In this letter, we apply a time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) method in ground-penetrating radar (GPR) signal processing. It decomposes the GPR signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. The key idea of this method relies on averaging the modes obtained by empirical mode decomposition (EMD) applied to several realizations of Gaussian white noise added to the original signal. It can solve the mode-mixing problem in the EMD method and improve the resolution of ensemble EMD (EEMD) when the signal has a low signal-to-noise ratio. First, we analyze the difference between the basic theory of EMD, EEMD, and CEEMD. Then, we compare the time and frequency analysis with Hilbert–Huang transform to test the results of different methods. The synthetic and real GPR data demonstrate that CEEMD promises higher spectral–spatial resolution than the other two EMD methods in GPR signal denoising and target extraction. Its decomposition is complete, with a numerically negligible error.

We present the theory for wave-equation inversion of dispersion curves, where the misfit function is the sum of the squared differences between the wavenumbers along the predicted and observed dispersion curves. The dispersion curves are obtained from Rayleigh waves recorded by vertical-component geophones. Similar to wave-equation traveltime tomography, the complicated surface wave arrivals in traces are skeletonized as simpler data, namely the picked dispersion curves in the phase-velocity and frequency domains. Solutions to the elastic wave equation and an iterative optimization method are then used to invert these curves for 2-D or 3-D S-wave velocity models. This procedure, denoted as wave-equation dispersion inversion (WD), does not require the assumption of a layered model and is significantly less prone to the cycle-skipping problems of full waveform inversion. The synthetic and field data examples demonstrate that WD can approximately reconstruct the S-wave velocity distributions in laterally heterogeneous media if the dispersion curves can be identified and picked. The WD method is easily extended to anisotropic data and the inversion of dispersion curves associated with Love waves.

The Chang'E‐4 (CE‐4) achieved the first successful soft landing on the floor of the Von Kármán crater located in the northeast of the South Pole‐Aitken (SPA) basin on the Moon. Here, through analysis and processing of the dual‐channel lunar penetrating radar (LPR) data, the shallow regolith structure and deep geological strata below the landing site are exposed. The high‐frequency data (CH‐2, 500 MHz) show that the Von Kármán crater underwent remodeling because of ejecta from multiple craters within a thickness of 40 m. Under the surface fine‐grained regolith, superpositions among these crater materials and mare basalt units establish shallow regolith stratigraphic sequences. The low‐frequency data (CH‐1, 60 MHz) show a deep stratigraphic structure to the depth of 360 m that indicates the previous geological evolution history of the Von Kármán crater. The LPR results provide an important scientific basis for understanding the geological history of the far side of the Moon.

Deep learning (DL) has gained traction in ground-penetrating radar (GPR) tasks. However, obtaining sufficient training data presents a significant challenge. We introduce a structure-adaptive GPR-generative adversarial network (GAN) to generate GPR defect data. GPR-GAN employs double normalization for stabilizing parameters and convolution outputs, an adaptive discriminator augmentation (ADA) module for small dataset training stability, and a modified self-attention (MSA) module to generate GPR defects with complex features. We evaluated the performance of GPR-GAN using three datasets in conjunction with three state-of-the-art detection networks (faster region-based convolutional neural network (FasterRCNN), single-shot multibox detector (SSD), and YOLOv5). Our results reveal that GPR-GAN exhibits strong generalization skills, adeptly adapting to GPR data generation tasks that encompasses a variety of targets, frequencies, and equipment. GPR-GAN generated data increased the $F1$ score for void recognition in simulation data by at least 5.27%, improved the average $F1$ score for highway pavement defect detection by at least 7.68%, and enhanced the average $F1$ score for railway subgrade defect detection by at least 9.22%. GPR-GAN offers a powerful data support tool for DL research in GPR.

We present a dispersion‐inversion method which inverts for the P‐velocity model from guided waves propagating in wave guides of arbitrary geometry. Its misfit function is the squared summation of differences between the predicted and observed dispersion curves of guided P waves, and the inverted result is a high‐resolution estimate of the near‐surface P‐velocity model. We denote this procedure as wave‐equation dispersion inversion of guided P waves (WDG), which is valid for near‐surface waveguides with irregular layers and does not require a high‐frequency approximation. It is more robust than full waveform inversion and can sometimes provide velocity models with higher resolution than wave‐equation traveltime tomography. Both the synthetic‐data and field data results demonstrate that WDG for guided P waves can accurately invert for complex P‐velocity models at the near surface of the Earth.

The Chinese Chang’E-4 mission for moon exploration has been successfully completed. The Chang’E-4 probe achieved the first-ever soft landing on the floor of Von Kármán crater (177.59°E, 45.46°S) of the South Pole-Aitken (SPA) basin on January 3, 2019. Yutu-2 rover is mounted with several scientific instruments including a lunar penetrating radar (LPR), which is an effective instrument to detect the lunar subsurface structure. During the interpretation of LPR data, subsurface velocity of electromagnetic waves is a vital parameter necessary for stratigraphic division and computing other properties. However, the methods in previous research on Chang’E-3 cannot perform velocity analysis automatically and objectively. In this paper, the 3D velocity spectrum is applied to property analysis of LPR data from Chang’E-4. The result shows that 3D velocity spectrum can automatically search for hyperbolas; the maximum value at velocity axis with a soft threshold function can provide the horizontal position, two-way reflected time and velocity of each hyperbola; the average maximum relative error of velocity is estimated to be 7.99%. Based on the estimated velocities of 30 hyperbolas, the structures of subsurface properties are obtained, including velocity, relative permittivity, density, and content of FeO and TiO₂.