Analysis of Lunar Boulder Tracks: Implications for Trafficability of Pyroclastic Deposits

Abstract

In a new era of lunar exploration, pyroclastic deposits have been identified as valuable targets for resource utilization and scientific inquiry. Little is understood about the geomechanical properties and the trafficability of the surface material in these areas, which is essential for successful mission planning and execution. Past incidents with rovers highlight the importance of reliable information about surface properties for future, particularly robotic, lunar mission concepts. Characteristics of 149 boulder tracks are measured in Lunar Reconnaissance Orbiter Narrow Angle Camera images and used to derive the bearing capacity of pyroclastic deposits and, for comparison, mare and highland regions from the surface down to ~5‐m depth, as a measure of trafficability. Results are compared and complemented with bearing capacity values calculated from physical property data collected in situ during Apollo, Surveyor, and Lunokhod missions. Qualitative observations of tracks show no region‐dependent differences, further suggesting similar geomechanical properties in the regions. Generally, bearing capacity increases with depth and decreases with higher slope gradients, independent of the type of region. At depths of 0.19 to 5 m, pyroclastic materials have bearing capacities equal or higher than those of mare and highland material and, thus, may be equally trafficable at surface level. Calculated bearing capacities based on orbital observations are consistent with values derived using in situ data. Bearing capacity values are used to estimate wheel sinkage of rover concepts in pyroclastic deposits. This study's findings can be used in the context of traverse planning, rover design, and in situ extraction of lunar resources.

Using Boulder Tracks as a Tool to Understand the Bearing Capacity of Permanently Shadowed Regions of the Moon

“…Boulder tracks that crossed the boundary from sunlit regions into PSRs (e.g., M1117841678LE/RE) show no significant difference in track morphology, suggesting similar geomechanical properties ( Figure 5). Additionally, when compared to the tracks identified by Bickel et al (2019) in highland, mare, and pyroclastic regions (Figure 6), the tracks do not show any significant difference in morphology. Boulders rolling into PSRs penetrate sufficiently deep to leave measurable tracks but do not completely sink into the regolith as might be expected if the regolith was unusually porous and weak.…”

“…An analytical solution provided by Hansen (1970) for shallow foundations provides a better and more realistic estimate of bearing capacity, because it allows for the adaptation of the bearing capacity estimation to the local slope and boulder shape . Here we apply the Hansen (1970) equation for bearing capacity, q f , with the same assumptions used by Bickel et al (2019) for other regions of the Moon:…”

“…Estimated bearing capacity values for each terrane (PSRs, highland, mare, and pyroclastics) were plotted with respect to the slope angle where the measurements were taken. The data are presented as distinct ranges for depth of track (≤1 m, 1 to 2 m, and 2 to 6 m) comparable to those of Bickel et al (2019). A least squares fit was applied with a shaded error bar derived from an estimate of the standard deviation of the error (Figure 7).…”

Deep Learning-Driven Detection and Mapping of Rockfalls on Mars

“…Besides their implications for weathering processes and seismic activity of Mars, rockfalls and their tracks can be used to infer the mechanical properties of regolith, as demonstrated on the Moon by e.g. [30] during the Apollo era and recently by [22][23], directly informing future surface exploration efforts. A deep learning-enabled global study of rockfall distribution on Mars could provide valuable information for a wide variety of relevant scientific and exploration-related applications.…”

“…Thus, suspected regions of increased seismic activity inferred from rockfall frequency patterns could be priority targets for the deployment of future geophysical networks. The tracks created by extraterrestrial rockfalls, i.e., boulder tracks, are also a valuable tool to estimate the basic mechanical properties of the surface substrate present [22][23]. However, the manual detection and mapping of martian rockfalls (boulders with tracks, here also called feature of interest) in satellite imagery remains a challenging task.…”