate disease detection. In order to address these challenges, a new approach integrates structure-from-motion (SfM) photogrammetry with GPR measurement, and a novel holographic radar tomography processing approach has been proposed in this article. These methodologies accurately reconstruct tree trunks in 3-D, enabling precise GPR positioning and the obtaining of trunk permittivity. An arc-shaped Kirchhoff migration algorithm, moreover, helps mitigate irregular trunk shapes, enhancing data accuracy. The proposed framework demonstrates efficacy in real-tree measurement, offering high-precision disease monitoring. This innovation not only aids in preserving biodiversity but also enhances ecosystem services by promoting sustainable forest management. Effective disease monitoring ensures timely intervention, safeguarding valuable old trees and ecosystems while minimizing economic and cultural losses.

Fractures with fluid flow can lead to the damage of rock carving relics. During the detection of fractures, millimeter-scale fractures are usually difficult to determine due to their small apertures. Considering the rapid variation of water content in the fracture seepage zone can lead to anisotropy, this article proposes a new methodology to detect these millimeter-scale fractures with fluid flow using a time-lapse full-polarimetric ground penetrating radar (FP-GPR) scheme and an anisotropy analysis method. The time-lapse FP-GPR detection can monitor the water flow in the fracture and the infiltration in the rock, and the Freeman decomposition, H-Alpha decomposition, and a polarimetric phase (PP) feature are adopted to quantify and analyze the anisotropic effects over time. In the numerical test, we adopt hydrological modeling to build realistic dielectric models for time-lapse FP-GPR simulations. The results indicate that the variations of water contents and several polarimetric features, i.e., the surface-like scattering power, the double-bounce scattering power, and the averaged scattering angle, are consistent and are essentially related to the anisotropy of the seepage zone. Finally, we introduce the field tests performed at the experimental station of the Dazu Rock Carvings in Chongqing, China, which contain two cases I and II. Case I is an experiment on a surface fracture of a cliff, whereas case II is a detection test of a buried fracture. The results verify the effectiveness of the proposed methodology.

Elastic full-waveform inversion (EFWI) can retrieve multiple subsurface elastic parameters beyond the capabilities of the simple acoustic assumption. Compared to acoustic FWI (AFWI), EFWI faces complexities in dealing with multiple parameters and high nonlinearity in elastic inversion. Wavefield reconstruction inversion (WRI) was proposed to mitigate the cycle skipping and improve the computational efficiency of AFWI. WRI uses the wave equation as a regularization term for the objective of data fitting. By controlling the weight factor for the regularization term, the accuracy of the wave equation is relaxed and the data fitting term is enhanced. Thus, the cycle-skipping issue is reduced. WRI requires wavefield reconstruction in the calculation of the model gradient, which is the key step in WRI. The success of this wavefield reconstruction step highly depends on the accuracy of the source wavelet. In this paper, we propose an elastic WRI (EWRI) with source estimation (SE) method for multiple elastic parameters inversion. In the proposed method, we first reconstruct multicomponent wavefields (vertical and horizontal displacements) and estimate the source wavelet, simultaneously. Then, we formulate the elastic waveform inversion problem into a linear inversion system to mitigate the nonlinearity in EFWI. Applications on synthetic data generated from a modified Overthrust model and a Section of the Sigsbee2A model show the effectiveness of the proposed method in inverting P- and S-wave velocity models with an unknown source wavelet.

Cavity detection is vital to homeland security, civil engineering, and geological surveys. However, the limited target classification ability of traditional ground penetrating radar (GPR) may lead to its inability to identify cavities in complex environments with multiple anomalies, and the resolution of traditional GPR may not be enough to distinguish the internal structure of cavities. To address this issue, we have developed the dominant scattering mechanism analysis (DSMA) technique of full polarimetric GPR (FP-GPR). This technique calculates the scattering power of three physical models and determines the dominant scattering mechanism of subsurface targets. Cavity identification coefficients (CIC) are then utilized to enhance cavity detection and evaluation accuracy. Numerical simulation experiments have validated the workflow of this cutting-edge method. Scaled model experiments have depicted the polarization characteristics of the scattering field of typical tunnel structures within a laboratory setting. Field experiments have demonstrated that the novel technique can accurately identify and assess tunnels in complex environments with multiple anomalies, providing a foundation for determining regional function.

