Hollows on Mercury: Creation and Analysis of a Global Reference Catalog With Deep Learning
Abstract Hollows are geologically young depressions on Mercury, most likely associated with the loss of volatile species. The distribution and morphometric properties of hollows provide information about the overall volatile budget of Mercury's (shallow) subsurface, with significant implication...
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| Language: | English |
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Wiley
2025-03-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2024JH000431 |
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| author | Valentin T. Bickel Ariel N. Deutsch David T. Blewett |
| author_facet | Valentin T. Bickel Ariel N. Deutsch David T. Blewett |
| author_sort | Valentin T. Bickel |
| collection | DOAJ |
| description | Abstract Hollows are geologically young depressions on Mercury, most likely associated with the loss of volatile species. The distribution and morphometric properties of hollows provide information about the overall volatile budget of Mercury's (shallow) subsurface, with significant implications for our understanding of the evolution of Mercury and airless planetary bodies in general. Here, we use a convolutional neural network to map the global geographic distribution and morphometric properties of hollows in MESSENGER orbital images and assess their geostatistical relationships with the thermophysical environment. We identify up to 19,110 hollows in the MESSENGER MDIS Narrow‐Angle Camera data set and discover previously unidentified hollows in more than twenty large‐scale geographic regions. Globally, the detected hollows are predominantly located in the northern hemisphere, where MESSENGER image coverage and spatial resolution are highest. Hollows are preferentially detected in impact craters, at low elevations, on low slope angles, and cluster toward the maxima of ejecta mass production by micrometeoroid bombardment. We observe that hollows tend to be located on equator‐facing slopes and increase in size toward the equator and hot‐pole longitudes. Our observations provide new, global‐scale evidence that micrometeoroid bombardment and insolation are the primary drivers of hollow formation and evolution. Our hollow catalogs are openly available and are anticipated to inform systematic, global‐scale studies of hollow formation as well as future orbital imaging efforts by the ESA/JAXA BepiColombo mission. |
| format | Article |
| id | doaj-art-0709d8d72ce24b7ca3c21096895aad71 |
| institution | OA Journals |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-0709d8d72ce24b7ca3c21096895aad712025-08-20T02:10:42ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-03-0121n/an/a10.1029/2024JH000431Hollows on Mercury: Creation and Analysis of a Global Reference Catalog With Deep LearningValentin T. Bickel0Ariel N. Deutsch1David T. Blewett2Center for Space and Habitability University of Bern Bern SwitzerlandNASA Ames Research Center Mountain View CA USAJohns Hopkins University Applied Physics Laboratory Laurel MD USAAbstract Hollows are geologically young depressions on Mercury, most likely associated with the loss of volatile species. The distribution and morphometric properties of hollows provide information about the overall volatile budget of Mercury's (shallow) subsurface, with significant implications for our understanding of the evolution of Mercury and airless planetary bodies in general. Here, we use a convolutional neural network to map the global geographic distribution and morphometric properties of hollows in MESSENGER orbital images and assess their geostatistical relationships with the thermophysical environment. We identify up to 19,110 hollows in the MESSENGER MDIS Narrow‐Angle Camera data set and discover previously unidentified hollows in more than twenty large‐scale geographic regions. Globally, the detected hollows are predominantly located in the northern hemisphere, where MESSENGER image coverage and spatial resolution are highest. Hollows are preferentially detected in impact craters, at low elevations, on low slope angles, and cluster toward the maxima of ejecta mass production by micrometeoroid bombardment. We observe that hollows tend to be located on equator‐facing slopes and increase in size toward the equator and hot‐pole longitudes. Our observations provide new, global‐scale evidence that micrometeoroid bombardment and insolation are the primary drivers of hollow formation and evolution. Our hollow catalogs are openly available and are anticipated to inform systematic, global‐scale studies of hollow formation as well as future orbital imaging efforts by the ESA/JAXA BepiColombo mission.https://doi.org/10.1029/2024JH000431MercuryMESSENGERhollowsvolatilesmachine learninglandscape evolution |
| spellingShingle | Valentin T. Bickel Ariel N. Deutsch David T. Blewett Hollows on Mercury: Creation and Analysis of a Global Reference Catalog With Deep Learning Journal of Geophysical Research: Machine Learning and Computation Mercury MESSENGER hollows volatiles machine learning landscape evolution |
| title | Hollows on Mercury: Creation and Analysis of a Global Reference Catalog With Deep Learning |
| title_full | Hollows on Mercury: Creation and Analysis of a Global Reference Catalog With Deep Learning |
| title_fullStr | Hollows on Mercury: Creation and Analysis of a Global Reference Catalog With Deep Learning |
| title_full_unstemmed | Hollows on Mercury: Creation and Analysis of a Global Reference Catalog With Deep Learning |
| title_short | Hollows on Mercury: Creation and Analysis of a Global Reference Catalog With Deep Learning |
| title_sort | hollows on mercury creation and analysis of a global reference catalog with deep learning |
| topic | Mercury MESSENGER hollows volatiles machine learning landscape evolution |
| url | https://doi.org/10.1029/2024JH000431 |
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