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.

The early Mars might have been covered by a northern hemispheric ocean. China's Mars rover "Zhurong" has begun its explorations near the putative ancient shorelines on Martian Utopia Planitia since 2021. The Mars rover penetrating radar (RoPeR) is one of the key payloads of the "Zhurong" rover, which aims at revealing the Martian underground structure. This paper proposes a data analysis method using a 2D sliding window is proposed to extract the valid data of the RoPeR low-frequency channel. Besides, a data processing scheme, as well as a modified hyperbolic fitting method, are proposed to obtain high-quality radargram and permittivity distribution, respectively. The results show that numerous dipping reflections are observed in the radar profile in a depth range of 10~30 m, corresponding to the reflection characteristics of coastal sediment on Earth. These may have been formed by the sedimentation of ancient ocean waves on the foreshore and were buried by later geological processes, suggesting the existence of a potential ancient coastline.

Cavities beneath urban roads pose a growing threat to traffic safety, mainly due to leakage from subsurface pipelines. Ground Penetrating Radar (GPR) and Pipe Penetrating Radar (PPR) have become widely adopted tools for the detection of cavities. However, a notable limitation of both GPR and PPR lies in their inability to clearly delineate the top and bottom of cavities. This letter introduces a collaborative detection technique that employs both GPR and PPR. Subsequently, a collaborative imaging method is proposed, derived separately from GPR and PPR data, utilizing a Reverse Time Migration (RTM) algorithm with a zero-lag cross-correlation imaging condition. Laboratory experimental results show that the artefacts caused by the RTM are significantly suppressed through the application of cross-correlation imaging. As such, the proposed technique allows clear imaging of both the top and bottom of cavities around a pipeline. It is concluded that the proposed technique can enhance the capability of GPR in detection and characterization of subsurface cavities.

The reverse time migration (RTM) algorithm is widely recognized in ground-penetrating radar (GPR) imaging for its high-resolution capabilities. However, the algorithm involves multiple forward modeling making it computationally intensive and less efficient. This article presents a workflow designed to enhance computational efficiency while maintaining the accuracy of RTM imaging. This purpose is achieved by implementing a source encoding strategy that integrates random polarity and time shifts to build a supergather as a new independent excitation source. This approach aims to suppress the crosstalk artifact among integrated excitation sources within the supergather during wave propagation, which could otherwise impact imaging accuracy. Subsequently, by integrating the feature pyramid attention network (FPANet) to further suppress residual multisource crosstalk artifact, thereby enhancing the overall imaging quality of RTM. Evaluations on synthetic GPR data demonstrate the algorithm’s capability to improve computational efficiency without sacrificing imaging accuracy, thereby confirming its effectiveness. Supported by both laboratory and field GPR data, the algorithm’s widespread applicability is proven. In summary, the proposed workflow is expected to enhance imaging efficiency significantly, achieving a$2\times $–$5\times $speedup ratio without compromising the quality of imaging progress.

Ground Penetrating Radar (GPR) is a subsurface sensing technology extensively utilized in various applications. However, the inhomogeneous distribution of the subsurface medium results in a stochastic clutter and drastically degrades the precision of subsurface anomalies imaging. To suppress the stochastic clutter, we introduce a deep learning-based cascade network, termed the Variational Autoencoder (VAE)-Residual Network (ResNet). It employs a unique architecture that integrates the VAE and ResNet, and is further enhanced by a Residual Feature Distillation Block (RFDB) module. This configuration capitalizes on the RFDB module’s sophisticated feature extraction capabilities, thereby enhancing its proficiency in suppressing stochastic clutter. Consequently, it facilitates an end-to-end processing of stochastic clutter in GPR recordings. Finally, Reverse Time Migration (RTM) is utilized to image the processed GPR data, thereby emonstrating the enhanced accuracy in subsurface anomaly imaging and detection. Comparative analyses with established algorithms, including Non-local Means (NLM) and Block-matching and 3D Filtering (BM3D) are conducted through numerical, laboratory and field experiments. The results have not only demonstrated the proposed algorithm’s effectiveness but also established its superiority over traditional methods. The field experimental results validate the practicality and wide applicability of the proposed algorithm.

