This study demonstrates the synergies and limits of multiple measurement types for the detection of smectite chemistry and oxidation state on planetary surfaces to infer past geochemical conditions. Smectite clay minerals are common products of water-rock interactions throughout the solar system, and their detection and characterization provides important clues about geochemical conditions and past environments if sufficient information about their composition can be discerned. Here, we synthesize and report on the spectroscopic properties of a suite of smectite samples that span the intermediate compositional range between Fe(II), Fe(III), Mg, and Al end-member species using bulk chemical analyses, X-ray diffraction, Vis/IR reflectance spectroscopy, UV and green-laser Raman spectroscopy, and Mössbauer spectroscopy. Our data show that smectite composition and the oxidation state of octahedral Fe can be reliably identified in the near infrared on the basis of combination and fundamental metal-OH stretching modes between 2.1–2.9 μm, which vary systematically with chemistry. Smectites dominated by Mg or Fe(III) have spectrally distinct fundamental and combination stretches, whereas Al-rich and Fe(II)-rich smectites have similar fundamental minima near 2.76 μm, but have distinct combination M-OH features between 2.24 and 2.36 μm. We show that with expanded spectral libraries that include intermediate composition smectites and both Fe(III) and Fe(II) oxidation states, more refined characterization of smectites from MIR data is now possible, as the position of the 450 cm–1 absorption shifts systematically with octahedral Fe content, although detailed analysis is best accomplished in concert with other characterization methods. Our data also provide the first Raman spectral libraries of smectite clays as a function of chemistry, and we demonstrate that Raman spectroscopy at multiple excitation wavelengths can qualitatively distinguish smectite clays of different structures and can enhance interpretation by other types of analyses. Our sample set demonstrates how X-ray diffraction can distinguish between dioctahedral and trioctahedral smectites using either the (02,11) or (06,33) peaks, but auxiliary information about chemistry and oxidation state aids in specific identifications. Finally, the temperature-dependent isomer shift and quadrupole splitting in Mössbauer data are insensitive to changes in Fe content but reliability differentiates Fe within the smectite mineral structure.

Airspace models have played an important role in the development and evaluation of aircraft collision avoidance systems for both manned and unmanned aircraft. As Urban Air Mobility (UAM) systems are being developed, we need new encounter models that are representative of their operational environment. Developing such models is challenging due to the lack of data on UAM behavior in the airspace. While previous encounter models for other aircraft types rely on large datasets to produce realistic trajectories, this paper presents an approach to encounter modeling that instead relies on expert knowledge. In particular, recent advances in preference-based learning are extended to tune an encounter model from expert preferences. The model takes the form of a stochastic policy for a Markov decision process (MDP) in which the reward function is learned from pairwise queries of a domain expert. We evaluate the performance of two querying methods that seek to maximize the information obtained from each query. Ultimately, we demonstrate a method for generating realistic encounter trajectories with only a few minutes of an expert's time.

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