Lunar Impact Craters Research Articles

Global Lunar Crater Density Using Buffered Nonsparseness Correction

The density of craters on a planetary surface directly relates to the age of the surface. As the surface ages, however, craters can be erased by subsequent large impacts via direct overprinting, known as geometric crater obliteration. Such counts become increasingly limited as surfaces become more heavily cratered. Techniques to infer the statistics of the regions obliterated by craters were developed in the past decade. Such techniques, however, have only been used for regional studies. Herein, we present a study of the global density of lunar impact craters ≥20 km in diameter using both traditional crater-counting and buffered nonsparseness correction (BNSC) crater-counting techniques. By comparing the measurements, we quantify the influence of geometric crater obliteration on the visible lunar crater record. Our results reveal that geometric crater obliteration erased up to three-fifths of craters ≥20 km in diameter that formed on the most ancient lunar terrains, whereas younger surfaces, like the Procellarum KREEP Terrane, show little to no evidence of such crater obliteration. The differences in derived crater densities highlight ancient surfaces in which the effects of geometric crater obliteration must be considered to characterize their cratering histories. Furthermore, our results identify the most heavily cratered area on the Moon, a region of the lunar highlands between Smythii basin and the South Pole–Aitken (SPA) basin (Smythii–SPA–Highlands); the number of impacts revealed by the BNSC technique for this region is consistent with estimates derived from the abundance of highly siderophile elements and from modeling crustal porosity.

Accurate Mapping and Evaluation of Small Impact Craters within the Lunar Landing Area

Impact craters, as the most distinct lunar structural unit and geological structure, are marked on the Moon’s surface. For over a decade, researchers have focused on identifying and exploring large- to medium-sized impact craters on the surface of the Moon (craters with a diameter greater than 1 km). Small impact craters have obvious statistical significance owing to their magnitude in numbers. The identification and analysis of small craters provide indispensable clues for the study of lunar geological evolution. However, such craters only remain in specific images and regions. At present, there is no comprehensive record of small impact craters in the existing lunar impact crater databases. The small impact craters on the surface of the Moon are enormous and vary in size by orders of magnitude, exhibiting small target characteristics in space. The present study focuses on the identification and spatial analysis of small impact craters on the surface of the Moon. A feature amplification strategy-based identification model was established for small impact crater detection, achieving accurate recognition of the small impact craters on the surface of the Moon (with a recall rate of 86.97% and a false-positive rate as low as 0.54% ± 0.16%). In total, 228,897, 142,872, and 42,008 new small lunar impact craters (with diameters as low as 4.5 m) were identified in the ten lunar landing areas of returned samples from the Apollo, Luna, and Chang’e-5 missions. In addition, the spatial distribution characteristics of small impact craters during different geological periods in the landing area are provided. Data on the newly identified small impact craters will provide an important basis for revealing the lunar impact fluxes and young lunar surface dating in lunar geological evolution research.

Quantitative Research on the Morphological Characteristics of Lunar Impact Craters of Different Stratigraphic Ages since the Imbrian Period

Impact craters serve as recorders of lunar evolutionary history, and determining the stratigraphic ages of craters is crucial. However, the age of many craters on the Moon remains undetermined. The morphology of craters is closely related to their stratigraphic ages. In the study, we systematically and quantitatively analyzed seven morphological parameters of 432 impact craters with known stratigraphic ages (Copernican, Eratosthenian, Imbrian), including crater depth, wall width, wall height, rim height, irregularity, volume, and roughness, as well as rock abundance. The study provided a range of morphological parameters for craters from the Copernican, Eratosthenian, and Imbrian. Additionally, we derived power law relationships between five morphological parameters and crater diameter, excluding irregularity and roughness. Furthermore, the transitional crater diameters from simple to complex crater morphology were determined for the Copernican and Eratosthenian, approximately 13 km and 15 km, respectively. These results suggest systematic differences in the lunar regolith in different stratigraphic ages. For impact craters of the same diameter, as crater age increases, irregularity tends to be greater, while crater depth, wall width, wall height, rim height, volume, roughness, and rock abundance tend to be smaller. Therefore, in cases where the diameter is determined, the actual values of morphological parameters and rock abundance can be used to constrain the stratigraphic age information of craters of an unknown age.