Inversion of ground-penetrating radar (GPR) data is an effective technique for imaging subsurface structures and restoring the physical parameters of mediums. However, the traditional full waveform inversion (FWI) algorithm often produces imaging artifacts and suffers from low computational efficiency. In addition, deep learning-based inversion algorithms frequently overlook the inherent time-depth relationships in the GPR data, leading to contradictions between the mapping of diverse data features to a single model and the uniqueness mapping principle of deep learning algorithms. To address these challenges, a Fusion Inversion Pix2PixGAN (FusionInv-GAN) is proposed for GPR data inversion. This approach utilizes the fused data features of reverse time migration (RTM) imaging results and GPR data to provide correct time-depth relationships for deep learning inversion, with the RTM imaging results serving as a guidance term for precise model predictions. The effectiveness and robustness of the proposed inversion framework are tested on one synthetic and two field GPR data, proving its suitability for geophysical inversion tasks.

China’s Tianwen-1 successfully landed in the southern Utopian Planitia of the Martian surface on 15 May 2021. The Zhurong Rover, equipped with a high frequency full polarimetric Rover Penetrating Radar (RoPeR), travelled 1,921 m to investigate the shallow geological structure and material composition of the Martian weathered layer. In this study, we propose a new processing strategy to estimate surface relative permittivity using the HH and VV reflections of the high frequency RoPeR data. This new strategy is based on the induced field rotation effect, which occurs when orthogonally-polarised electromagnetic waves propagate into an uneven surface with incident angles. 3D time-domain finite-difference simulations were performed using random surfaces with various relative permittivities under the same geometry as the Zhurong Rover. Polarimetric alpha angle versus relative permittivity was then calculated based on the simulation results. At the same time, direct coupling removal, band-pass filtering and channel calibration were performed on the real RoPeR data and clear surface reflections were extracted. The surface reflection amplitudes of the HH and VV were then obtained and the polarimetric alpha angle calculated. Finally, relative permittivity was estimated through the relationship obtained from the simulation results. The average value of the relative permittivity estimated by the proposed approach is 3.292, with a standard deviation of 0.235. This result is consistent with that obtained by orbiting Radar Systems and the low frequency RoPeR system. This study will contribute to the further signal processing and accurate interpretation of real radar data captured by way of RoPeR on Mars.

Ground-penetrating radar (GPR) has been widely applied to the detection and delineation of buried targets in the subsurface. Compared with conventional single-channel GPR, polarimetric GPR has been proven to possess an improved ability to detect and characterize an elongated object in the subsurface. This letter proves that the scattering signals from a cylinder in two orthogonal polarization channels have a phase difference of about 90° when its diameter-to-wavelength ratio is about 0.05–0.33. Consequently, a polarization-difference imaging method, which shifts the VV component by −90° and subtracts it from the HH component, is proposed for the improved detection and imaging of a subsurface elongated object. Its effectiveness is verified by numerical, laboratory, and field tests on buried rebars and pipes. The signal-to-clutter ratios of the reconstructed GPR images can be improved by up to 4.5 dB by considering the phase difference between the dual-polarization components.

China’s first Mars rover named “Zhurong” has begun its exploration at the surface of the Utopian Plain in the Martian northern hemisphere on May 22, 2021. Mars rover penetrating radar (RoPeR) is one of the key payloads onboard the Zhurong rover and one of its prima scientific objectives is to detect potential water ice and/or dry ice in the subsurface soil at the landing site. The high-frequency channel of RoPeR, which is equipped with a fully-polarized antenna array with a center frequency of 1.3 GHz, can record full polarimetric radar reflections from subsurface anomalies. In this article, a radar system with a polarimetric antenna array the same as RoPeR was set up and carefully calibrated. Laboratory experiments were carried out to test the feasibility of RoPeR in the detection and discrimination of potential dry ice and/or water ice in Martian soils. The experimental results indicate that the reflection signals from the bottom of the dry ice and water ice samples present different polarimetric scattering characterizations. The characteristic polarimetric scattering features of dry ice and water ice revealed in this article would guide the analysis and interpretation of the real data acquired by RoPeR on Mars.

On May 22, 2021, China's first Mars Rover “Zhurong” began its exploration on the Utopian Plain of Martian northern hemisphere. It carries a Mars Rover Penetrating Radar (RoPeR) which contains a high- and a low-frequency channels. The high-frequency channel is equipped with a full polarimetric antenna array with a central frequency of 1.3 GHz, whose scientific objective is to find potential water ice and/or dry ice in the subsurface of Mars. In this study, laboratory experiments are carried out to validate the feasibility of RoPeR to detect and discriminate ices. Results preliminarily reveal that the reflection signals from the bottom of the dry ice and water ice present different polarimetric scattering mechanism based on H-a polarization decomposition.

