Signal to Noise Ratio and Spectral Sampling Constraints on Olivine Detection and Compositional Determination in the Intermediate Infrared Region: Applications in Planetary Sciences

Abstract

Abstract Spectral features of olivine across the intermediate infrared region (IMIR, 4–8 μm) shift systematically with iron‐magnesium content, enabling determination of olivine composition. Previous IMIR studies have used laboratory data with signal‐to‐noise ratios (SNRs) and spectral resolutions potentially greater than those of data derived from planetary missions. Here we employ a feature fitting algorithm to quantitatively assess the influence of data quality on olivine detection and compositional interpretation from IMIR data of 29 spectra of pure olivine of synthetic, terrestrial, lunar, and Martian origins, as well as 5 spectra of lunar pyroclastic beads measured as bulk samples. First, we demonstrate the effectiveness of the feature fitting algorithm in the interpretation of IMIR olivine spectra, predicting olivine composition with an average error of 6.4 mol% forsterite across all test spectra using laboratory‐quality data. We then extend this analysis to degraded test spectra with reduced SNRs and sampling rates and find a range of data qualities required to predict olivine composition within ±11 Mg# (molar Mg/[Mg + Fe] × 100) for the test spectra explored here. Spectra for the sample most relevant to lunar exploration, an Apollo 74002 drive tube consisting of microcrystalline olivine and glass‐rich pyroclastics, required SNRs ≥ 200 for sampling rates ≤25 nm to predict composition within ±11 Mg# of the sample's true composition. Derived limits on SNRs and sampling rates will serve as valuable inputs for the development of IMIR spectrometers, enabling comprehensive knowledge of olivine composition on the lunar surface.

Keywords

• infrared
• spectroscopy
• signal‐to‐noise ratio
• spectral resolution
• feature fitting