Magnetic signatures of lunar impact craters

The Moon almost certainly had an internally generated magnetic field in its past, but the duration of the dynamo, its temporal stability, and the surface field intensity are poorly known. Impact cratering events heat portions of the crust above the Curie temperature, allowing the ambient magnetic field strength to be recorded as the crater cools. We systematically analyzed the magnetic signatures of lunar impact craters with diameters greater than 90 km using recent magnetic field models and crater databases. Craters were classified as having evidence for impact-related central magnetization or demagnetization, and synthetic magnetic field models were used to estimate the number of incorrect identifications. In total, about 15% of craters were found to have impact-related magnetized or demagnetized signatures. The proportion of pre-Nectarian and Nectarian aged craters in the magnetized class is about 2%–3%, and there is little evidence for magnetized craters in the Imbrian and younger periods. The percentage of craters in the demagnetized class is about 0.3%–3% in the pre-Nectarian and Nectarian periods and abruptly increases to 16% in the Imbrian period. Our observations are consistent with the presence of strong dynamo fields during portions of the older pre-Nectarian and Nectarian periods, with a weakening (or cessation) of the dynamo at the beginning of the younger Imbrian period. These results differ from paleomagnetic analyses of lunar samples that imply the existence of strong dynamo field strengths from the pre-Nectarian up until at least the first half of the Imbrian period.

Lightweight tensorial convolutional neural network for lunar impact crater detection

Impact crater detection is one of the most important and critical studies of the geological history of planetary surfaces. Recent literature on crater identification demonstrates the powerful potential of deep-learning algorithms. However, these methods potentially assumed that the detection is conducted in devices with sufficient computing resources, and used networks with massive learnable parameters to obtain superb performance. In this paper, we proposed a lightweight tensorial convolutional neural network (CNN) for lunar impact crater detection. Specifically, we develop a tensorial you only look once (TYOLO) network by representing the convolutional kernels in tensor train (TT) decomposition. Thus, the parameter redundancy of TYOLO can be greatly reduced. To the best of our knowledge, this is the first work that considers the lightweight CNN model for impact crater detection. We demonstrate the lunar impact crater detection results over the ChangE-2 lunar digital orthophotos model (DOM) and digital evaluated model (DEM). Experimental results show that the proposed TYOLO network can obtain 1.44×speed up and 39× compression with 2.4% improvement in crater detection. Overall, the proposed network demonstrates great potential in spacecraft with emerging computing resource constraints.

An automatic anchor-free convolutional neural network method for recognizing small-scale lunar impact craters

An automatic anchor-free convolutional neural network method for recognizing small-scale lunar impact craters

Lunar Impact Craters: New Perspectives From Full‐Polarimetric Analysis of Chandrayaan‐2 Dual‐Frequency SAR Data

AbstractWith the availability of the full‐polarimetry data from Chandrayaan‐2 Dual‐frequency Synthetic Aperture Radar (DFSAR) for the first time, the lunar impact craters are studied for comprehensive characterization, which was earlier limited due to hybrid‐polarimetry based observations. In conjunction with the conventional Circular Polarization Ratio (CPR), we explore the unique full‐polarimetric parameters such as Single‐bounce Eigenvalue Relative Difference and T‐Ratio which are sensitive to surface roughness and dielectric constant, respectively. We studied the polarimetric behavior of a total of 115 nos. of impact craters within a specific size range (∼1–25 km diameter) belonging to different categories viz. fresh, degraded, polar and non‐polar anomalous craters. Our results confirm that both kinds of non‐polar and polar anomalous craters overall hold centimeter‐decimeter scale surface roughness similar to the exterior of the fresh craters, which mainly leads to the anomalous nature of the craters. Our analysis indicates possible control of the crater degradation process on the change in the radar scattering behavior of the studied lunar craters. The crater degradation causes a reduction in the surface roughness and dielectric property (density) of the scatterers present in various craters leading to a change in the radar scattering behavior. Alongside, water ice, if at all present admixed into the regolith in the polar anomalous craters, is not in the form of dominant scatterers that can influence the radar signature. However, the occurrence of relatively higher CPR values in smooth low dielectric crater interiors supports the possibility of the presence of water ice in some of the studied polar anomalous craters belonging to Pre‐Imbrian to Imbrian lunar geological periods.

