Exploring the diversity in pyroclastic deposits and volcanic vents on Mercury with machine learning techniques

Evidence of explosive volcanism on the surface of Mercury has been identified in the form of vents and pyroclastic deposits using images and spectral data acquired by the MESSENGER mission (Goudge et al. 2014, Thomas et al. 2014, Jozwiak et al. 2018, Pegg et al. 2021). Understanding the history of the volcanic eruptions forming these features provides an insight in the geological and thermal evolution of the planet. To this end, it is important to constrain the characteristics of each vent and, correlating them with the environment, classify the features according to their age and geological conditions. An individual analysis of a selection of vents has been carried out by Barraud et al. 2021 and Besse et al. 2015, providing new insights on the size, volcanic content and spectral properties of these features. However, performing a global analysis presents further challenges.  The collection of volcanic features identified presents a wide variety of characteristics in terms of morphology (simple vent, pit vent, vent-with-mound etc.), shape (circular, elliptical, curved), location (crater centre, crater rim, inter-crater plain), distribution (isolated or compound) and spectral properties of the pyroclastic deposit. This introduces a large number of variables that complicate the characterisation and timing of volcanic eruptions. 

The vast amount of data returned by the MESSENGER mission offers both a challenge and an opportunity in the methodology to solve this problem. While the combination of a large number of observations from different instruments can complicate the physical interpretation of a given process, it opens the door to the use of machine learning techniques. These methods rely on the identification of patterns on the input data without considering the associated physics, with the aim to reveal underlying correlations that can then be related to physical and chemical phenomena. This technique has been applied to the entire dataset collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS), to classify the visible-near-infrared reflectance spectra into three categories (D'Amore et al. 2022). 

In this work, we investigate the application of machine learning to explore the differences amongst the pyroclastic deposits and volcanic vents, with the aim of improving the understanding on the evolution of explosive volcanism in Mercury. In this methodology we combine data from the MASCS and the Mercury Laser Altimeter (MLA) instruments with other properties of the vent surroundings (e.g., crustal thickness). By treating unrelated physical variables together as components of the same input vector, the outcome is a set of dimensions that have no direct physical meaning but can uncover underlying structures to be later physically or chemically interpreted.