This letter proposes a method based on deep learning for the automatic recognition and localization of underground pipelines using the ground penetrating radar (GPR). Firstly, an automatic recognition model with an average precision (AP) of 0.9256 is proposed and trained based on Faster R-CNN. The feature extraction is optimized by the Attention-guided Context Feature Pyramid Network (ACFPN), and the cascade structure is used to improve the detection frame regression accuracy. Moreover, using Tesseract OCR, a positioning model is developed based on recognition results to obtain the burial and horizontal position of the pipeline. Furthermore, on-site experiments were carried out on real embedded pipes to verify the feasibility and effectiveness of the developed method. The absolute error of the localization data is lower than 11 cm, and the average error ratio is smaller than 12%. Consequently, it is demonstrated that the proposed method is considerably automatic, efficient, and reliable for the recognition and localization of underground pipelines.

Migration can reconstruct the geometric structure of a subsurface object from the ground penetrating radar (GPR) data. However, a GPR antenna is usually simplified as an ideal point/line source of normal migration algorithms, which ignore the influence of the antenna radiation pattern in subsurface soil. In this letter, the back-propagation algorithm is corrected with the analytical half-space far-field radiation pattern of an infinite line source. The superiority of this modified migration algorithm is verified through numerical, laboratory, and field experiments. The results show that the undesired diffractive artifacts at the target edges can be suppressed, while reserving the reflection amplitude in the migrated images with antenna pattern correction, compared with the conventional back-propagation and Kirchhoff algorithms.

The lunar regolith penetrating array radar (LRPR) carried by Chang’E-5 (CE-5) has explored the subsurface regolith structure of the moon and guided the drill sampling procedures. To evaluate LRPR performance in subsurface imaging and physical-parameter estimation, we apply multiscale full-waveform inversion (FWI) with total variation (TV) regularization to the synthetic CE-5 LRPR data. The time-domain multiscale inversion strategy gives a low-frequency update for the deep region and increases the frequency range for updating the shallow area. The TV regularization reduces the image noise and improves the inversion accuracy of local structures. To mimic the actual scenario, we use an LRPR source wavelet obtained from the LRPR instrument prototype for our FWI test. The LRPR source wavelets include the effect of the antenna radiation patterns and signal scattering from the metallic lander. We test the proposed FWI scheme on two heterogeneous models of the lunar regolith and demonstrate that the scheme effectively reduces the signal scattering from the metallic lander and provides a reliable way to image the lunar regolith structures. These images can be used to estimate the regolith physical parameters.

Ground penetrating radar (GPR) is a recognized nondestructive testing technique, which has been commonly applied to detect the steel bars (rebars) in concrete. However, most commercial GPR systems can only record reflection signals in single polarization, making the inspection of rebar corrosion difficult. In this paper, we employ a full polarization GPR system to record polarimetric information and use H-Alpha polarization decomposition to evaluate the rebar corrosion process. A yearly long corrosion process of one rebar was accelerated within 15 days by applying a constant current density of 0.3 mA/cm2on the embedded rebar. The preliminary experimental results of polarization decomposition show that the scattering characteristics of rebars change to low entropy surface scattering after corrosion. It is concluded that full-polarimetric GPR has a potential for characterization of the early-stage corrosion of concrete rebar.

Ground-penetrating radar (GPR) has been widely applied to the nondestructive inspection of concrete structures such as tunnel lining, bridge deck, and retaining wall, which are usually reinforced by steel bars. The scattering of electromagnetic (EM) waves caused by the dense steel rebar embedded in the concrete structures has a severe influence on the penetration capacity of GPR waves. In this letter, the scattering and penetration characteristics of EM waves propagating through rebar net are investigated via both numerical and laboratory experiments, with an aim to select the antenna nominal frequency for a different reinforcement density. The results show that the rebar, which is perpendicular to the polarization direction of GPR waves and has a very small diameter compared with the wavelength, is almost transparent to the impinged GPR waves. The scattering and interaction of GPR waves caused by the rebar that is parallel to the polarization direction result in a shielding effect, which is manifested as a blind band in the low-frequency range in the transmitted spectrum. This result violates the rule of thumb commonly used in the GPR community, i.e., the lower frequency has a deeper GPR penetration depth. In the end, a low cutoff frequency is recommended for selecting a GPR antenna with an appropriate nominal frequency when it is used in the detection of an anomaly inside and behind a reinforced concrete structure, in which the spacing of the rebar net is known.