Study on the volume of landslides in lunar impact craters

The lunar exploration project is of great significance, among which the statistics and measurement of lunar landslide are an important part of lunar exploration. With the help of Google Earth software, lunar image data were obtained, and the volume calculation and analysis of landslides in 14 meteorite impact craters on the Moon were performed using the Alfred S. McEwen formula. It is found by calculation and observation that the diameter of the crater is proportional to the volume of the landslide in most of the meteorite impact craters on the moon.

Automatic detection for small-scale lunar impact crater using deep learning

The impact crater is the most dominant geomorphic structural units over the planetary surface and its accurate extraction is of great significance to investigate the evolution of the Moon, the impact history of the solar system and the space environment. Through the impact crater’s identification, especially small-scale, and their spatial distribution characteristics, the impact flux, morphological characteristics, age of impact craters and surface degradation can be revealed. Therefore, this paper presents an automatic method for the impact crater detection, especially small-scale ones, using anchor-free deep learning. Due to the limitation of traditional object proposals for detecting different types of impact craters, we retrain the anchor-free CenterNet model using a transfer learning strategy, where the detection task of impact crater is formulated into the detection of its center point and regression of its property (i.e., size) without non-maximal suppression. We select the stacked Hourglass network as backbone to aggregate different levels of feature for enhancing the capability of estimating the impact crater centers. Moreover, we find the center points of impact craters on the image features’ heatmap only based on their locations, instead of box overlap, which allows us to detect different types of impact craters, even the impact craters that contain other impact craters. The model is trained in an end-to-end manner and applied to detect the impact craters on the lunar images between ±50° of latitude, with the spatial resolution of 100 m/pixel, from Wide Angle Camera (WAC) onboard Lunar Reconnaissance Orbiter (LRO) mission. The obtained results are compared relative to an existing crater database both qualitatively and quantitatively that suggests the reliability and robustness of the developed method in the automatic detection of small-scale impact craters, where the smallest one is 500 m. Moreover, the developed method is capable to detect different types of impact craters, including dispersal and connective ones, with the recall of 73.66% and precision of 78.27% compared with an existing crater database. The code is publicly available at https://github.com/ShuoweiZhang/crater_detection.

Morphological Characterization of Decimeter‐ to Hectometer‐Scale Impact Craters at the Chang’E‐3/4/5 Landing Sites

AbstractThe morphology of impact craters on the Moon is important for understanding impact cratering process and lunar surface properties. Though the morphology of lunar impact craters at hectometer scales and larger is extensively investigated and well understood, the morphology of very small craters is rarely studied due to the lack of high‐resolution data. In this study, we investigated the morphology of 1,513 craters at decimeter to hectometer scales using the 5 mm/pixel–0.5 m/pixel topographic data of the Chang’E‐3/4/5 (CE‐3/4/5) landing regions. Our results show that crater depth, rim height, and inner wall slope increase with the diameter and that the mean values of depth/diameter ratio, rim height/diameter ratio, and inner wall slope are 0.065, 0.017, and 10.5°, respectively. These morphometric parameters are smaller than those of large craters above hectometer scales, which is probably due to low‐strength surficial regolith and rapid topographic degradation. The comparison of the morphometric parameters among the three regions suggests that the CE‐4, CE‐3, and CE‐5 landing regions have the highest, intermediate, and lowest regolith strengths, respectively. In addition, we first observed widespread meter‐scale concentric and flat‐bottomed craters in optical images, which are most probably formed due to strength heterogeneity within the regolith layer. Our results can help to understand near‐surface properties of the regolith layer, lunar surface evolution, and impact cratering process in the strength regime.