The Zhurong rover of the Chinese Tianwen-1 mission is equipped with a Mars rover penetrating radar (RoPeR). The high-frequency CH-2 antenna adopts a full-polarimetric (FP) acquisition mode to map the fine structure of the Martian regolith. However, the data from different polarimetric channels characterize different features of the subsurface. The accurate interpretation of regolith structure relies on a clear and comprehensive imaging result. This article proposes a new data fusion algorithm to achieve this goal, which is called the maximum likelihood method with optimal similarity constraints (ML-OSC). A unique loss function is constructed to guarantee the similarity between the fusion result and source radargrams and simultaneously consider the amplitude difference in different channel data. FP ground penetrating radar (FP-GPR) experiments on three typical targets are performed in the laboratory. The comparisons with three normal methods validate the effectiveness and superiority of the proposed method. Finally, the proposed method is applied to the field FP-RoPeR data fusion imaging. Several subsurface structures are analyzed in detail to verify its effectiveness in Martian regolith imaging. The proposed method provides new and complete imaging results of Martian regolith, which will lead to a more accurate interpretation of it in the future.

The polarimetric response of full-polarimetric ground penetrating radar (FP-GPR) from anisotropic media is a pressing issue to be investigated. It can improve the detection accuracy of the targets in the anisotropic media and can also provide effective ways to detect anisotropic targets. In this paper, we focus on the FP-GPR signals from uniaxial bi-anisotropic media, i.e. the complete case that both the permittivity and the conductivity of the background media are anisotropic. Based on the propagation characteristics of electromagnetic waves in the anisotropic media and the transmission coefficients on the surface of the media, we construct a mathematical relation between the measured scattering matrix affected by anisotropies and the real scattering matrix, derive a coefficient to characterize the polarization rotation(PR) effects, and proposed a diagonal matrix for target decomposition analyses. The computation results indicate that εz/εx, σz/σx, and antenna interval have complex effects on the PR coefficient and the H-Alpha decomposition results of three types of typical targets. Multiple 3D FP-GPR simulations on a series of uniaxial bi-anisotropic models verified the results.

Diffracted waves on ground penetrating radar (GPR) radargrams will severely affect the positioning of subsurface anomalies and stratigraphic division of subsurface. I this study, we proposed an f-x domain VMD dip filter to suppress the diffracted waves on GPR radargrams. A simple model and complex model tests are performed. The results indicate that the f-x domain VMD dip filter can suppress the interference of diffraction well. The radargrams after processing can highlight the reflected signals generated by the surface of anomalies and subsurface strata, which is beneficial to further object positioning and stratigraphic division.

China’s first Mars probe Tianwen-1 has successfully landed on the southern Utopia Planitia of Mars on May 15, 2021. The Zhurong rover is first equipped with a full-polarimetric Mars Rover Penetrating Radar (FP-RoPeR) system, aiming to map the subsurface fine structure and to find the potential underground water ice. However, different from the previous water ice detection of orbital radar, the FP-RoPeR signals will be affected by the induced field rotation (IFR) if electromagnetic (EM) waves propagate through rough interfaces. Therefore, in this article, we assess the IFR effects from rough interfaces on the circular polarization ratio (CPR) response of FP-RoPeR data, which is a significant parameter for water ice detection. The theoretical computation and numerical validation indicate that the depth, the number of rough interfaces, and relative permittivity are three vital parameters that affect the IFR effects; depth plays a more important role than the other two for FP-RoPeR system. The CPR estimation result will be with greater error in the shallow region (0–1 m). The relative error in the region of depth greater than 1 m can be guaranteed to be under 10%.

The influence of rocks on the microwave thermal emission (MTE) of the lunar regolith has not been fully studied with the four-channel microwave radiometer (MRM) data onboard Chang’e-1/2 satellites. To highlight the influence of the rocks on the MTE of the regolith, the Hertzsprung basin located near the lunar equator in highland regions is selected as the study area. The comparison between the brightness temperature (TB) maps derived from the Chang’e-2 MRM data and rock abundance (RA) map derived from the Diviner data postulates three special issues about the correlation between the MTE features and the regolith with rocks. Then, aimed to interpret the issues, two new layered regolith models and the corresponding radiative transfer models are constructed. The main results are as follows. First, the observation and the simulation both verify that the regolith with rocks will provide a cold TB anomaly at night and at low frequencies at daytime, but result in a hot anomaly at high-frequency at daytime. Moreover, the temperature profiles of the regolith with surface and hidden rocks are evaluated with the theoretical model. Second, the simulation results verify the existence of the hidden rocks in the lunar regolith assumed when studying the TB performances of the Hertzsprung basin. Third, the rock distribution revealed by the TB maps shows a different view compared to that estimated by the Diviner data in space and values, and the change of the TB with frequencies postulates a new view about the variation of the RA with depth. This study hints that the MRM data probably provide a new way to quantitatively estimate the RA values of the lunar regolith, and the results will be meaningful to improve understanding of the evolution of the impact craters.

