用深度神经网络从水星大气数据反推地表成分，为未来探测提供新方法。
Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury's exospheric data
- 构建多层感知机模型，输入大气密度和质子通量，输出地表元素组成。
- 在模拟数据上验证，能准确重建表面成分分布图，误差可控。
- 适合从事行星表面-大气相互作用研究的科研人员，尤其关注水星探测者。
通过行星体大气测量获取的地表信息，可补充成像设备提供的表面地图，对理解地表释放过程、行星环境相互作用、空间风化及行星演化具有重要意义。本研究探索利用深度神经网络（DNN）从水星中性大气的原位观测数据中反演其风化层元素组成。提出一种监督式前馈型多层感知机（MLP）架构，以大气密度与质子沉积通量为输入，预测下方地表岩石化学成分，作为地表-大气相互作用及大气形成机制的估计器。由于此前任务未提供完整大气数据集，研究采用模拟的大气组分与驱动因素进行训练与测试。大规模实验表明，该MLP能准确重建模拟测量下的表面成分分布图。尽管当前版本未复现真实水星地表组成，但已验证算法鲁棒性与处理复杂数据的能力，为建立大气生成模型估算器提供可行性证明。测试还揭示了进一步优化潜力，有望显著提升对复杂地表-大气相互作用的分析能力，并补充行星大气模型。该方法将应用于2027年启动的贝皮科伦布任务（BepiColombo）SERENA科学包数据。
原文摘要 · Abstract (English)
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.
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