For water dynamics investigation in unsaturated (vadose) zones, ground penetrating radar is a popular hydro-geophysical method because it is non-invasive for soil, has high resolution and the results have a direct link with water content. Soil water content and soil hydraulic properties are two key factors for describing the water dynamics in vadose zones. There has been tremendous progress in soil water content and soil hydraulic properties estimation with ground penetrating radar. The purpose of this paper is to provide an overview of the application of ground penetrating radar for soil water dynamics studies. This paper first summarizes various methods for the determination of soil water content. including traditional methods in the surveys of surface ground penetrating radar, borehole ground penetrating radar, and off-ground ground penetrating radar, as well as relatively new methods, such as full waveform inversion, the average envelope amplitude method, and the frequency shift method. This paper further provides a review for estimating soil hydraulic properties with GPR according to the types of ground penetrating radar data. We hope that this review can provide a reference for the application of ground penetrating radar in soil water dynamics studies in the future.

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 paper 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.

The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.

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.

Soil water content (SWC) estimation is important for many areas including hydrology, agriculture, soil science, and environmental science. Ground penetrating radar (GPR) is a promising geophysical method for SWC estimation. However, at present, most of the studies are based on partial information of GPR, like travel time or amplitude information, to invert the SWC. Full waveform inversion (FWI) can use the information of the entire waveform, which can improve the accuracy of parameter estimation. This study proposes a novel SWC estimation scheme by using the FWI of GPR, optimized by the grey wolf optimizer (GWO) algorithm. The proposed scheme includes a petrophysical relationship to link the SWC with the relative dielectric permittivity, 1D GPR forward modeling, and a GWO optimization algorithm. First, numerical modeling was carried out, and the proposed scheme was applied to both noise‐free and noisy data to verify its applicability. Then, the proposed method was applied to data collected from a field experimental site. These results, derived from both synthetic and real datasets, show that the proposed inversion scheme resulted in a good match between the observed and calculated GPR data. In the numerical modeling, it was observed that the SWC could be inverted accurately, even when noise was present in the data. These demonstrate that the GWO method can be applied for the quantitative interpretation of GPR data. The proposed scheme shows potential for SWC estimation by using GPR full waveform data.