11:30 〜 11:45
[PPS09-10] The Estimation of the Spatiotemporal Distribution of Martian Cryosphere Based on Rampart Crater Morphology and Distribution
キーワード：ランパートクレーター、地下氷圏、機械学習、地下氷
It is believed that liquid water once existed on Mars' surface but dissipated into space through the Martian history. On the other hand, currently, layered ice deposits have been confirmed in the polar regions through observation, and the existence of subsurface ice has been revealed through radar sounding. The total amount of dissipated water and the ice deposits is far less than past surface water volume. Therefore, there is missing gap in water-ice reservoir on Mars[e.g., 1]. The existence of Martian cryosphere has been estimated [2], and it is thought that some of the past surface liquid water on Mars is preserved as subsurface ice. Therefore, detail study of the cryosphere is necessary to understand the evolutionary history of water on Mars.
Rampart craters are the landform formed by impacting into volatile-rich layer beneath the surface [e.g., 3] and the interaction between atmosphere and ejecta [e.g., 4]. They are distributed on Mars globally [5] and have various morphology due to the difference of target layer [e.g., 6].
In this study, focusing on rampart craters as investigators of the location and the depth of subsurface ice, using machine learning methods to investigate the transition and distribution of the depth of the cryosphere on Mars. Morphological classification of rampart craters was conducted using image classification in the Acidalia Planitia region of Mars' northern lowlands. Based on this classification, we conducted a distribution survey and estimated the depth of the subsurface ice. Additionally, we also examined the historical changes in the depth of the subsurface ice in some geological units. These geological units were mainly divided into three geological ages and two regions (Figure 1).
In the image classification using machine learning, using a binary classification, rampart craters were classified into thick lobe type and thin lobe type. Thick lobe rampart craters were predominantly distributed below the elevation boundary corresponding to past shorelines and water deposition layers. This suggests that thick lobe rampart craters are related to past water deposition sites. A trend was observed where the excavation depth of thick lobe rampart craters decreases as latitude increases. Furthermore, there are no significant differences in excavation depth between the geological units. This suggests that subsurface ice may have been present at similar depths below the surface across different geological ages.
References:
[1] Kurokawa et al. (2014) Earth and Planetary Science Letters, 394, 179-185. [2] Clifford et al. (2010) Journal of Geophysical Research: Planets, 115(E7). [3] Carr et al. (1977) Journal of Geophysical Research, 82(28), 4055-4065. [4] Schultz (1992) Journal of Geophysical Research: Planets, 97(E7), 11623-11662. [5] Li et al. (2015) Meteoritics & Planetary Science, 50(3), 508-522. [6] Barlow (2005) Large meteorite impacts III, 384, 433-442.
Rampart craters are the landform formed by impacting into volatile-rich layer beneath the surface [e.g., 3] and the interaction between atmosphere and ejecta [e.g., 4]. They are distributed on Mars globally [5] and have various morphology due to the difference of target layer [e.g., 6].
In this study, focusing on rampart craters as investigators of the location and the depth of subsurface ice, using machine learning methods to investigate the transition and distribution of the depth of the cryosphere on Mars. Morphological classification of rampart craters was conducted using image classification in the Acidalia Planitia region of Mars' northern lowlands. Based on this classification, we conducted a distribution survey and estimated the depth of the subsurface ice. Additionally, we also examined the historical changes in the depth of the subsurface ice in some geological units. These geological units were mainly divided into three geological ages and two regions (Figure 1).
In the image classification using machine learning, using a binary classification, rampart craters were classified into thick lobe type and thin lobe type. Thick lobe rampart craters were predominantly distributed below the elevation boundary corresponding to past shorelines and water deposition layers. This suggests that thick lobe rampart craters are related to past water deposition sites. A trend was observed where the excavation depth of thick lobe rampart craters decreases as latitude increases. Furthermore, there are no significant differences in excavation depth between the geological units. This suggests that subsurface ice may have been present at similar depths below the surface across different geological ages.
References:
[1] Kurokawa et al. (2014) Earth and Planetary Science Letters, 394, 179-185. [2] Clifford et al. (2010) Journal of Geophysical Research: Planets, 115(E7). [3] Carr et al. (1977) Journal of Geophysical Research, 82(28), 4055-4065. [4] Schultz (1992) Journal of Geophysical Research: Planets, 97(E7), 11623-11662. [5] Li et al. (2015) Meteoritics & Planetary Science, 50(3), 508-522. [6] Barlow (2005) Large meteorite impacts III, 384, 433-442.