Mapping Planetary Surface Ages at Ultimate Resolutions with Machine Learning: The Moon

The density of impact craters upon a terrestrial surface can give an accurate estimate of the surface's formation age. The Moon has hundreds of millions of impact craters scattered across its surface. Through the power of machine learning, we can automatically count those craters to date any surface on the Moon

Automated Impact Crater Detection and Characterization Using Digital Elevation Data

Impact craters are used as subjects for the remote study of a wide variety of surface and subsurface processes throughout the solar system. Their populations and shape characteristics are collected, often manually, and analysed by a large community of planetary scientists. This research investigates the application of automated methods for both the detection and characterization of impact craters on the Moon and Mars, using machine learning techniques and digital elevation data collected by orbital spacecraft. We begin by first assessing the effect of lunar terrain type variation on automated crater detection results. Next, we develop a novel automated crater degradation classification system for martian complex craters using polynomial profile approximation. This work identifies that surface age estimations and crater statistics acquired through automatic crater detection are influenced by terrain type, with unique detection error responses. Additionally, we demonstrate an objective system that can be used to automate the classification of crater degradation states, and identify some potential areas of improvement for such a system

In-Situ Radar Observation of Shallow Lunar Regolith at the Chang’E-5 Landing Site : Research Progress and Perspectives

Funding Information: This work is supported by the National Natural Science Foundation of China (Grant No. 42241139 and 42004099), the Opening Fund of the Key Laboratory of Lunar and Deep Space Exploration, Chinese Academy of Sciences (No. LDSE202005), the National Innovation and Entrepreneurship Training Program for College Students (No. 202310590016), the Fund of Shanghai Institute of Aerospace System Engineering (No. PZ_YY_SYF_JY200275), and the Shenzhen Municipal Government Investment Project (No. 2106_440300_04_03_901272).Peer reviewedPublisher PD

The Moon Zoo citizen science project: preliminary results for the Apollo 17 landing site

Moon Zoo is a citizen science project that utilises internet crowd-sourcing techniques. Moon Zoo users are asked to review high spatial resolution images from the Lunar Reconnaissance Orbiter Camera (LROC), onboard NASA’s LRO spacecraft, and perform characterisation such as measuring impact crater sizes and identify morphological ‘features of interest’. The tasks are designed to address issues in lunar science and to aid future exploration of the Moon. We have tested various methodologies and parameters therein to interrogate and reduce the Moon Zoo crater location and size dataset against a validated expert survey. We chose the Apollo 17 region as a test area since it offers a broad range of cratered terrains, including secondary-rich areas, older maria, and uplands. The assessment involved parallel testing in three key areas: (1) filtering of data to remove problematic mark-ups; (2) clustering methods of multiple notations per crater; and (3) derivation of alternative crater degradation indices, based on the statistical variability of multiple notations and the smoothness of local image structures. We compared different combinations of methods and parameters and assessed correlations between resulting crater summaries and the expert census. We derived the optimal data reduction steps and settings of the existing Moon Zoo crater data to agree with the expert census. Further, the regolith depth and crater degradation states derived from the data are also found to be in broad agreement with other estimates for the Apollo 17 region. Our study supports the validity of this citizen science project but also recommends improvements in key elements of the data acquisition planning and production

Mineralogical analysis and iron abundance estimation of the Moon using the SIR-2, HySI and M3 spectrometers on-board the munar orbiter chandrayaan-1

The work presented in this thesis is focused on mineralogical studies of the Moon aiming to create maps of iron abundances. We used the data from visible to near-infrared (VISNIR) spectrometers on-board Chandrayaan-1 spacecraft, with our major concentration on the Spectrometer InfraRed-2 (SIR-2) data. The SIR-2 on-ground and in-flight calibrations are discussed. The location of the SIR-2 tracks on the imaging spectrometers, Moon Mineralogy Mapper (M3), and Hyper-Spectral Imager (HySI) is determined by comparing the radiance profiles of the three instruments measured at the same Coordinated Universal Time (UTC) and the same photometric conditions...thesi

Summary of the Results from the Lunar Orbiter Laser Altimeter after Seven Years in Lunar Orbit

