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.

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.

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 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%.

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.

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.

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.

We analyze online gaming as a site of collaboration in a digital-physical hybrid. We ground our analysis in findings from an ethnographic study of the online game World of Warcraft in China. We examine the interplay of collaborative practices across the physical environment of China's Internet cafes and the virtual game space of World of Warcraft. Our findings suggest that it may be fruitful to broaden existing notions of physical-digital hybridity by considering the nuanced interplay between the digital and physical as a multi-dimensional environment or "ecology". We illustrate how socio-economics, government regulations and cultural value systems shaped a hybrid cultural ecology of online gaming in China.