Selection of Whole-Moon Landing Zones Based on Weights of Evidence and Fractals

Abstract

At present, the selection of lunar landing areas is mostly determined by experts’ argumentation and experience. Generally, it is artificially limited to a small zone, and there are few effective quantitative models for landing areas. Under the premise that big data, artificial intelligence, and other technologies are becoming increasingly mature, with in-depth analysis and the mining of lunar-related digital data, it is possible to automatically optimize the landing zones in the whole moon. Factors such as engineering constraints, scientific goals, and resource requirements are comprehensively considered. This paper proposes a new method that strategically applies the weights of evidence (WoE) and fractals to optimize the landing area of the detector in the whole moon. The method takes the thickness of the lunar crust, roughness, slope, digital elevation model, gravity gradient, iron oxide distribution, and lunar soil optical maturity as evidence layers, and known landing sites as the target layer. After all moon data are divided into grids, the prior probability of each evidence factor, the in-cell weight of each evidence factor, and the Bayesian posterior probability are calculated. According to the semi-parabolic distribution in the fuzzy distribution, the fuzzy membership degree of the impact crater radius is presented and the complexity of the number of impact craters in a cell is calculated. The distribution complexity of impact craters in each cell is calculated according to the fractal. The result of the weights of evidence is further constrained by the complexity of the number of cells and the complexity of the distribution, and the posterior probability map of suitable landings is finally obtained. When comparing and analyzing the posterior probability map of the landing zones with the known landing points and the artificially preferred landing zones, it is found that 84% and 82.6% fall within the suitable landing zones, respectively. Among them, the first gradient is 58% and 58.7%, and the second gradient is 26% and 23.9%. The results at different resolutions are relatively stable and are consistent with the distribution of craters or basins in the lunar mantle and the spatial distribution of olivine, which proves the effectiveness and feasibility of this method. This method is a typical application of lunar big-data-driven knowledge discovery and will help promote the transformation of lunar landing area selection from traditional qualitative analyses to automated intelligence optimization.