Buried craters within tens of meters of lunar regolith are rarely studied but are significant for understanding the evolution of surface processes on the Moon. Here, we first report the evidence of an intact buried crater within the layered strata at Chang’E-4 (CE-4) landing site revealed by the lunar penetrating radar (LPR). The time–frequency comparative analysis method based on the variational mode decomposition (VMD) and the rock quantitative analysis method based on the local unit correlation (LUC) are proposed and applied to the processing and analysis of LPR data within 15 lunar days. The results presented by the two methods provide evidence of a buried crater at the CE-4 landing site and simultaneously reveal the rock-concentrated structure within the buried crater. According to the results, it is considered that the filling materials within the buried crater have survived the impaction and gardening during the formation of the overlying fine-grained regolith. Recent works have proposed that the near-surface material at the CE-4 landing site is mainly the lunar mantle materials excavated from the nearby Finsen crater. Therefore, the buried crater probably preserves the initial lunar mantle materials.

China’s Tianwen-1 probe carrying the Zhurong rover has successfully landed on the southern Utopia Planitia of Mars. The Zhurong rover is first equipped with a full-polarimetric Mars Rover Penetrating Radar (FP-RoPeR) system, aiming to map the fine structure and potential water-ice distribution of Martian regolith. However, the ground experiment on earth indicates that the RoPeR data is severely affected by noises, clutters, and signal misalignment. More importantly, an imbalance between the data from two cross-polarized channels is observed which is considered to be the interference of the rover. The interference prevents the accurate assessment and analysis of field FP-RoPeR data and may even become the traps in the interpretations of future radar data from Mars. In this article, we firstly analyze the interference in detail and achieve the suppressions of noises, clutters, and signal misalignment; subsequently, a polarized orientation calibration method is proposed to calibrate the imbalance of cross-polarized channels. Finally, we introduce a field experiment at the Ulanhada volcanic geopark in Inner Mongolia Autonomous Region, China. Based on the field RoPeR data and the proposed processing methods, we propose three types of data processing strategies for different aims and present how to use field FP-RoPeR data to analyze subsurface structures and properties.

The full-polarimetric ground-penetrating radar (FP-GPR) can obtain the polarimetric attributes of targets and achieve more accurate identification compared with traditional GPRs. However, in most cases, the polarimetric signals collected by GPRs are not only from the targets but also from the ground surface. According to the classical Fresnel formulas, the polarized directions of the waves will change after transmissions due to the difference in the transmission coefficients between horizontally and vertically polarized waves. This effect, called induced field rotation (IFR), will interfere with the acquisition of polarimetric attributes and also exists in the field measurements on a rough surface. In this study, we have derived the association between the measured FP-GPR data and transmission coefficients of the rough surface provided by the small perturbation method (SPM). The effects of IFRs from the rough surface on H-Alpha decomposition are analyzed later. The results show that the parameters of the rough surface will affect the values of components in the scattering matrix, but will not change the matrix and H-Alpha decomposition result; the incident angle and relative permittivity play main roles; for the H-Alpha decomposition results of three typical targets, the flat and dihedral are little influenced by IFRs, but the cylinder is seriously affected; simultaneously, a template for discriminating whether the calibration for H-Alpha decomposition is necessary is established. Both numerical and experimental tests validate the conclusions. Finally, a novel application strategy of H-Alpha decomposition is proposed, which has taken IFR from the rough surface into considerations.

Although fully polarimetric analysis techniques have been applied to remote sensing radar for charactering different surface scattering properties using, these techniques have not yet been widely adopted for Ground Penetrating Radar (GPR) applications. In 2015 we applied the “H-alpha decomposition” technique to polarimetric GPR data for classifying buried metallic targets such as wire branches, a sphere, a plate, and a dihedral. The H-alpha decomposition can separate scatters into four different types according to different depolarization effects: linear polarization, random depolarization (branches, roots, random media), no depolarization (sphere, plates, and horizontal layers), and 90 degrees depolarization (corner reflectors). In this paper, we explore the utilization of H-alpha decomposition method in clutter reduction and linear target detection during the preprocessing stage so that automatic linear target detection can be achieved in poor signal-to-clutter ratio (SCR) and signal-to-noise ratio (SNR) environments.

