Glinka crater on Mercury: a spectral and morphological analysis

The surface of Mercury has been extensively altered by space weathering and impact processes, making it challenging to identify the boundaries of geological units. We analyzed the Glinka crater in the Beethoven quadrangle (H-07), a region characterized by notable spectral and geological variability, including impact craters, a possible pyroclastic vent, hollows, and compressive structures. To delineate morphological boundaries, we integrated high-resolution imaging, spectral data, and topographic products.

For morphological mapping, we produced monochromatic mosaics at 121 m/px, 56 m/px, and 14 m/px resolutions using MESSENGER MDIS/NAC data. Spectral investigations utilized an eight-filters MDIS/WAC-derived multispectral image (268 m/px). Additional datasets including Digital Elevation Model (DEM, 222 m/px), roughness and shading maps, and gravity data. Data processing involved the Integrated Software for Imagers and Spectrometers (ISIS3), applying the Kaasalainen-Shkuratov photometric correction model considering the parameters derived by Domingue et al. (2016). Spectral unit identification relied on four parameters: Reflectance at 750 nm (R750), Global Spectral Slope between 430 and 1000 nm (S430-1000), IR Slope ranging between 750 and 1000 nm (S750-1000), and UV Slope between 430 and 560 nm (S430-560). Threshold values for these parameters were determined through supervised k-means clustering (k=4), resulting in maps showing Regions of Interest (ROIs) for each spectral parameter. To combine all threshold values of the four parameters, an automated process generated a composite map with over 400 ROIs. Smaller ROIs (<15% of the average pixel count per ROI) were excluded, and those with similar values (∆10%) were merged iteratively, yielding seven final spectral units.

We are producing a geological map of the area by integrating data from the spectral map and high-resolution imagery. The spectral map highlights spectral variations and, in some cases, compositional differences. This integration enables a more precise definition of the boundaries between geological units. involves detailed geological and chronostratigraphic interpretations involves the exploration of various RGB combinations to extract additional information. This analysis includes spectral parameter values for each unit, taking into account surface morphology and texture, which may influence spectral responses without necessarily indicating compositional differences.

Domingue D. L. et al. (2016) Icarus 268, 172-203. https://doi.org/10.1016/j.icarus.2015.11.040

 Acknowledgements:  M.I. and G.M. acknowledges support from the Italian Space Agency (2022-16-HH.1-2024).