Field studies at terrestrial analogue sites represent an important contribution to the science of ocean worlds. The value of the science and technology investigations conducted at field analogue sites depends on the relevance of the analogue environment to the target ocean world. We accept that there are no perfect analogues for many of the unique environments represented by ocean worlds but suggest that a one-to-one matching of environmental characteristics and conditions is not crucial to the success or impact of the work. Instead, we must instead determine which processes and parameters are required to map directly to the target ocean world environment with high fidelity to address the science question or engineering challenge. Where there are discrepancies between the model and target environment, we must fully understand how those limitations impact the applicability of the study, and mitigate these where possible using alternative approaches. Here we present a two-step approach to 1) identify the most crucial processes and parameters associated with a given science question and 2) assess the fidelity of these processes and parameters at a proposed field site to those expected for the target ocean world. We demonstrate this approach in a test case evaluating three types of ocean world analogue environments with respect to a science question. Our proposed framework will not only enhance the scientific rigor of field research but also provide access to a broader range of field sites relevant to ocean worlds processes, enabling a greater diversity of ocean and geological science researchers.

The precise spectroscopic identification of mineral polytypes and specific organic molecules is key to understanding planetary processes and the potential for life beyond Earth in the solar system. For in situ exploration, Raman spectroscopy has been chosen for the NASA Mars Perseverance Rover and upcoming ESA ExoMars missions because it is an information-rich, non-contact, non-destructive method for identifying and characterizing compounds. Misinterpretation of Raman spectra can result in the misidentification of key information used to reconstruct environmental regimes or the detection of potential biosignatures. Machine learning can provide a means to disentangle the mixed signatures that occur in spectra from heterogenous targets by building algorithms capable of discerning subtle differences. Here we discuss an approach that incorporates a Matlab-based machine learning algorithm to study individual mineral samples as a starting point for more complex algorithms targeted for rocks and sediments. The present study focuses on Raman spectroscopy using visible (VIS) excitation laser (514 nm and 532 nm) and a near IR (NIR) excitation laser (at 780 nm) of an assortment of mineral samples typical for rocks on Mars and the Moon, namely olivines, three types of plagioclase minerals (anorthite, bytownite, labradorite), and pyroxenes (augite and enstatite). We have also begun to study the effect of temperature on the vibrational modes for the same mineral samples over a temperature range 300 – 473 K under NIR excitation. Our preliminary data show, for example, that olivine samples from two different locations may exhibit the same typical symmetric and asymmetric stretch and bending vibrations for forsterite (Mg2SiO4); however, under increasing temperatures the peak intensities of ~ 820 cm-1 and ~ 845 cm-1 features exhibited by each sample differed. Our results also showed an enhancement of the Raman peak intensity for plagioclase samples as the temperature increased up to 373K, but a decrease at temperatures beyond that. *Acknowledgments: P. Misra and R. Coleman, Jr. acknowledge support from NASA Award # 80NCCS20M0019, NSF Award # PHY 1950379 & Howard University IDCR # U100189; and D. Bower would like to acknowledge the support of the Internal Research and Development and Fundamental Laboratory Research Programs at NASA Goddard.