Exploration Geophysics is published by CSIRO PUBLISHING on behalf of the Australian Society of Exploration Geophysicists. Papers published report the results of significant case histories and relevant original research in geophysics, with emphasis on the Australian and similar environments.

The Lunar Penetrating Radar (LPR) onboard the Yutu-2 rover from China’s Chang’E-4 (CE-4) mission is used to probe the subsurface structure and the near-surface stratigraphic structure of the lunar regolith on the farside of the Moon. Structural analysis of regolith could provide abundant information on the formation and evolution of the Moon, in which the rock location and property analysis are the key procedures during the interpretation of LPR data. The subsurface velocity of electromagnetic waves is a vital parameter for stratigraphic division, rock location estimates, and calculating the rock properties in the interpretation of LPR data. In this paper, we propose a procedure that combines the regolith rock extraction technique based on local correlation between the two sets of LPR high-frequency channel data and the common offset semblance analysis to determine the velocity from LPR diffraction hyperbola. We consider the heterogeneity of the regolith and derive the relative permittivity distribution based on the rock extraction and semblance analysis. The numerical simulation results show that the procedure is able to obtain the high-precision position and properties of the rock. Furthermore, we apply this procedure to CE-4 LPR data and obtain preferable estimations of the rock locations and the properties of the lunar subsurface regolith.

Hyperspectral unmixing plays an important role in hyperspectral imagery (HSI) processing. Numerous unmixing algorithms have been proposed over the last decades. In this letter, we focus on the blind source separation model, which has drawn much attention in the hyperspectral community. However, the nonconvexity of the blind unmixing method often suffers from undesired solution. Thus, additional assumptions and regularizations are required to advance the unmixing performance. In this work, we proposed a new weighted total variation regularized blind unmixing (wtvBU) for HSI. The nonconvex sparsity-inducing function log-exp was exploited to build the weight matrix, which promotes the smooth transitions in the abundance map while preserving the spatial contextual information of the image scene. The proposed algorithm was efficiently solved via the alternating direction method of multipliers. Experimental results on two benchmark HSIs demonstrated that wtvBU achieved competitive performance when compared with other state-of-the-art unmixing algorithms.