Spectral Properties of Ilmenite, Hematite, and Spinel: Implications for Upcoming Lunar Exploration Missions
Bibliographic record
Abstract
Abstract Upcoming missions to the south polar region of the Moon will investigate this uncharted terrain. Investigations will include determining the bulk composition of the lunar crust to enhance our understanding of planetary processes. Lunar samples and remote-sensing data indicate that the lunar crust is mainly composed of silicate minerals such as pyroxene, plagioclase feldspar, and olivine. Oxide minerals such as ilmenite, hematite, and spinel, present in lower abundances, are valuable for in situ resource utilization, serving as sources of oxygen, hydrogen, or titanium. This study focuses on characterizing the spectral properties of ilmenite, hematite, and spinel and assessing their detectability using rover-based spectral instruments. We performed reflectance measurements of these minerals across the 350–15,385 nm range, for several grain sizes, viewing geometries, and abundances in mixtures with LHS-1 simulant. This resulted in a spectral library comprising over 1000 spectra. We modeled various mineral/LHS-1 spectral mixtures using the Hapke radiative transfer model and compared them to our laboratory spectra to assess the model’s accuracy. The model yielded an average error of 4 wt% using the rms error and 5 wt% using the spectral angle. Finally, we explored the potential for detecting these minerals with multispectral sensors and identified band ratios that correlate with mineral abundance. Our findings suggest that the ratios BR_ILM_AIM1, BD_HEM_RMM, and BD_SPI_AIM are most strongly correlated with ilmenite, hematite, and spinel abundances and that the calculation of these ratios allows us to quickly evaluate the presence of these minerals within highlands material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".