Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury's exospheric data
Surface information derived from exospheric measurements at planetary bodies complements surface mapping provided by dedicated imagers, offering critical insights into surface release processes, interactions within the planetary environment, space weathering, and planetary evolution. This study explores the feasibility of deriving Mercury's regolith elemental composition from in-situ measurements of its neutral exosphere using deep neural networks (DNNs). We present a supervised feed-forward DNN architecture - a multilayer perceptron (MLP) - that, starting from exospheric densities and proton precipitation fluxes, predicts the chemical elements of the surface regolith below. It serves as an estimator for the surface-exosphere interaction and the processes leading to exosphere formation. Because the DNN requires a comprehensive exospheric dataset not available from previous missions, this study uses simulated exosphere components and simulated drivers. Extensive training and testing campaigns demonstrate the MLP's ability to accurately predict and reconstruct surface composition maps from these simulated measurements. Although this initial version does not aim to reproduce Mercury's actual surface composition, it provides a proof of concept, showcasing the algorithm's robustness and capacity for handling complex datasets to create estimators for exospheric generation models. Moreover, our tests reveal substantial potential for further development, suggesting that this method could significantly enhance the analysis of complex surface-exosphere interactions and complement planetary exosphere models. This work anticipates applying the approach to data from the BepiColombo mission, specifically the SERENA package, whose nominal phase begins in 2027.