Towards Automated Identification and Characterization of Impact Craters on Mercury for Future BepiColombo BELA Observations

Towards Automated Identification and Characterization of Impact Craters on Mercury for Future BepiColombo BELA Observations

   Impact craters preserve key information about the geological evolution, surface age, and crustal structure of planetary bodies, making their identification and characterization an important objective in planetary remote sensing. On Mercury, interpretation of crater morphology is complicated by widespread volcanic resurfacing and modification, which can embay crater rims, infill interiors, and obscure older basin structures (Du et al., 2020; Bertoli et al., 2024; Golder et al., 2025). Laser altimetry from the MESSENGER Mercury Laser Altimeter (MLA) has provided the first detailed topographic view of Mercury’s northern hemisphere (Zuber et al., 2012) and enabled quantitative investigation of crater and basin morphology.

   Building on previous morphometric studies of Mercurian craters (Susorney et al., 2016) and our earlier work using RANSAC-based surface fitting and geomorphological analysis of laser-altimetry point clouds (Arghavanian et al., 2024; Arghavanian et al., 2025), this study investigates how crater-related topographic signatures can be identified directly from MLA-derived digital elevation models and point clouds. The analysis focuses on cross-sectional geometry (Arghavanian & Leloglu, 2024) e.g. diameter, D/H, curvature, elevation residuals, and roughness (Nishiyama et al., 2026) characteristics as diagnostic indicators for distinguishing large crater (>10 km) (Herrick et al., 2018) structures from surrounding terrain and for evaluating possible volcanic overprinting.

   The aim is to determine which topographic parameters are most effective for recognizing impact-crater morphology in Mercury altimetry data, and to establish a framework for identifying craters that may be especially suitable for future compositional and mineralogical investigation. In this way, crater detection is treated not only as a geomorphological problem, but also as a step toward identifying impact structures that may reveal distinctive subsurface materials or compositionally unusual units. Also, we are optimistic to automate the process potentially by combining these geomorphological features with machine-learning–based methods. This framework is intended as a preparatory step for application to higher-resolution observations from the BepiColombo Laser Altimeter (BELA) (Thomas et al., 2021), which will provide new opportunities to refine the identification and characterization of complex impact craters and to assess their relationships with volcanic resurfacing, composition, and other surface processes.

References

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Azar Arghavanian, Oliver Stenzel, Martin Hilchenbach, 2025, Hermean curvature-based geomorphic feature classification using Laser Altimetry data, EPSC2025, Helsinki, Finland.

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