Impacts drive lunar rockfalls over billions of years

Abstract

Past exploration missions have revealed that the lunar topography is eroded through mass wasting processes such as rockfalls and other types of landslides, similar to Earth. We have analyzed an archive of more than 2 million high-resolution images using an AI and big datadriven approach and created the first global map of 136.610 lunar rockfall events. Using this map, we show that mass wasting is primarily driven by impacts and impact-induced fracture networks. We further identify a large number of currently unknown rockfall clusters, potentially revealing regions of recent seismic activity. Our observations show that the oldest, pre-Nectarian topography still hosts rockfalls, indicating that its erosion has been active throughout the late Copernican age and likely continues today. Our findings have important implications for the estimation of the Moon's erosional state and other airless bodies as well as for the understanding of the topographic evolution of planetary surfaces in general.

Global Drivers and Transport Mechanisms of Lunar Rockfalls

“…All data sets used for the local study are either in a regular equirectangular projection (Moon 2000) or a polar stereographic projection (Moon 2000), depending on the geographic location of the area of interest, as further discussed in Section 3.1 and 3.2. Bickel, Jordan, et al (2020) created a global catalog of lunar rockfalls using a CNN, which mapped 136,610 rockfalls. This data set consists of point locations of each mapped rockfall, scattered across the surface of the Moon from ∼70°N to ∼70°S, as well as CNN-derived meta annotations, such as estimated boulder diameter.…”

“…In contrast, a mismatch of both distributions could indicate that this particular geophysical property does have an influence on the rockfall distribution. To do so, we used the rockfall catalog derived by Bickel, Jordan, et al (2020) to extract the values (attributes) of each global map at each rockfall location. We refer to the distributions of these extracted attributes (values derived at all rockfall locations) as "distribution of data set <type> at-target," or short "at-target."…”

“…For (b), we first derived the distances between the 28 original Apollo shallow moonquake epicenter locations (Nakamura et al, 1979), the 12 recently confirmed, re-located Apollo-era epicenter locations (Watters et al, 2019) and all rockfalls mapped by Bickel, Jordan, et al (2020). We then used three different spatial buffers (0.5°, 1°, and 5° radius around each epicenter, i.e., ∼15, ∼30, and ∼150 km at the equator, respectively), and determined the number of rockfalls within each buffer.…”

“…In order to further study the characteristics of rockfalls on a local scale we selected and performed measurements in 13 areas of interest (AoIs) across the Moon (Bickel, 2021). These specific locations have been selected because they are rockfall hotspots and belong to one of the four relevant lunar geomorphic (sub) classes as outlined by Bickel, Jordan, et al (2020): crater (impact-caused, -induced, and -ejected), volcanic vent, tectonic structure, or other, unclassified geomorphic context. We included an increased number of impact-related AoIs because the vast majority of rockfalls are located in impact craters (Bickel, Jordan, et al, 2020).…”

“…These specific locations have been selected because they are rockfall hotspots and belong to one of the four relevant lunar geomorphic (sub) classes as outlined by Bickel, Jordan, et al (2020): crater (impact-caused, -induced, and -ejected), volcanic vent, tectonic structure, or other, unclassified geomorphic context. We included an increased number of impact-related AoIs because the vast majority of rockfalls are located in impact craters (Bickel, Jordan, et al, 2020). We manually mapped 687 rockfalls in these AoIs, where we expect that an estimated ∼60%-70% of these manually mapped features are also present in the global rockfall catalog derived by Bickel, Jordan, et al (2020) used for the global study, based on the recall (percentage of rockfalls found in a testset) of the deployed CNN (Bickel et al, 2018).…”