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.

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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.

As one of the main payloads mounted on the Yutu-2 rover of Chang’E-4 probe, lunar penetrating radar (LPR) aims to map the subsurface structure in the Von Kármán crater. The field LPR data are generally masked by clutters and noises of large quantities. To solve the noise interference, dozens of filtering methods have been applied to LPR data. However, these methods have their limitations, so noise suppression is still a tough issue worth studying. In this article, the denoising convolutional neural network (CNN) framework is applied to the noise suppression and weak signal extraction of 500 MHz LPR data. The results verify that the low-frequency clutters embedded in the LPR data mainly came from the instrument system of the Yutu rover. Besides, compared with the classic band-pass filter and the mean filter, the CNN filter has better performance when dealing with noise interference and weak signal extraction; compared with Kirchhoff migration, it can provide original high-quality radargram with diffraction information. Based on the high-quality radargram provided by the CNN filter, the subsurface sandwich structure is revealed and the weak signals from three sub-layers within the paleo-regolith are extracted.

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A full-polarimetric ground penetrating radar (FP-GPR) uses an antenna array to detect subsurface anomalies. Compared to the traditional GPR, FP-GPR can obtain more abundant information about the subsurface. However, in field FP-GPR measurements, the arrival time of the received electromagnetic (EM) waves from different channels cannot be strictly aligned due to the limitations of human operation errors and the craftsmanship of the equipment. Small misalignments between the radargrams acquired from different channels of an FP-GPR can lead to erroneous identification results of the classic Freeman decomposition (FD) method. Here, we propose a local Freeman decomposition (LFD) method to enhance the robustness of the classic FD method when managing with misaligned FP-GPR data. The tests on three typical targets demonstrate that misalignments will severely interfere with the imaging and the identification results of the classic FD method for the plane and dihedral scatterers. In contrast, the proposed LFD method can produce smooth images and accurate identification results. Besides, the identification of the volume scatterer is not affected by misalignments. A test of ice-fracture detection further verifies the capability of the LFD method in field measurements. Due to the different relative magnitudes of the permittivity of the media on two sides of the interfaces, the ice surface and ice fracture show the features of surface-like and double-bounce scattering, respectively. However, the definition of double-bounce scattering is different from the definition in polarimetric synthetic aperture radar (SAR). Finally, a quantitative analysis shows that the sensitivities of the FD and LFD methods to misalignments are related to both the type of target and the polarized mode of the misaligned data. The tolerable range of the LFD method for misalignments is approximately ±0.2 times the wavelength of the EM wave, which is much wider than that of the FD method. In most cases, the LFD method can guarantee an accurate result of identification.