A Compressive Sensing-Based Approach to Reconstructing Regolith Structure from Lunar Penetrating Radar Data at the Chang’E-3 Landing Site

Kun Wang,Zhaofa Zeng,Ling Zhang,Jing Li,Shugao Xia

doi:10.3390/rs10121925

Abstract

Lunar Penetrating Radar (LPR) is one of the important scientific systems onboard the Yutu lunar rover for the purpose of detecting the lunar regolith and the subsurface geologic structures of the lunar regolith, providing the opportunity to map the subsurface structure and vertical distribution of the lunar regolith with a high resolution. In this paper, in order to improve the capability of identifying response signals caused by discrete reflectors (such as meteorites, basalt debris, etc.) beneath the lunar surface, we propose a compressive sensing (CS)-based approach to estimate the amplitudes and time delays of the radar signals from LPR data. In this approach, the total-variation (TV) norm was used to estimate the signal parameters by a set of Fourier series coefficients. For this, we chose a nonconsecutive and random set of Fourier series coefficients to increase the resolution of the underlying target signal. After a numerical analysis of the performance of the CS algorithm, a complicated numerical example using a 2D lunar regolith model with clipped Gaussian random permittivity was established to verify the validity of the CS algorithm for LPR data. Finally, the compressive sensing-based approach was applied to process 500-MHz LPR data and reconstruct the target signal’s amplitudes and time delays. In the resulting image, it is clear that the CS-based approach can improve the identification of the target’s response signal in a complex lunar environment.

Highlights

The capability of ground-penetrating radar (GPR) to penetrate different materials makes it an effective and nondestructive geophysical tool for mapping the subsurface stratigraphy of the Moon to a given depth, which depends on the radar frequency and dielectric property of the lunar surface materials [1,2]
The Lunar Radar Sounder (LRS) onboard Kaguya was used to detect the geological structure at depths of 4–5 km under the lunar surface [3,4]; the Apollo Lunar Sounder Experiment (ALSE) on the Apollo 17 spacecraft obtained a large amount of geological data from depths of 1–2 km below the surface of Moon [4,5]; and the dual-frequency Lunar Penetrating Radar (LPR) on the Yutu lunar rover, part of China’s Chang’E-3 (CE-3) lunar mission, focuses on mapping the near-surface stratigraphic structure of the lunar regolith to a depth of several tens of meters [2,4,6,7,8]
Even though the fine-grained regolith was composed of numerous layers, the layer thickness was typically on the order of several centimeters [37], which is much smaller than the LPR range resolution [4]

Summary

Introduction

The capability of ground-penetrating radar (GPR) to penetrate different materials makes it an effective and nondestructive geophysical tool for mapping the subsurface stratigraphy of the Moon to a given depth, which depends on the radar frequency and dielectric property of the lunar surface materials [1,2]. The composition and structure of the lunar regolith hold vital clues about the geology and impact history of the Moon. The LPR of the CE-3 mission provides the opportunity to explore the subsurface structure and vertical distribution of the lunar regolith with a high resolution. Compared to the LRS (frequency of 5 MHz [3]) and ALSE(frequencies of 5, 15, and 150 MHz [5]), LPR can map the composition and structure of the regolith at shallower depths and with a higher range of resolution due to the higher frequencies used, especially the channel with a frequency at 500 MHz [6,13]. In the analysis and evaluation of LPR data, the response signal caused by discrete reflectors beneath the lunar surface provides very useful information [4,6,7,14,15,16]. Improving the capability to identify response signals of the discrete reflectors from LPR data is necessary

Methods

Results

Discussion

Conclusion