Influence of Target Properties on Wall Slumping in Lunar Impact Craters Within the Simple‐To‐Complex Transition

AbstractThe 15–20 km crater diameter range on the Moon spans simple to transitional to complex crater morphologies. Simple craters in this range are only in the highlands. Transitional craters that contain localized slumps are scattered across the lunar surface. Most craters with localized slumps in the highlands superpose pre‐impact topography with obvious slope breaks. We interpret this as a condition favorable for post‐excavation internal slumping. However, some of these craters formed on terrains with topographic variation like the settings of simple craters: flat or gradually sloping surface, or degraded structures of older craters such as rims and terraces. To resolve the conundrum of two morphologies on one terrain type, we performed investigations of the local geology and topography of the inferred pre‐impact terrains. We assessed if the localized slumping in the craters happened during or well after crater formation, looked for spatial variations in the strength of the highlands crust, detected topographic breaks (through elevation data) that were unnoticeable in the optical data, and examined rim circularity. Our findings corroborate the influence of pre‐existing slopes on mass wasting along crater walls. Most of the craters with localized slumps have walls superposing topographic breaks that slope toward the crater interior. These walls are located near the uphill sector of the rims which initiated localized slumping. Most simple craters were found to have formed on surfaces with topographic breaks/slopes that face away from the adjoining crater walls, so that any immediate mass wasting would likely be outside the crater cavity.

Lunar Evolution Analysis Based on Numerical Simulations of Typical Lunar Impact Craters

Impact craters are one of the most important landforms on the lunar surface, playing a crucial role in the formation and later evolution of the Moon. For example, as a primary source of remote sensing observations and lunar samples, lunar regolith is predominantly composed of impact ejecta. Based on their morphologies, lunar impact craters with increasing sizes can be classified into simple craters, complex craters, and multiring basins, and they play different roles in lunar evolution. In our study, we conducted numerical simulations of the South Pole-Aitken basin and the Orientale basin on the lunar surface, as well as the Aristarchus complex crater and the Zhinyu simple crater. The resultant effects of them are further analyzed. Because Zhinyu crater is relatively close to the Chang’e-4 landing site, while Aristarchus crater is relatively close to the Chang’e-5 landing site, their simulation results have direct significance for interpreting the corresponding exploration data from both missions. The numerical simulation results demonstrate that the formation of large basins not only affects the subsurface structure within the basin but also significantly disturbs the surrounding geological layers. Complex and simple craters mainly disturb the subsurface layers within the crater, but complex craters can cause uplift of the underlying strata. These impact processes dominate the primary geological framework of the lunar surface, depositing ejecta materials of varying thicknesses from different depths, which has important implications for future sample collection missions. In conclusion, impact processes are one of the primary driving forces in the lunar evolution.

Inconsistency between the Ancient Mars and Moon Impact Records of Megameter-scale Craters

Impact crater populations provide a record of the bombardment history of a planetary body. These craters reveal an intense bombardment history on Earth's Moon that included significant, large impacts, at least four of which created craters larger than ∼1000 km across more than 3.7 Ga. Scaling the lunar impact crater history to Mars using generally well-established scaling rules indicates Mars should have ∼40–80 similarly sized, ancient craters. However, Mars has roughly seven. This is a nontrivial mismatch between observation and expectation. Possible methods to resolve the mismatch are discussed in this work: small number statistics, incorrect scaling laws, heat flow produced larger-than-expected craters, the ratio of Mars:Moon impactors was different in early solar system history, Mars’s ancient crater chronology is wrong, and/or Mars has hidden a large fraction of its large impacts. None of these scenarios are mutually exclusive. This work details how the different scenarios could work to bring observations more in-line with the scaling expectations, or vice versa. It is posited that the most likely sources of the mismatch are that the initial bolide rate was different (this is a noncontroversial supposition), that lunar heat flow produced larger craters than expected, and the formation of the Martian Borealis basin could have kept the surface warm enough for long enough to prevent large features from forming for an extended period of time. The primary purpose of this work is to present the issue with possible ways to solve it through future efforts.

A Global Analysis of Crater Depth/Diameter Ratios on the Moon

AbstractOn the Moon, the old anorthositic highland rocks and younger basaltic mare units show distinguishable differences in appearance. The craters on the lunar highlands and maria also show variation in morphological characteristics. We analyzed the crater morphologies on a global scale, based on our new global catalog of lunar impact craters (≥1 km in diameter) with morphological information for each crater. We find that there is a global power‐law relationship with an offset term between crater depth‐to‐diameter ratios and crater densities. Small craters (less than ∼4 km in diameter) on the lunar maria are found to be deeper than those on the highlands, indicating differences in target properties and crater degradations. Furthermore, the depths of the deepest simple craters identified on the lunar maria and the highlands provide references that the mare basalts and highland upper megaregolith have thicknesses of ∼2.3 and ∼3.3 km, respectively.

