Indiana University-Purdue University Indianapolis (IUPUI) / The main objective of this work is to better quantify lunar surface minerals (agglutinate, clinopyroxene, orthopyroxene, plagioclase, olivine, ilmenite, and volcanic glass), particle sizes and the abundance of SMFe from the lunar soil characterization consortium (LSCC) dataset with our improved model based on Hapke's radiative transfer theory. The model is implemented for both forward and inverse modeling. Hapke's radiative transfer theory is implemented in the inverse model means Newton's method and least squares are jointly used to solve nonlinear questions rather than commonly used look-up Table (LUT). Although the effects of temperature and surface topography are incorporated into the implementation to improve the model performance for application of lunar spacecraft data, these effects cannot be extensively addressed in the current work because of the use of lab measured reflectance data. Our forward radiative transfer model (RTM) results show that the correlation coefficients between modeled and measured spectra are over 0.99. For the inverse model, the distribution of the calculated particle sizes is all within their measured range. The range of modeled SMFe for highland samples is 0.01% - 0.5 % and for mare samples is 0.03% - 1 %. The linear trend between SMFe and ferromagnetic resonance (Is) for all the LSCC samples is consistent with laboratory measurements. For quantifying lunar mineral abundances, the results show that the R-squared for the training samples (Is/FeO <= 65) are over 0.65 with plagioclase having highest correlation (0.94) and pyroxene the lowest (0.68). In the future work, the model needs to be improved for handling more mature lunar soil samples.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/2747 |
| Date | 16 March 2012 |
| Creators | Li, Shuai |
| Contributors | Li, Lin, Licht, Kathy J., Swope, R. Jeffery |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0016 seconds