The project focuses on the statistical analysis of the distribution of impact craters on Mercury in order to (i) study the geological processes and (ii) determine the age of geological units. This analysis is crucial for understanding the evolution of the surface and thermal history of the planet. Mercury, the smallest and closest planet to the Sun, was studied by NASA's Mariner 10 and MESSENGER missions. They provided extensive data, including extreme temperature variations due to its thin atmosphere and a weak magnetic field likely generated by convection currents in its liquid iron core. These missions also enabled global mapping and the geological interpretation of Mercury's surface. An automated catalog created by the INAF-Padova group (La Grassa et al., submitted) was used. Such a catalog is based on a deep learning algorithm (YOLOLens) to detect and measure craters from 1 to 170 km in diameter. The crater density was evaluated using the cumulative crater Size Frequency Distribution (SFD). Firstly, a Python code was developed to compute the crater cumulative curves and derive relative ages between different areas of Mercury. Secondly, the Craterstats software (DLR Freie Universität Berlin) was used to derive absolute ages for the same areas, by adopting and comparing the Neukum’s (2001) and Le Feuvre and Wieczorek (2011) non-porous’ chronological models. Mercury's surface is divided into fifteen quadrangles. The crater statistical analysis focused on features between 5 and 50 km in diameter. QGIS software was used to select and analyze geological units within the quadrangles. The analysis suggested impacts events around 3.8 billion years ago, likely linked to the Late Heavy Bombardment, with further younger phenomena around 3.7 billion years ago, possibly related to last lava flows.

The project focuses on the statistical analysis of the distribution of impact craters on Mercury in order to (i) study the geological processes and (ii) determine the age of geological units. This analysis is crucial for understanding the evolution of the surface and thermal history of the planet. Mercury, the smallest and closest planet to the Sun, was studied by NASA's Mariner 10 and MESSENGER missions. They provided extensive data, including extreme temperature variations due to its thin atmosphere and a weak magnetic field likely generated by convection currents in its liquid iron core. These missions also enabled global mapping and the geological interpretation of Mercury's surface. An automated catalog created by the INAF-Padova group (La Grassa et al., submitted) was used. Such a catalog is based on a deep learning algorithm (YOLOLens) to detect and measure craters from 1 to 170 km in diameter. The crater density was evaluated using the cumulative crater Size Frequency Distribution (SFD). Firstly, a Python code was developed to compute the crater cumulative curves and derive relative ages between different areas of Mercury. Secondly, the Craterstats software (DLR Freie Universität Berlin) was used to derive absolute ages for the same areas, by adopting and comparing the Neukum’s (2001) and Le Feuvre and Wieczorek (2011) non-porous’ chronological models. Mercury's surface is divided into fifteen quadrangles. The crater statistical analysis focused on features between 5 and 50 km in diameter. QGIS software was used to select and analyze geological units within the quadrangles. The analysis suggested impacts events around 3.8 billion years ago, likely linked to the Late Heavy Bombardment, with further younger phenomena around 3.7 billion years ago, possibly related to last lava flows.

Distribution of impact craters on Mercury's surface

FALETTI, MADDALENA
2023/2024

Abstract

The project focuses on the statistical analysis of the distribution of impact craters on Mercury in order to (i) study the geological processes and (ii) determine the age of geological units. This analysis is crucial for understanding the evolution of the surface and thermal history of the planet. Mercury, the smallest and closest planet to the Sun, was studied by NASA's Mariner 10 and MESSENGER missions. They provided extensive data, including extreme temperature variations due to its thin atmosphere and a weak magnetic field likely generated by convection currents in its liquid iron core. These missions also enabled global mapping and the geological interpretation of Mercury's surface. An automated catalog created by the INAF-Padova group (La Grassa et al., submitted) was used. Such a catalog is based on a deep learning algorithm (YOLOLens) to detect and measure craters from 1 to 170 km in diameter. The crater density was evaluated using the cumulative crater Size Frequency Distribution (SFD). Firstly, a Python code was developed to compute the crater cumulative curves and derive relative ages between different areas of Mercury. Secondly, the Craterstats software (DLR Freie Universität Berlin) was used to derive absolute ages for the same areas, by adopting and comparing the Neukum’s (2001) and Le Feuvre and Wieczorek (2011) non-porous’ chronological models. Mercury's surface is divided into fifteen quadrangles. The crater statistical analysis focused on features between 5 and 50 km in diameter. QGIS software was used to select and analyze geological units within the quadrangles. The analysis suggested impacts events around 3.8 billion years ago, likely linked to the Late Heavy Bombardment, with further younger phenomena around 3.7 billion years ago, possibly related to last lava flows.
2023
Distribution of impact craters on Mercury's surface
The project focuses on the statistical analysis of the distribution of impact craters on Mercury in order to (i) study the geological processes and (ii) determine the age of geological units. This analysis is crucial for understanding the evolution of the surface and thermal history of the planet. Mercury, the smallest and closest planet to the Sun, was studied by NASA's Mariner 10 and MESSENGER missions. They provided extensive data, including extreme temperature variations due to its thin atmosphere and a weak magnetic field likely generated by convection currents in its liquid iron core. These missions also enabled global mapping and the geological interpretation of Mercury's surface. An automated catalog created by the INAF-Padova group (La Grassa et al., submitted) was used. Such a catalog is based on a deep learning algorithm (YOLOLens) to detect and measure craters from 1 to 170 km in diameter. The crater density was evaluated using the cumulative crater Size Frequency Distribution (SFD). Firstly, a Python code was developed to compute the crater cumulative curves and derive relative ages between different areas of Mercury. Secondly, the Craterstats software (DLR Freie Universität Berlin) was used to derive absolute ages for the same areas, by adopting and comparing the Neukum’s (2001) and Le Feuvre and Wieczorek (2011) non-porous’ chronological models. Mercury's surface is divided into fifteen quadrangles. The crater statistical analysis focused on features between 5 and 50 km in diameter. QGIS software was used to select and analyze geological units within the quadrangles. The analysis suggested impacts events around 3.8 billion years ago, likely linked to the Late Heavy Bombardment, with further younger phenomena around 3.7 billion years ago, possibly related to last lava flows.
Mercury
Impact craters
Statistical analysis
Age determination
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/71367