Abstract
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.
Highlights
Based on the high-quality radargram provided by the convolutional neural network (CNN) filter, the subsurface sandwich structure is revealed and the weak signals from three sub-layers within the paleo-regolith are extracted
The results presented by the CNN filter are cleaner than that of the combined method
The low-frequency clutter extracted from CE-3 lunar penetrating radar (LPR) data using the empirical mode decomposition (EMD) method is applied to the training of CNN
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Many filtering methods have been applied to LPR data, such as band-pass filtering [34], bi-dimensional empirical mode decomposition (BDEMD) [35], complete ensemble empirical mode decomposition (CEEMD) [36], mathematical morphology filtering [37], and variational mode decomposition (VMD) [38] These methods have their limitations and cannot provide a satisfactory result. Based on the well trained CNN filter, the low-frequency clutters and high-frequency noises are both suppressed in the field LPR profile and the weak signals in the deep are extracted. 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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