Chandrayaan-1 observation of distant secondary craters of Copernicus exhibiting central mound morphology: Evidence for low velocity clustered impacts on the Moon

Kumar, Prakash; Kumar, Ankush; Keerthi, V.; Goswami, J. N.; Krishna, B. Gopala

doi:10.1016/j.pss.2011.04.004

Cited by 24 publications

(6 citation statements)

References 21 publications

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“…Though some studies have manually identified secondary craters [1,20,44], their databases only contain secondary craters and lack primary craters. Head et al [3] proved that craters with diameters larger than 20 km are usually primary craters by statistically searching the density of significantly increased craters (>20 km) in annular zones of the Imbrium Basin and South Pole-Aitken Basin.…”

Section: Sample Datamentioning

confidence: 99%

“…Moreover, secondary craters have a more elliptical or irregular shape and are usually shallower than primary craters with the same diameter [16,18,19]. Researchers also found that primary and secondary craters also differ in rock size and center mound, which may be useful for distinguishing the two [20][21][22].Similar to crater detection, current methods aiming at distinguishing primary craters from secondary craters include manual and automatic techniques. Compared with the great progress made in automatic crater detection, however, manual identification is still the method used in the most recent works related to distinguishing primary craters from secondary craters [1,[23][24][25][26], and very few works have tried to develop a useful automatic approach [5,10,[27][28][29][30].…”

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confidence: 99%

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“…• Secondary craters have an elliptical or irregular shape [15,16]. • Secondary craters show interference features such as septa and mounds [20]. • Secondary craters are usually shallower than primary impact craters with the same diameter [1,16,18,19].…”

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A Machine Learning Approach to Crater Classification from Topographic Data

et al. 2019

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Craters contain important information on geological history and have been widely used for dating absolute age and reconstructing impact history. The impact process results in a lot of ejected fragments and these fragments may form secondary craters. Studies on distinguishing primary craters from secondary craters are helpful in improving the accuracy of crater dating. However, previous studies about distinguishing primary craters from secondary craters were either conducted by manual identification or used approaches mainly concerning crater spatial distribution, which are time-consuming or have low accuracy. This paper presents a machine learning approach to distinguish primary craters from secondary craters. First, samples used for training and testing were identified and unified. The whole dataset contained 1032 primary craters and 4041 secondary craters. Then, considering the differences between primary and secondary craters, features mainly related to crater shape, depth, and density were calculated. Finally, a random forest classifier was trained and tested. This approach showed a favorable performance. The accuracy and F1-score for fivefold cross-validation were 0.939 and 0.839, respectively. The proposed machine learning approach enables an automated method of distinguishing primary craters from secondary craters, which results in better performance.

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A Machine Learning Approach to Crater Classification from Topographic Data

et al. 2019

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“…The basin interior and exterior contain some of the secondary craters of Orientale, Antoniadi, Hausen, Humbolt, and Tsiolkovsky, which postdate the Schrödinger basin [Shoemaker et al, 1994;Kramer et al, 2013]. Some of the secondary craters exhibit central mound morphology that indicates the impacts of low-velocity clustered ejecta projectiles producing these secondary craters [e.g., Kumar et al, 2011]. Primary impact craters superimposed on the Schrödinger basin provide a tentative formation age of 3.8 Ga [Shoemaker et al, 1994].…”

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Recent shallow moonquake and impact-triggered boulder falls on the Moon: New insights from the Schrödinger basin

Kumar

Sruthi

Krishna

et al. 2016

J. Geophys. Res. Planets

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Shallow moonquakes are thought to be of tectonic origin. However, the geologic structures responsible for these moonquakes are unknown. Here we report sites where moonquakes possibly occurred along young lobate scarps in the Schrödinger basin. Our analysis of Lunar Reconnaissance Orbiter and Chandrayaan‐1 images revealed four lobate scarps in different parts of the Schrödinger basin. The scarps crosscut small fresh impact craters (<10–30 m) suggesting a young age for the scarps. A 28 km long scarp (Scarp 1) yields a minimum age of 11 Ma based on buffered crater counting, while others are 35–82 Ma old. The topography of Scarp 1 suggests a range of horizontal shortening (10–30 m) across the fault. Two scarps are associated with boulder falls in which several boulders rolled and bounced on nearby slopes. A cluster of a large number of boulder falls near Scarp 1 indicates that the scarp was seismically active recently. A low runout efficiency of the boulders (~2.5) indicates low to moderate levels of ground shaking, which we interpret to be related to low‐magnitude moonquakes in the scarp. Boulder falls are also observed in other parts of the basin, where we mapped >1500 boulders associated with trails and bouncing marks. Their origins are largely controlled by recent impact events. Ejecta rays and secondary crater chains from a 14 km diameter impact crater traversed Schrödinger and triggered significant boulder falls about 17 Ma. Therefore, a combination of recent shallow moonquakes and impact events triggered the boulder falls in the Schrödinger basin.

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Impact fragmentation of Lonar Crater, India: Implications for impact cratering processes in basalt

et al. 2014

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Impact fragmentation is an energetic process that has affected all planetary bodies. To understand its effects in basalt, we studied Lonar Crater, which is a rare terrestrial simple impact crater in basalt and analogues to kilometer-scale simple craters on Mars. The Lonar ejecta consists of basaltic fragments with sizes ranging from silt to boulder. The cumulative size and mass frequency distributions of these fragments show variation of power index for different size ranges, suggesting simple and complex fragmentation. The general shape of the fragments is compact, platy, bladed, and elongated with an average edge angle of 100°. The size distribution of cobble-to boulder-sized fragments is similar to the fracture spacing distribution in the upper crater wall, indicating the provenance of those large fragments. Its consistency with a theoretical spallation model suggests that the large fragments were ejected from near surface of the target, whereas the small fragments from deeper level. The petrophysical properties of the ejecta fragments reflect the geophysical heterogeneity in the target basalt that significantly reduced the original size of spall fragments. The presence of Fe/Mg phyllosilicates (smectites) both in the ejecta and wall indicates the role of impact in excavating the phyllosilicates from the interior of basaltic target affected by aqueous alteration. The seismic images reveal a thickness variation in the ejecta blanket, segregation of boulders, fractures, and faults in the bedrock beneath the crater rim. The fracturing, fragmentation, and fluvial degradation of Lonar Crater have important implications for Mars.

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Chandrayaan-1 observation of distant secondary craters of Copernicus exhibiting central mound morphology: Evidence for low velocity clustered impacts on the Moon

Cited by 24 publications

References 21 publications

A Machine Learning Approach to Crater Classification from Topographic Data

A Machine Learning Approach to Crater Classification from Topographic Data

Recent shallow moonquake and impact-triggered boulder falls on the Moon: New insights from the Schrödinger basin

Impact fragmentation of Lonar Crater, India: Implications for impact cratering processes in basalt

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