ELCD: Efficient Lunar Crater Detection Based on Attention Mechanisms and Multiscale Feature Fusion Networks from Digital Elevation Models

The detection and counting of lunar impact craters are crucial for the selection of detector landing sites and the estimation of the age of the Moon. However, traditional crater detection methods are based on machine learning and image processing technologies. These are inefficient for situations with different distributions, overlaps, and crater sizes, and most of them mainly focus on the accuracy of detection and ignore the efficiency. In this paper, we propose an efficient lunar crater detection (ELCD) algorithm based on a novel crater edge segmentation network (AFNet) to detect lunar craters from digital elevation model (DEM) data. First, in AFNet, a lightweight attention mechanism module is introduced to enhance the feature extract capabilities of networks, and a new multiscale feature fusion module is designed by fusing different multi-level feature maps to reduce the information loss of the output map. Then, considering the imbalance in the classification and the distributions of the crater data, an efficient crater edge segmentation loss function (CESL) is designed to improve the network optimization performance. Lastly, the crater positions are obtained from the network output map by the crater edge extraction (CEA) algorithm. The experiment was conducted on the PyTorch platform using two lunar crater catalogs to evaluate the ELCD. The experimental results show that ELCD has a superior detection accuracy and inference speed compared with other state-of-the-art crater detection algorithms. As with most crater detection models that use DEM data, some small craters may be considered to be noise that cannot be detected. The proposed algorithm can be used to improve the accuracy and speed of deep space probes in detecting candidate landing sites, and the discovery of new craters can increase the size of the original data set.

Geochemistry of lunar meteorite Northwest Africa 11962 and its potential source region/crater in the Procellarum KREEP terrane

AbstractLunar meteorite Northwest Africa (NWA) 11962 is a regolith breccia with a diverse range of mineral and lithic clasts. For the present study, major and trace element contents and selected isotopic compositions were determined on a homogenized bulk powder of NWA 11962 by instrumental neutron activation analysis, thermal ionization mass spectrometry, and inductively coupled plasma mass spectrometry. The chemical composition of the sample points toward an origin of the meteorite from within the Procellarum KREEP terrane (PKT). Samarium‐Nd and Rb‐Sr isotopic compositions and concentrations show a similarity to those of Apollo 15 soils and KREEP basalt. Highly siderophile element (HSE) abundances and ratios, as well as the Re/Os isotopic system, are often used as tracers of different impactor types. The 187Os/188Os and 187Re/188Os isotopic ratios are within the range of ordinary chondrites, yet some of the siderophile element and highly siderophile element ratios are comparable to those of iron meteorites. Using Fe, Th, and Ti abundances of lunar surface regolith, measured by the Lunar Prospector gamma‐ray spectrometer, we attempt to constrain potential lunar source regions for NWA 11962. By matching these possible source regions with coordinates of recently (<1 Ma) formed lunar impact craters (so‐called lunar cold spots), we localized a potential source crater of NWA 11962 at the western rim of the PKT close to Sinus Medii.

An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery

Lunar craters and rilles are significant topographic features on the lunar surface that will play an essential role in future research on space energy resources and geological evolution. However, previous studies have shown low efficiency in detecting lunar impact craters and poor accuracy in detecting lunar rilles. There is no complete automated identification method for lunar features to explore space energy resources further. In this paper, we propose a new specific deep-learning method called high-resolution global–local networks (HR-GLNet) to explore craters and rilles and to discover space energy simultaneously. Based on the GLNet network, the ResNet structure in the global branch is replaced by HRNet, and the residual network and FPN are the local branches. Principal loss function and auxiliary loss function are used to aggregate global and local branches. In experiments, the model, combined with transfer learning methods, can accurately detect lunar craters, Mars craters, and lunar rilles. Compared with other networks, such as UNet, ERU-Net, HRNet, and GLNet, GL-HRNet has a higher accuracy (88.7 ± 8.9) and recall rate (80.1 ± 2.7) in lunar impact crater detection. In addition, the mean absolute error (MAE) of the GL-HRNet on global and local branches is 0.0612 and 0.0429, which are better than the GLNet in terms of segmentation accuracy and MAE. Finally, by analyzing the density distribution of lunar impact craters with a diameter of less than 5 km, it was found that: (i) small impact craters in a local area of the lunar north pole and highland (5°–85°E, 25°–50°S) show apparent high density, and (ii) the density of impact craters in the Orientale Basin is not significantly different from that in the surrounding areas, which is the direction for future geological research.