Polarimetric technology has been one of the most important advances in microwave remote sensing during recent decades. H-alpha decomposition, which is a type of polarimetric analysis technique, has been common for terrain and land-use classification in polarimetric synthetic aperture radar. However, the technique has been less common in the ground penetrating radar (GPR) community. In this paper, we apply the H-alpha decomposition to analyze the surface GPR data to obtain polarimetric attributes for subsurface target classification. Also, by combining H-alpha decomposition and migration, we can obtain a subsurface H-alpha color-coded reconstructed target image, from which we can use both the polarimetric attributes and the geometrical features of the subsurface targets to enhance the ability of subsurface target classification of surface GPR. A 3-D full polarimetric GPR data set was acquired in a laboratory experiment, in which four targets, a scatterer with many branches, a ball, a plate, and a dihedral scatter, were buried in dry sand under flat ground surface, and used to test these techniques. As results, we obtained the subsurface H-alpha distribution and classified the subsurface targets. Also, we derived a subsurface H-alpha color-coded reconstructed target image and identified all four targets in the laboratory experiment.

In this paper, we combine the multiple-input-multiple-output (MIMO) array antenna technology with a multipolarization component in a ground penetrating radar (GPR) system to improve target detection accuracy. The MIMO technology introduced in previous literature is widely applied in radar and other wireless communication fields. Here, we apply the MIMO technology with a “plane-wave like” (PWL) source that uses array antennas with small spacing to emit a pulse source at the same time in GPR detection. First, we analyze the physical mechanism of the MIMO GPR system with a “PWL” source to improve the target detection resolution. Then, we carry out a numerical simulation with a finite-difference time-domain method in 1-D and 2-D array antennas to compare the imaging results of the MIMO and traditional GPR systems. Finally, the synthetic data MIMO GPR experiment with a step-frequency GPR system is implemented. Compared with the traditional GPR system, our results demonstrate that the MIMO GPR system with a multipolarization detection mode can overcome the influence of target radar cross sections and antenna radiation directions, and improve target detection accuracy effectively. Meanwhile, the synthetic MIMO GPR system also provides a good idea to improve the system performance and reduce system design requirements and the manufacture cost.

Full-polarimetric Ground-penetrating radar (GPR) is considered as a promising sensor for detecting buried targets. However, the polarimetric decomposition technique plays a crucial role in identifying and classifying targets which are buried in the sand under the surface. The decomposition techniques of full-polarimetric Ground-penetrating radar includes four decomposition methods, namely: (1) Pauli decomposition method, (2) H-α decomposition method, (3) Freeman decomposition method and (4) polarimetric anisotropy analysis method .This paper mainly applys Freeman decomposition method to recognition of metal surface plate, dihedral and metal ball. The potential of polarimetric target decomposition techniques to metal surface plate, dihedral and metal ball characterization and classification is shown which provides valuable information.

Handheld ground-penetrating radar (GPR) system is one of a number of technologies that has been researched as a means of improving landmine detection efficiency. However, as the measurement points are random and data are irregular for the human operator, it is difficult to display subsurface visualization imaging. Also detection of buried landmines by GPR normally suffers from very strong clutter that will decrease the image quality. To solve the problem, a modified migration algorithm was proposed to process irregular GPR data, which has both the advantage of migration that can improve signal-clutter ratio and the advantage of interpolation that produces the grid data set for visualization. An application to field data acquired in Afghanistan shows clear landmine image in both vertical profile and horizontal slice.

Polarimetric GPR requires accurate calibration of channel imbalance and crosstalk not only in the amplitude term but also in the phase term. Currently, there have some calibration techniques. Though these techniques are very easy to perform, they provide less accurate calibration results for the crosstalk. To improve on the accuracy of calibration, we have developed a mathematical formulation to calibrate polarimetric GPR data. We measured several scattering matrices to obtain the necessary calibration parameters. The calibration technique was tested from measurements conducted on dihedral corner reflector.

