Ground-penetrating radar (GPR) is a kind of high-frequency electromagnetic detection technology. It is mainly used to locate targets and interfaces in underground structures. In addition to the effective signals reflected from the subsurface objects or interfaces, the GPR signals in field work also include noise and different clutters, such as antenna-coupled waves, ground clutters, and radio-frequency interference, which have similar wavelet spectral characteristics with the target signals. Clutter and noise seriously interfere with the target’s response signal. The singular value decomposition (SVD) filtering method can select appropriate singular values and characteristic components corresponding to the effective signals for signal reconstruction to filter the GPR data. However, the conventional time-domain SVD method introduces fake signals when eliminating direct waves, and does not have good suppression of random noise around non-horizontal phase axes. Here, an SVD method based on the Hankel matrix in the local frequency domain of GPR data is proposed. Different numerical models and real field GPR data were handled using the proposed method. Based on the power of fake signals introduced via different processes, qualitative and quantitative analyses were carried out. The comparison shows that the newly proposed method could improve efforts to suppress random noise around non-horizontal phase reflection events and weaken the horizontal fake signals introduced by eliminating clutter such as ground waves.

The evaluation and inspection of steel bars in reinforced concrete structures are critical for prolonging the service life of buildings. In this regard, ground-penetrating radar (GPR) has been a crucial alternative due to its non-invasiveness and convenience. This paper reports the experimental activities on a test-site area inside a camp in Shanghai, China. To assess the concrete structures of the building, GPR was employed for the detection and localization of rebars in columns, beams, and floors. From the GPR B-scan profiles acquired using a high-frequency antenna, the exact quantity of reinforcements was identified according to the hyperbola responses. Considering the difficulty of inferring the exact position and the scale of the rebars, we applied reverse time migration (RTM) to collapse the hyperbolic response and retrieve the target in a migrated image. To verify the effectiveness of the RTM algorithm, we carried out an experiment on a concrete model with three reinforced bars. We also utilized the RTM algorithm to process the B-scan profiles collected in a column that was later excavated. The imaging results validated the capacity of RTM in localizing and shaping rebars. Then, we employed the RTM algorithm for the GPR B-scan data collected from the other column. Based on the imaging profile, the quantity and positions of the rebars were correctly determined. Moreover, the thickness of the protective layer was evaluated according to the migrated result. These results demonstrate that GPR combined with RTM could provide useful foundation data for structural evaluation.

SUMMARY: The analysis of multistation surface wave records is of increasing popularity in imaging the structure of the Earth due to its robustness on dispersion measurement. Since the representation of multistation surface wave dispersion curves (DCs) is uncertain in laterally varying media, average information beneath the receiver array is assumed to be obtained by inverting the dispersion curves with a horizontally layered model. To retrieve a more realistic 2-D laterally varying structure, we present a multiscale window analysis of surface waves (MWASW) method for analysing 2-D active-source surface wave data. The MWASW method is based on the use of a forward algorithm for calculating the theoretical DCs over 2-D models and multisize spatial windows for estimating the dispersion data. The forward algorithm calculates the theoretical dispersion considering the lateral variation beneath the receiver array; hence, the estimated DC is not treated as representative of the average properties but as data containing the lateral variation information. By inverting the dispersion data extracted from different spatial windows, the subsurface information at different depth ranges and lateral extensions are integrated to produce a shear wave velocity model. The dispersion curves analysed from smaller spatial windows retrieve the shallow structure with a higher lateral resolution, whereas the phase velocity data from larger spatial windows provide average information with a greater depth. We test the effectiveness of the MWASW method using three synthetic examples and two field data sets. Both results show the improved lateral resolution of the S-wave velocity structure retrieved with the MWASW method compared to the traditional multistation method in which the local horizontally layered model is adopted.

Point cloud registration is a fundamental problem in many applications. The point cloud registration based on local shape descriptor has been widely researched. In order to further improve the performance of registration, a novel registration method is proposed in this paper. First, a binary descriptor is designed to establish correspondences between two point clouds. The descriptor has high descriptiveness. Thus, more correct correspondences are established. Then, a 3D transformation estimation technique is developed, in which multiple constraints are used to accelerate the computation. When the randomly selected correspondences do not satisfy the constraints, the iteration is skipped. Finally, the experiments are performed to analyze the descriptor and 3D transformation estimation technique. The comparison with the existing descriptors is implemented on three datasets. The results demonstrate that our descriptor has better matching performance. As for the 3D transformation estimation technique, the combinations of the constraints are first analyzed. The performance of different constraints are presented and the best combination is chose. The comparative results with the existing techniques demonstrate that the proposed 3D transformation estimation technique can obtain better registration accuracy and computation efficiency.

Numerous learning-based methods have been proposed to address the challenging task of solving inverse scattering problems (ISPs), with the requirement of ample labeled training datasets. To overcome the difficulty of collecting fully labeled datasets, we present a physically guided deep unfolding parallel network with self-supervised learning strategy to solve nonlinear ISPs, called SSISP-Net. The proposed method incorporates a physical model and the iterative optimization algorithm of ISPs into the network architecture and, thus, forms a deep unfolding network. First, here, two subsets of scattered fields are generated from fully measured scattered field data using receiver antenna positions. Second, we construct a parallel network by using two deep unfolding networks and then train this parallel network using only these two subsets of the scattered field. Finally, to constrain the two unfolding networks in the parallel network, we design two scattered field crossing losses and a contrast difference loss in the network. In this way, the reconstructed model can be properly trained using only scattered field data. The numerical and experimental results demonstrate that the proposed self-supervised learning method outperforms traditional and several other supervised learning methods under similar conditions. Moreover, it achieves competitive reconstruction performance compared with supervised learning methods known for their superior reconstruction capabilities.

Regularization has been an effective technique to provide unique and stable solutions for crosshole ground-penetrating radar (GPR) traveltime tomography. The traditional form of this method, in which a low-order differential operator was used, commonly yields a smooth solution that may not be appropriate when anomalies occur in block patterns, such as voids or irregular objects. The minimum support (MS) functional can be used to improve the resolution of blocky structures; however, in crosshole GPR traveltime tomography, the MS functional is unable to resolve residual artifacts, whose departure from an a priori model are smaller than the focusing parameter selected from a trade-off curve. In addition, it would result in severe instability and yield a trade-off curve with poorly defined corners when the focusing parameter nears the precision of the apparatus. We have developed a new stabilizing functional based on the arctangent (AT) function that effectively removes the artificially small values in the crosshole GPR traveltime tomography, and ultimately is more efficient because it does not require the user to select a focusing parameter. We inverted three 2D synthetic data sets based on the reweighted regularized conjugate gradient algorithm. Compared with the low-order differential and MS functional, the user will be able to clearly distinguish the anomaly boundary using this method, which will yield results that are closer to the actual structure. We also discussed the impact of some influencing factors caused by the noise contained in the data, the central frequency of the antenna, the anomalous trends, and the ray coverage angle. We further inverted an experimental data set to test the effectiveness and robustness of the method.