Coupling Complementary Strategy to U-Net Based Convolution Neural Network for Detecting Lunar Impact Craters

Lunar crater detection plays an important role in lunar exploration, while machine learning (ML) exhibits promising advantages in the field. However, previous ML works almost all used a single type of lunar map, such as an elevation map (DEM) or orthographic projection map (WAC), to extract crater features; the two types of images have individual limitations on reflecting the crater features, which lead to insufficient feature information, in turn influencing the detection performance. To address this limitation, we, in this work, propose feature complementary of the two types of images and accordingly explore an advanced dual-path convolutional neural network (Dual-Path) based on a U-NET structure to effectively conduct feature integration. Dual-Path consists of a contracting path, bridging path, and expanding path. The contracting path separately extracts features from DEM and WAC images by means of two independent input branches, while the bridging layer integrates the two types of features by 1 × 1 convolution. Finally, the expanding path, coupled with the attention mechanism, further learns and optimizes the feature information. In addition, a special deep convolution block with a residual module is introduced to avoid network degradation and gradient disappearance. The ablation experiment and the comparison of four competitive models only using DEM features confirm that the feature complementary can effectively improve the detection performance and speed. Our model is further verified by different regions of the whole moon, exhibiting high robustness and potential in practical applications.

Determining the age of Vaughan, a potential source crater for lunar meteorites, using boulder size-frequency distributions

We use Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images to analyze the boulder distribution around Vaughan crater, a small, fresh crater on the far side of the Moon. Using six similar lunar impact craters of known age for comparison, we determine the approximate age of the crater. Vaughan crater lies in a part of the South Pole-Aitken basin that is compositionally similar to lunar meteorite Dhofar 961, on the basis of Lunar Prospector Gamma Ray Spectrometer data, making Vaughan crater a potential candidate for the source of this meteorite and possibly others paired with it. Boulder distributions reveal that as impact craters age, the boulder population density in their ejecta decreases. This understanding allows us to use size-frequency distributions to compare crater ages on the basis of their boulder population density. We find that Vaughan crater is very young (<25 Ma) owing to its placement within the boulder size-frequency comparison plot and its population of large (>10 m diameter) boulders. Additionally, we compare LROC NAC distributions to thermophysically derived values for rock abundance from the Diviner Lunar Radiometer. We find that our LROC NAC count is consistent with Diviner rock abundance values, which further supports the inference that Vaughan is a very young impact crater and could plausibly be a source of lunar meteorite Dhofar 961.

Split-Attention Networks with Self-Calibrated Convolution for Moon Impact Crater Detection from Multi-Source Data

Impact craters are the most prominent features on the surface of the Moon, Mars, and Mercury. They play an essential role in constructing lunar bases, the dating of Mars and Mercury, and the surface exploration of other celestial bodies. The traditional crater detection algorithms (CDA) are mainly based on manual interpretation which is combined with classical image processing techniques. The traditional CDAs are, however, inefficient for detecting smaller or overlapped impact craters. In this paper, we propose a Split-Attention Networks with Self-Calibrated Convolution (SCNeSt) architecture, in which the channel-wise attention with multi-path representation and self-calibrated convolutions can generate more prosperous and more discriminative feature representations. The algorithm first extracts the crater feature model under the well-known target detection R-FCN network framework. The trained models are then applied to detecting the impact craters on Mercury and Mars using the transfer learning method. In the lunar impact crater detection experiment, we managed to extract a total of 157,389 impact craters with diameters between 0.6 and 860 km. Our proposed model outperforms the ResNet, ResNeXt, ScNet, and ResNeSt models in terms of recall rate and accuracy is more efficient than that other residual network models. Without training for Mars and Mercury remote sensing data, our model can also identify craters of different scales and demonstrates outstanding robustness and transferability.