Data collected by space probes and Earth-based observations allow to investigate multiple aspects of planetary bodies in the Solar System. Typical spatial data utilized for geological and geophysical global scale investigations include imagery basemaps, digital elevation models, feature density maps, geochemical, magnetic and gravity anomaly maps. Different datasets often present local correlations, and their simultaneous use can help unveil the wide diversity of geological and geophysical processes that contribute to the planetary evolution. A geostatistical approach can be applied to quantify the level of correlation and anticorrelation between the different datasets. We developed a method based on a linear regression analysis of multiple planetary data, producing georeferenced maps that can be imported into a Geographic Information System (GIS) environment. We show how this geostatistical method can be used to quantify the level of correlation between different spatial data, and to investigate the geological and geophysical processes of planetary bodies. We present the results of the geostatistical method for the Mercury science case, using datasets returned by the MESSENGER mission.

Triton is one of the few satellites in the Solar System which shows an ongoing geological activity, with plumes and geysers whose origin is still controversial [1]. Its surface is relatively young, as shown by the paucity of craters detected on its surface [2]. Our knowledge of this moon comes from the Voyager 2 mission, which obtained several images, covering about 40% of Triton’s surface [3]. However, few studies were focused principally on surficial geomorphology, and those are mostly limited to the cantaloupe region and surrounding areas. Triton’s crust is composed predominantly by solid nitrogen (N2) but several other ices have been detected [3]. Crater counting has revealed that the surface is very young and likely it went through a resurfacing process in the past. In fact, a very small number of craters has been detected, and these usually exhibit a typical bowl-like shape [2]. Geological features on Triton include regions, called terrains, such as cantaloupe terrains or plains, which show different textures. Usually, plains are categorized within smooth, walled and terraced plains [3]. The latter are the flattest areas on Triton, a characteristic which has been explained by evoking a lava-like or other viscous liquid infill. Their central depressions also present a cluster of irregular pits, which have been interpreted as drainage pits or eruptive vents [3]. These peculiar morphologies seem to indicate the presence of a viscous fluid on the surface in a remote epoch, which may imply potential climatic and atmospheric changes during Triton’s geological history. In this work we analyse an area located at NW of Tuonela Planitia, which shows several depressions rimmed by sharp margins. Two of these depressions are named Kulilu Cavus and Mah Cavus [4]. Cavi are elliptical-shaped depressions, distributed in an ordered trend, which constitute the cantaloupe terrain [3]. Diapirism is the main candidate process to explain these collapsed depressions [3] but other hypotheses, such as cryovolcanism or impact cratering [5], have also been proposed. Methodology A new geological map has been realized. We used Voyager 2 imagery named c1139533 [6] (600 m/px), properly calibrated, filtered and georeferenced using the Integrated Software for Imagers and Spectrometers (ISIS4) [7]. We mapped the different geological units and main features according to differences in surface morphology (fig.1). We also produced a DEM of the study area, using the open-source suite of tools NASA Ames Stereo Pipeline (ASP) [8]. We applied the photoclinometry-based “shape-from-shading” (SfS) tool to produce the DEM. Since SfS needs an input DEM generated preferably with stereo images, and we do not have such data for Triton, we used the methodology proposed by Lesage et al. 2021[9]. We analysed four different cross sections to measure the relative height of Kulilu Cavus, Mah Cavus and two other depressions, as well as their associated terraces (fig.2). Discussion Geologic

Introduction The complex geomorphology of Triton reflects its geological history [1]. The morphological heterogeneity on small scale has led to discerning three main complexes: cantaloupe terrains, equatorial plains, and south polar cap terrains. We analyse an area located in the east equatorial zone of Triton called Monad Regio (centred at 37°N, 2°E), characterized by the presence of walled plains. We produced a new geological map and a DEM (Digital Elevation Model) to recognize the main terrains and features in the study area. We used Voyager 2 imagery named c1139533 (600 m/px) [2], properly calibrated, filtered, and georeferenced using the Integrated Software for Imagers and Spectrometers (ISIS4) [3]. Results and conclusions We mapped the different geological units and main features according to differences in surface morphology (Fig.1). Terraced terrain covers most of the studied area. It shows a chaotic pattern characterized by several terraces, some of which lay in a parallel arrangement around some of the large depressions. These basins have areas ranging from 1300 to 2050 km2, and their degree of alteration is variable, with the features inferred to be more recent showing an inner minor basin within the main one. The most altered basins appear smoother, featureless, and shallower. Sizes and excavation depths estimated using DEM data of the observed basin features appear to be relatively homogeneous, which leads us to exclude an impact related origin. We argue that the origin of these depressions is linked to processes analogue to those described in the formation of terrestrial maar craters and possible explosion craters discussed on Titan [4]. Alternatively, diapirism may also explain the origin of such features. Further analysis could help to understand the nature and related processes that originated these basins. Acknowledgements G.M., C.C. and D.S. acknowledge support from the Italian Space Agency (2020-13-HH.0). References [1] Basilevsky A.T. et al.,1992. Adv. Space Res.,12(11), 123-132. [2] Smith, B. A., et al. (1989), Science, 246 (4936), 1422-1449. [3] Houck J.C. and DeNicola L.A. (2000) Astronomical Data Analysis Software and Systems IX, ASP Conference Series,216. [4] Mitri G., et al. (2019), Nature Geoscience, 12, 791,796.