A hybrid method is proposed to reconstruct the subsurface 3-D objects with electromagnetic fields. The frequency-domain reverse time migration (FRTM) is first used to determine the approximate locations and sizes of the objects. Then, based on these results, the full-wave inversion, the variational Born iteration method (VBIM) is used to reconstruct both the shapes and dielectric parameters of the objects. The Monte Carlo method (MCM) is adopted to further refine the reconstructed shapes. Numerical simulations show that the proposed hybrid method can be effectively used for the subsurface imaging and detection.

This letter proposes a data arrangement for fully polarimetric synthetic aperture radar (PolSAR). It is an essential novel method in the use of the rotation transformation in data interpretation. The key point of the proposal is employing a single pixel-based and selective rotation transformation for each pixel before the speckle filtering. The experimental results with ALOS2-PALSAR2 data show that the proposed data arrangement has much higher performance in recognizing double-bounce scattering in the man-made target area. At the same time, it is effective in avoiding the overestimation of double-bounce and/or surface scattering in natural target areas.

This letter presents the joint reconstruction of unknown subsurface structures by the full-wave inversion (FWI) and reverse time migration (RTM) imaging. In the FWI, the 1-D distorted Born iteration method (DBIM) is employed to retrieve the dielectric parameters of the layered subsurface medium by minimizing the difference between measured fields and calculated fields via Fréchet derivatives. Based on the inversion results, the RTM is directly performed in the frequency domain to image the buried objects using the ground-penetrating radar (GPR) data. Numerical and laboratory experiments show that the proposed joint method can be used to reconstruct the subsurface structures reliably and efficiently.

When sounding pavement layers using ground penetrating radar (GPR), antenna effects including dispersion and multiple reflections usually degrade the vertical resolution. In far-field conditions, these effects can be analytically filtered out by a linear method. However, for near-field operation, the antenna model tends to be nonlinear, and thus, these unwanted effects cannot be analytically removed anymore. In this letter, a method based on the radial basis function (RBF) neural network is proposed to filter out antenna effects under near-field conditions. A well-developed GPR model is used to simulate the input training data, and the corresponding zero-offset Green's function is calculated as the desired output for each training data. The trained RBF network is applied to the simulated and measured data. The results show that the proposed method is effective in filtering out the antenna effects and increasing the vertical resolution of GPR.

A newly designed grounded electrical-source airborne transient electromagnetics (GREATEM) system was introduced recently. Detailed data preprocessing techniques to acquire the high-precision measured magnetic field are discussed here. Different from the previous work in which the reconstruction of the underground structure is performed in 1-D, we interpret the GREATEM data in 3-D by the volume integral equation (VIE) method in the frequency domain. Therefore, the VIE in the forward electromagnetic scattering model is formulated in the low-frequency regime. It is solved by using the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method. In the nonlinear inversion, the Born iterative method (BIM) and the conjugate gradient method are adopted to minimize the cost function. A synthetic model of GREATEM survey is used to validate the proposed 3-D forward and inversion algorithms. Then, the field data from two GREATEM surveys are used to test the effectiveness and accuracy of the proposed inversion algorithm. The reconstructed conductivity structures are consistent with geological drilling results, confirming the potential of our method for solving the 3-D GREATEM inversion problems in geophysical engineering applications. This paper represents the first application of the BCGS-FFT and BIM algorithms to a GREATEM system.

Ground penetrating radar (GPR) has been widely applied to detection of subsurface linear targets, such as underground pipes and reinforced rebars in concrete structures. The azimuth direction of a subsurface linear target can be hardly delineated by a commercial single-polarization GPR system. In this paper, a hybrid dual-polarimetric GPR system is employed to detect buried linear objects. A full-polarimetric scattering matrix is extracted from the double-channel GPR reflection signal. A rotation transformation is applied to the scattering matrix to estimate the target azimuth angle. A laboratory experiment was conducted to detect four metal rebars buried in dry sand at different azimuth angle relative to the GPR scan direction. The maximum error of the estimated azimuth direction is less than 15%. It is concluded that radar polarimetry can provide richer information than single-polarization GPR.

