Investigating Low-Reflectance Material (LRM) on Mercury using an integrated spectro-morphologic clustering approach

Introduction

Low-Reflectance Material (LRM) represents a widespread surface unit on Mercury, typically occurring as basin ejecta and characterized by the lowest reflectance values among the planet’s surface materials (e.g., Peplowski et al., 2015; Klima et al., 2018). LRM deposits are predominantly of pre-Tolstojan age (>3.9 Ga; Denevi et al., 2018), and their compositional modeling suggests they may contain up to ~4 wt% graphite, a conclusion supported by both reflectance analyses and modeled gamma ray and neutron spectra (Peplowski et al., 2016; Klima et al., 2018). These findings support the hypothesis that LRM may be remnants of an early graphite-rich crust (Vander Kaaden & McCubbin, 2015; 2018), later excavated and redistributed across the surface by large impact events (Lark et al., 2023).

Method and Analytical approach

Previous efforts to map and characterize LRM have largely relied on spectral techniques, using combinations of reflectance ratios and principal component analyses to delineate their distribution (e.g., Peplowski et al., 2015; Klima et al., 2018). While these approaches have yielded important constrains on LRM distributions and possible compositional information, they offer limited integration with the morphological and stratigraphical context of the surface.

In contrast to terrestrial geology, where lithology, morphology, and in situ measurements are jointly assessed, in general, planetary investigations must rely exclusively on remote sensing data. Given these observational constraints, integrated methodologies that combine compositional (i.e., spectral) and morphological data are essential for inferring geologically meaningful information from remote observations.

To address this need, we previously developed an unsupervised learning framework for generating exploratory classification maps of Mercury’s surface, integrating spectral and topographic inputs into multidimensional data cubes (Figure 1, see also Vergara Sassarini et al., 2025). As a case study, we selected the Hokusai quadrangle and used as a starting dataset the 665 m/px DTM (Becker et al., 2016) and the 8-color map (Zambon et al., 2022) to build 3 different cubes: morphological (DEM + TRI-ruggedness), spectral (430-1000nm + 750-1000nm + 430-560nm spectral slopes +  750nm band), and a morpho-spectral cube that contains both spectral and morphology data. These cubes were processed using a Gaussian Mixture Model based approach to derive surface classes that reflect both compositional and morphological characteristics of the surface.  

Figure 1. A. Workflow for the generation of exploratory classification maps based on unsupervised clustering and B. morpho-spectral predictions for H05 quadrangle (Vergara Sassarini et al., 2025).

From the derived morpho-spectral map (Figure 1B), we identified several clusters that spatially correspond to known LRM units, as observed in enhanced color imagery (Figure 2A). Notably, the LRM-related clusters exhibit internal diversity (Figure 2B), suggesting potential compositional heterogeneities.

Figure 2. Comparison between LRM (blue colors) in A. Mercury Dual Imaging System (MESSENGER) enhanced color 665 m/pixel global mosaic (Denevi et al., 2016) and B. Isolated clusters from Vergara Sassarini et al., 2025.

To investigate these heterogeneities, this ongoing work will include a detailed analysis of the reflectance spectra across the derived LRM-related clusters to test for relatively compositional differences among them. We also aim to refine the input cubes used during fitting by including additional spectral ratio layers or PCA-derived features that may improve the clustering sensitivity to subtle spectral variations. As a future application, we intend to apply this integrated and tailored classification model to selected regions of interest on other regions of the planet different from Hokusai Quadrangle, with the goal of producing a global-scale distribution map of LRM, investigating their possible heterogeneity highlighted by this preliminary investigation. Such automated, multi-dimensional classification will be essential for identifying regions of interest for detailed investigation with the high-resolution data expected from SIMBIO-SYS (Cremonese et al., 2020) data on the BepiColombo's mission (Benkhoff et al., 2021).

 

Acknowledgments

This study has been supported from the Italian Space Agency (ASI) under ASI-INAF agreement 2024-18-HH.0.

References

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