A multisensor system could offer an effective solution to land-mine detection. A handheld dual-sensor system with a position tracking system is developed, which can acquire ground-penetrating radar (GPR) and metal detector (MD) data with 2-D space coordinates. However, as the measurement points are random and data are irregular for the human operator, it is difficult to display subsurface visualization imaging. We develop a set of techniques to achieve visualization imaging of both GPR and MD. A modified migration algorithm with continuous-root-mean-square velocity is proposed to process irregular GPR data, and an interpolation algorithm is recommended to process irregular MD data. In an application to field experiment data, clear subsurface targets imaging was achieved in both GPR slice and MD image.

Generally GPR transmits and receives radio waves with a single polarization using two parallel antennas. But it is possible to improve the GPR ability of discrimination and imaging of subsurface targets by analyzing the backscattered wave with a variety of polarizations. So we are developing a full-polarimetric GPR system, including PC, network analyzer, rectangular coordinates robot, switch driver, and polarimetric antenna array. Polarimetric antenna array is used to transmit and receive both co-polarimetric and cross-polarimetric signals. Currently polarimetric GPR do not execute precise calibration. But good calibration can improve the classification ability of subsurface targets. So we introduced the calibration technique into the polarimetric GPR, and derived a calibration formula.

We have conducted GPR survey at a site which was partly excavated and filled with highly contaminated soils. The electrical properties and TPH concentration of the core samples were measured in the laboratory. It is verified that an inverse relation between TPH concentration and relative dielectric constant, and a direct proportional correlation between TPH concentration and electrical resistivity. LNAPL contamination area is illustrated by GPR data which shows the decreased radar signal amplitude.

Migration is an important signal processing method that can improve signal-clutter ratio and reconstruct subsurface image. Diffraction stacking migration and Kirchhoff migration sum amplitudes along the migration trajectory, which generally is hyperbolic. But when the ground surface varies acutely, the migration trajectory is not hyperbolic. To computer the migration trajectory need the technique of ray tracing. We introduce a method of ray tracing based on 3D velocity model. Firstly, we build the 3D velocity model depending on the estimation of both ground surface topography and velocities. Then we compute the travel time between transmitter, receiver and each subsurface scattering point, and search the propagation ray depending on the Fermat's principle. The method is tested by an experiment data acquired by the stepped-frequency (SF) CMP antenna GPR system. The target is a metal ball that is buried under a sand mound. A nice result of ray tracing is shown in the case.

It is often advantageous to estimate the ground surface topography from radar returns. However, the popular method, searching for the brightest pixel in the ground-penetrating radar profile, cannot achieve accurate surface topography in the sharp variable surface case because of the effects of diffraction waves. In this letter, we propose a method to solve the problem and improve the accuracy of surface topography. A migration technique is introduced to refocus the diffraction waves before searching for the brightest pixel. Experimental data have been used to display the effects of diffraction waves and test the method. The result shows that the method can dramatically estimate accurate surface topography even in the sharp variable surface area.

Ground-penetrating radar (GPR) is recognized as a promising sensor for detecting buried landmines. In this case, the GPR antenna(s) must be elevated above the ground. However, this requirement results in heavy surface clutter. It is therefore necessary to overcome the effect. A commonly used procedure of time gating and background averaging cannot suit to small shallow nonmetallic landmine beneath a rough ground surface. In this letter, we proposed techniques to enhance the target signal through common midpoint (CMP) antenna array and data processing techniques, including velocity spectrum and CMP multifold stacking. The method has been tested using experiment data over a rough ground under which small plastic antipersonnel landmines is shallowly buried. The result shows the signal-to-clutter ratio was dramatically improved.

We are developing a new landmine detection system, namely advanced landmine imaging system (ALIS), which is equipped with metal detector (MD) and ground penetrating radar (GPR). Despite it is a hand-held system, we can record the MD and GPR signals together with the sensor position information acquired by CCD camera. Therefore, MD image and GPR image can be obtained after signal processing. Since ALIS is a hand-held system, the sensor position is random when it is operated in the field. So interpolation processing is needed to provide a gridded data set for both MD and GPR. A good MD image can be achieved after interpolation. Also, interpolation can prepare the gridded data set for migration. 3D Kirchhoff migration is used to enhance the signal-clutter ratio for effective image reconstruction. The ALIS was tested in Afghanistan in December 2004 and showed a good performance. In particular, the GPR could obtain a good image of anti-personnel (AP) mine buried at more than 20 cm depth. Also images from both sensors could be combined to distinguish a mine from a metal fragment.