The hardware instability of a ground penetrating radar (GPR) system has a severe impact on the quantitative analysis of GPR data, which is aimed for material characterization and subsurface monitoring. In this letter, an instability index is proposed to quantify the stability performance of a GPR system and the influences of the GPR system type, warm-up time, environmental noise, and the antenna vibration on it are evaluated through a series of laboratory experiments on a sandbox model. It is found that the GPR signal recorded by a stepped-frequency GPR system based on a vector network analyzer is much more stable than that by a commercial impulse GPR system at a cost of more sweep time. A warm-up time of several minutes is enough for an impulse GPR system. Environmental noise has a negligible influence on the stability performance of a GPR system. Mechanical vibrations of GPR antennas have a severe impact on the stability performance of the GPR system, and the instability index and timing jitter can be increased by more than one order of magnitude in a vibrating condition over those in a static condition. The instability index of the direct signal has a negligible difference with that of the reflection signal from a metal plate; thus, a simple measurement of direct signal on the ground surface is suggested for the evaluation of the instability of a GPR system in field in the future.

An efficient 2-D joint full-waveform inversion method for electromagnetic and seismic data in a layered medium background is developed. The joint inversion method based on the integral equation (IE) method is first proposed in this paper. In forward computation, the IE method is employed, which usually has smaller discretized computation domain and less cumulative error compared with the finite-difference method. In addition, fast Fourier transform is used to accelerate the convolution between Green's functions and induced sources due to the shift invariance property of the layered Green's functions in the horizontal direction. In the inversion model, the cross-gradient function is incorporated into the cost function of the separate inversion to enforce the structure similarity between electric conductivity and seismic-wave velocity. We use the improved variational Born iteration method and two different iteration strategies to minimize the cost function and reconstruct the contrasts. Several typical models in geophysical applications are used to validate our joint inversion method, and the numerical simulation results show that joint inversion can improve the inversion results when compared with those from the separate inversion.

A new iterative inversion algorithm is proposed to reconstruct the electrical conductivity profile in a stratified underground medium for the grounded electrical source airborne transient electromagnetic (GREATEM) system. In forward modeling, we simplify the mathematical expressions of the magnetic fields generated by a finite line source in the layered ground to semianalytical forms in order to save the computation time. The Fréchet derivative is derived for the electromagnetic response at the receivers due to a small perturbation of the conductivity in a certain layer underground. The initial expression of the Fréchet derivative has an expensive triple integral and contains the Bessel function in the integrand. It is simplified by partially eliminating the integration along the source line and deriving the analytical expression for the integration in the vertical direction inside the perturbed layer. In the inverse solution, we use the distorted Born iterative method (DBIM). This is the first time that the DBIM is applied to data measured by the GREATEM system. Besides, the forward and inverse procedures are carried out in the frequency domain and based on the Fréchet derivative of a line source. We demonstrate the validity of our forward model, Fréchet derivative, inverse model, and the precision as well as robustness of the inversion algorithm through numerical computation and comparisons. Finally, we apply the inversion algorithm to the measured data and compare the retrieved conductivity to the actual drilling data.

The variational Born iterative method (VBIM) is employed here to reconstruct 3-D objects with permittivity contrast buried in spherically multilayered media. The nonlinear inverse problem is solved iteratively via the conjugate-gradient method, and in each iteration, the scattered field is linearized by using the Born approaximation. The forward solver is provided by the method of moments combined with a Krylov subspace method. The dyadic Green's function for spherically layered media is constructed in terms of the spherical vector wave functions by using the scattering superposition in the spherical coordinate system and then transformed into the Cartesian coordinate system. Thus, the inversion region is discretized into N uniform cubic cells and the reconstructed result can be obtained in the Cartesian coordinate system by employing VBIM. Numerical results with high resolution are presented to validate the capability of our method in reconstructing 3-D multiple objects of arbitrary shapes buried in spherically multilayered media.