In June 2009 the Lunar Reconnaissance Orbiter (LRO) spacecraft was launched to the Moon. The payload consists of 7 science instruments selected to characterize sites for future robotic and human missions. Among them, the Lunar Orbiter Laser Altimeter (LOLA) was designed to obtain altimetry, surface roughness, and reflectance measurements. The primary phase of lunar exploration lasted one year, following a 3-month commissioning phase. On completion of its exploration objectives, the LRO mission transitioned to a science mission. After 7 years in lunar orbit, the LOLA instrument continues to map the lunar surface. The LOLA dataset is one of the foundational datasets acquired by the various LRO instruments. LOLA provided a high-accuracy global geodetic reference frame to which past, present and future lunar observations can be referenced. It also obtained high-resolution and accurate global topography that were used to determine regions in permanent shadow at the lunar poles. LOLA further contributed to the study of polar volatiles through its unique measurement of surface brightness at zero phase, which revealed anomalies in several polar craters that may indicate the presence of water ice. In this paper, we describe the many LOLA accomplishments to date and its contribution to lunar and planetary science

Morphometric analysis of differently degraded simple craters on the moon

The main focus of this PhD research is the morphologic characterization of simple impact craters on lunar maria in order to find out a correlation between craters morphological degradation and absolute model ages of the surfaces where they were emplaced. Crater degradation can be indeed used to constrain the chronological evolution of planetary surfaces. The crater degradation is usually retrieved through visual inspection by subdividing craters into 4 classes: C1 represents the freshest ones, C2 are the ones with the first evidence of degradation (smoothed rim), C3 and C4 are related to morphologies ranging from heavily eroded to totally flattened respectively [Arthur, 1963]. We firstly conducted a morphometric analysis of craters representative of the four classes starting from the freshest one represented by the Linné crater. Craters were chosen on a homogeneous geological unit, the S28 unit in mare Serenitatis, with an absolute model age of 2.84 Gy [Hiesinger et al., 2011]. This analysis allowed us to establish the thresholds of mean slope from craters inner wall, in order to constrain the morphometric characterization of the four degradation classes. Successively we have extracted all impact craters (383) from a unique geological unit and we have defined the morphologic relationships among the degradation classes in function of the craters diameters. Finally, we expanded our analysis to six lunar maria, considering six lunar maria with different average absolute model ages, in order to perform this analysis with the wider range of ages. For each mare we considered a unique surface (dataset) derived from the merging of geological units with similar absolute model ages within the basin, in order to guarantee the most homogeneous possible surfaces, both in terms of impact rheology and absolute age. From the six surfaces we have extracted inner wall mean slopes from over 1000 impact craters. The mean slope values of the inner walls have shown a relation between crater morphology and the absolute model ages of the geological units where they are located. Older basins are characterized by craters with lower mean slope values, suggesting a dominance of older craters in their population, whereas the younger units have shown higher mean slope values of their simple craters, suggesting a population dominated by recent impacts. This tendency is the expression of the morphological alteration strictly connected to the lunar maria age. Since the geomorphometry of impact craters is influenced by the absolute age of the target area, we have constrained potential isochrones by fixing absolute age thresholds based on the morphological variations of impact craters

Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods

Determining when an impact crater formed is a complex and tedious task. However, this knowledge is crucial to understanding the geological history of planetary bodies and, more specifically, gives information on erosion rate measurements, meteorite ejection location, impact flux evolution and the loss of a magnetic field. The derivation of an individual crater's age is currently performed through manual counting. Because crater size scales as a power law, this method is limited to small (and/or young) surface areas and, in the case of the derivation of crater emplacement age, to a small set of impact craters. Here, we used a Crater Detection Algorithm, specifically retrained to detect small impact craters on large‐ and high‐resolution imagery data set to solve this issue. We applied it to a global, 5 m/pixel resolution mosaic of Mars. Here, we test the use of this data set to date 10 large impact craters. We developed a cluster analysis tool in order to distinguish potential secondary crater clusters from the primary crater population. We then use this, filtered, crater population to date each large impact crater and evaluate our results against literature ages. We found that automated counting filtered through clustering analysis produced similar model ages to manual counts. This technique can now be expanded to much wider crater dating surveys, and by extension to any other kind of Martian surface. We anticipate that this new tool will considerably expand our knowledge of the geological events that have shaped the surface of Mars, their timing and duration