Mercury: explorative geological maps through unsupervised learning

The mapping of planetary surfaces represents a fundamental activity in planetary science, offering invaluable insights into the formation history, surface processes, and compositional variations of celestial bodies. In addition, accurate and detailed mapping are crucial for tasks ranging from identifying potential landing sites to planning future exploration missions. These maps are primarily constructed from visible image data sets, providing topographic and albedo information which is mostly used to delineate and define the stratigraphy of geomorphological units (i.e., morpho-stratigraphic maps). However, the creation of such maps requires the specialized knowledge of expert planetary scientists and constitutes a time-intensive and highly complex task. In addition, often these maps rely solely on a geomorphology‐led approach overlooking meaningful details about composition (i.e., multispectral data) and physical properties of the defined units, with spectral information usually supplementing rather than informing geomorphological data.

This work aims to create the first set of global, explorative classification maps of Mercury’ surface which incorporate both spectral and morpho-stratigraphic information using an unsupervised learning approach based on Gaussian Mixture Models. This work represents an ambitious and promising approach for facilitating the generation of comprehensive geological maps.

In addition, this classification will facilitate geological interpretation and enhance the mapping of the planet's unexplored regions, while enriching the understanding of already surveyed regions. Such advancements are pivotal for unraveling the complexities of Mercury's surface, contributing significantly to our understanding of the planet in anticipation of the new wave of data expected from SIMBIO-SYS (Cremonese et al., 2020) data on the BepiColombo's mission (Benkhoff et al., 2021)