Inversion of lunar regolith layer thickness with CELMS data using BPNN method

Inversion of the lunar regolith layer thickness is one of the scientific objectives of current Moon research. In this paper, the global lunar regolith layer thickness is inversed with the back propagation neural network (BPNN) technique. First, the radiative transfer simulation is employed to study the relationship between the lunar regolith layer thickness d and the observed brightness temperature TB׳s. The simulation results show that the parameters such as the surface roughness σ, slope θs and the (FeO+TiO2) abundance S have strong influence on the observed TB׳s. Therefore, TB׳s, σ, θs and S are selected as the inputs of the BPNN network. Next, the four-layer BPNN network with seven-dimension input and two hidden layers is constructed by taking nonlinearity into account with sigmoid functions. Then, BPNN network is trained with the corresponding parameters collected in Apollo landing sites. To tackle issues introduced by the small number of the training samples, the six-dimension similarity degree is introduced to indicate similarities of the inversion results to the correspondent training samples. Thus, the output lunar regolith layer thickness is defined as the sum of the product of the similarity degree and the thickness at the corresponding landing site. Once training phase finishes, the lunar regolith layer thickness can be inversed speedily with the four-channel TB׳s concluded from the CELMS data, σ and θs estimated from LOLA data and S derived from Clementine UV/vis data. the inversed thickness agrees well with the values estimated by ground-based radar data in low latitude regions. The results indicate that the thickness in the maria varies from about 0.5 m to 12 m, and the mean is about 6.52 m; while the thickness in highlands is a bit thicker than the previous estimation, where the thickness varies widely from 10 m to 31.5 m, and the mean thickness is about 16.8 m. In addition, the relation between the ages, the (FeO+TiO2) abundance and the inversed regolith layer thicknesses in the nine main maria indicates that the regolith layer thickness is directly related to its age if the basalt is of the same kind. Furthermore, the correlation between the inversed thickness and the seven input parameters along the Moon Equator indicates that the surface roughness has the largest impact on the inversed thickness, followed by the CELMS data in 3 GHz and the slope.