Lithological mapping of Eratosthenes crater region using Moon Mineralogy Mapper of Chandrayaan-1

Kumaresan, Pappanaicken R.; Saravanavel, J.; Palanivel, K.

doi:10.1016/j.pss.2019.104817

Cited by 11 publications

(4 citation statements)

References 38 publications

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“…Such wall exposures have been used to investigate lunar mare and Martian lava flow thicknesses, and sub-surface composition, etc. (e.g., Kumaresan et al, 2020).…”

Section: Compositional Anomaliesmentioning

confidence: 99%

Effects on Target Geology

Rae,

Bray

2023

Preprint

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The process of impact crater formation can fundamentally change the nature of target materials, creating products that can be observed from planetary-scale geophysics all the way down to mineral microstructure. Impact-generated shock waves can vaporise, melt, and permanently deform the materials they pass through. Large quantities of the target are damaged and displaced, ejecting material outside of the crater and redistributing various shock products and brecciated material within the crater. The aim of this chapter is to describe the changes that occur within target materials and the physical processes that produce them during an impact event.

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“…Such wall exposures have been used to investigate lunar mare and Martian lava flow thicknesses, and sub-surface composition, etc. (e.g., Kumaresan et al, 2020).…”

Section: Compositional Anomaliesmentioning

confidence: 99%

Effects on Target Geology

Rae,

Bray

2023

Preprint

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“…Figures [8][9][10][11][12] show the flight paths for the cases described in Table 1. Terminal landing position is not specified in this research.…”

Section: Study On Robustnessmentioning

confidence: 99%

“…While the Apollo program is already too old, the lunar maria is not striking aims to investigate. A landing spot close to the principal peak of a large crater is believed to be the utmost technically fascinating target [11,12]. Such locations may provide us hints about the lunar origin because these areas are still totally unexplored.…”

Section: Introductionmentioning

confidence: 99%

Landing Trajectory Generation and Energy Optimization for Unmanned Lunar Mission

Islam

Mehedi

2021

Mathematical Problems in Engineering

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The moon is recognized as an important destination for space science and exploration. To find a satisfactory answer for the mystery of the universe and to make use of the lunar resources for the welfare of human beings, several space agencies are planning manned and unmanned missions on the moon. As a result, the concept of lunar vehicles has begun with an advanced descent scheme to execute a precise and safe landing on the surface of the moon. On the contrary, the energy budget is an important issue for any space mission. To reduce the cost of a space mission, it is necessary to design the vehicle trajectory based on optimized energy resources. Fuel is the main energy in a space mission. Therefore, a fuel-optimized energy generation technique is focused on this research. The design of an algorithm that generates a real-time trajectory for the descent and landing of a lunar probe is critical to ensuring a successful lunar landing mission. A scheme of dual-step trajectory generation for lunar descent is also investigated in this paper. In the algorithm developing process, the thrust-to-mass ratio is considered as a principle variable. Algorithm design along with mathematical modeling and simulation results are described in detail. In addition, the proposed method for generating reference trajectory profiles is also analyzed for fuel consumption and robustness.

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“…The solution should be cost-effective and free from subjective factors while providing accurate lithology mapping. This is especially true in the context of the increased need to understand the lithology of other planetary bodies 12 , 13 in our solar system in the age of accelerated space exploration. Machine Learning Algorithms (MLAs), when provided with extensive datasets, excel in extracting complex and non-linear relationships between physical properties and lithology, thereby enhancing the precision of lithology detection in certain samples.…”

Section: Introductionmentioning

confidence: 99%

Automatic lithology identification in meteorite impact craters using machine learning algorithms

Yirenkyi,

Boateng,

Ahene

et al. 2024

Sci Rep

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Identifying lithologies in meteorite impact craters is an important task to unlock processes that have shaped the evolution of planetary bodies. Traditional methods for lithology identification rely on time-consuming manual analysis, which is costly and limits the efficiency of rapid decision-making. This paper utilizes different machine learning algorithms namely Random Forest, Decision Tree, K Nearest Neighbors, and Logistic Regression with Grid Search to classify rock lithologies using data from the Bosumtwi impact crater in Ghana. A repeated stratified k-fold cross-validation method is applied to Grid Search to select the best combination of hyperparameters. The findings demonstrate that the Random Forest algorithm achieves the most promising results in classifying lithologies in the meteorite impact crater with an accuracy score of 86.89%, a recall score of 84.88%, a precision score of 87.21%, and an F1 score of 85.48%. The findings also suggest that more high-quality data has the potential to further increase the accuracy scores of the machine learning algorithm. In conclusion, this study demonstrates the significant potential of machine learning techniques to revolutionize lithology identification in meteorite impact craters, thus paving the way for their influential role in future space exploration endeavors.

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Lithological mapping of Eratosthenes crater region using Moon Mineralogy Mapper of Chandrayaan-1

Cited by 11 publications

References 38 publications

Effects on Target Geology

Effects on Target Geology

Landing Trajectory Generation and Energy Optimization for Unmanned Lunar Mission

Automatic lithology identification in meteorite impact craters using machine learning algorithms

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