We apply the forward scattering and inverse scattering algorithms to a cold unmagnetized plasma region within a multilayered background medium. Each layer has a different plasma frequency. The disturbed region in the plasma has an arbitrary shape, so it is an electromagnetic wave scatterer and can exist in any layer. The stabilized biconjugate-gradient fast Fourier transform (BCGS-FFT) algorithm is used to compute the scattered field. The scattered fields calculated by the BCGS-FFT yield excellent agreement with simulated results from the commercial software. In the inverse scattering process, the variational Born iterative method is used to reconstruct the relative permittivity, and thus the plasma frequency of the disturbed region. Multiple frequencies are adopted to determine the dispersive property of the plasma medium.

This paper presents a multi-input and multi-output (MIMO) ground penetrating radar (GPR) system, which is going to be launched to the moon for imaging shallow regolith structures and estimating the dielectric properties. This system, as an important part of China' Chang-E 5 lunar exploration mission, employs twelve off-ground Vivaldi antennas as transmitters/receivers, and works in a stationary mode. A reverse-time migration algorithm is developed to process the MIMO GPR dataset for obtaining a high-resolution image of the subsurface objects. The results of a laboratory experiment on a volcanic ash pit demonstrate that the upper and lower interfaces of a marble slab of 3 cm thickness buried at a depth up to 2 m can be clearly imaged. A full waveform inversion algorithm based on Born iterative method is applied to invert the dielectric properties of the subsurface objects. The preliminary results of a numerical experiment demonstrate that the dielectric permittivity of a subsurface cubic object can be accurately obtained using the MIMO GPR dataset at only six discrete frequencies.

Low-frequency subsurface electromagnetic measurements are important tools for characterizing natural resources and environmental wastes. Rapid simulations of low-frequency subsurface electromagnetic measurements are still a challenge because of the large computational domain and low-frequency breakdown phenomenon. We develop an effective method to simulate these low-frequency subsurface electromagnetic measurements by using the spectral element method together with a domain decomposition method (DDM). A specific mesh has been designed based on the traveling wave nature in the air and the diffusion field nature in the underground space to greatly reduce the number of unknowns. The frequency-domain version of the Riemann solver (upwind flux) is used as an effective transmission condition to simulate the interactions between neighboring subdomains in DDM. Several numerical examples demonstrate the efficiency of the proposed approach in low-frequency subsurface electromagnetics simulations.

We are developing radar technology to inspect the inner structure of wooden buildings suffered from strong quake by earthquake. GB-SAR (Ground Based Synthetic Aperture Radar) for inspection of wooden and concrete walls and structures has been developed and the system was evaluated by test measurements. The GB-SAR system uses frequency bandwidth 1-20GHz, and it can acquire full polarimetric radar signal. We found that frequency up to 20GHz can be usefully used for inspection, and show that the radar could achieve the resolution about 1.5cm, and found that t can be used for detection of crack inside concrete structure. Then Synthetic Aperture (SAR) Radar signal processing is used to reconstruct 3-dimenatioal images of inner structure of the targets. We found that the radar polarimetry gives us very precise information of the damaged structures, and demonstrated that radar polarimetry is a useful tool for detecting fractures inside a concrete structures, and detection of small deformation of wooden structures.

In order to accurately monitor the dynamic groundwater level change caused by the well pumping in a non-destructive way, we developed Common Mid-Point (CMP) method using Ground Penetrating Radar (GPR). The envelope velocity spectrum and an automatic velocity picking scheme were proposed for the accurate velocity analysis of CMP dataset. We applied the algorithm to the GPR data acquired near Tuul river in Mongolia. The obtained vertical velocity profile was converted to the vertical water content profile, in which the groundwater level could be easily identified. In this measurement, a groundwater level rise of 0.21 m was observed after the pumping was stopped. The dynamic groundwater level change estimated by GPR was used to estimate the hydraulic conductivity of the unconfined groundwater aquifer. The result agreed well with a previous study.

In this paper, we proposed a new cross-correlation sum making use of the complex analytic signal to calculate the coherency of the signals for velocity spectrum analysis. It can estimate dielectric constant from multi-channel dataset robustly even the signal with low SNR. On the other hand, a vector network analyzer based GPR system with an antenna array was developed. The antenna array consisted of one transmit antenna and five receive antennas and it can detect the refection signals at different antenna offset with a high signal-to-clutter ratio. Air layer, gypsum wall and concrete wall of different thickness from 10 cm to 30 cm were tested and four field datasets on asphalt pavement of highway road were collected. The estimation results of dielectric constant and layer thickness validated the accuracy. The maximum errors of the thickness estimation results were less than 1.3 cm and the relative errors were smaller than 11%.