Automated Discontinuity Detection and Reconstruction in Subsurface Environment of Mars Using Deep Learning: A Case Study of SHARAD Observation

Autor: Vanshika Gupta, Sharad Kumar Gupta, Jungrack Kim

Publikováno v: Applied Sciences, Vol 10, Iss 7, p 2279 (2020)

Machine learning (ML) algorithmic developments and improvements in Earth and planetary science are expected to bring enormous benefits for areas such as geospatial database construction, automated geological feature reconstruction, and surface dating

Externí odkaz: https://doaj.org/article/15c0274e79e34e3eb5eeb527dc0592f4

BepiColombo - Mission Overview and Science Goals

Autor: Benkhoff, Johannes, Murakami, Go, Baumjohann, W., Besse, S., Bunce, E.J., Casale, Mauro, Cremonese, G., Glassmeier, K. H., Hayakawa, H., Heyner, Daniel, Hiesinger, H., Huovelin, Juhani, Hussmann, H., Iafolla, V., Iess, Luciano, Kasaba, Yasumasa, Kobayashi, Masanori, Milillo, Anna, Mitrofanov, Igor G., Montagnon, Elsa, Novara, M., Orsini, Stefano, Quemerais, Eric, Reininghaus, U., Saito, Yoshifumi, Santoli, Francesco, Stramaccioni, D., Sutherland, O., Thomas, N., Yoshikawa, I., Zender, Joe

Publikováno v: Benkhoff, J.; Murakami, G.; Baumjohann, W.; Besse, S.; Bunce, E.; Casale, M.; Cremosese, G.; Glassmeier, K.-H.; Hayakawa, H.; Heyner, D.; Hiesinger, H.; Huovelin, J.; Hussmann, H.; Iafolla, V.; Iess, L.; Kasaba, Y.; Kobayashi, M.; Milillo, A.; Mitrofanov, I. G.; Montagnon, E.; ... (2021). BepiColombo-Mission Overview and Science Goals. Space science reviews, 217(8) Springer 10.1007/s11214-021-00861-4
Space Science Reviews
Space Science Reviews, Springer Verlag, 2021, 217, pp.90. ⟨10.1007/s11214-021-00861-4⟩

BepiColombo is a joint mission between the European Space Agency, ESA, and the Japanese Aerospace Exploration Agency, JAXA, to perform a comprehensive exploration of Mercury. Launched on $20^{\mathrm{th}}$ 20 th October 2018 from the European spacepo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::549f860b83f7bdd0312324711cbcecf3
https://doi.org/10.1007/s11214-021-00861-4

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Automated Discontinuity Detection and Reconstruction in Subsurface Environment of Mars Using Deep Learning: A Case Study of SHARAD Observation

Autor: Jungrack Kim, Vanshika Gupta, Sharad Kumar Gupta

Publikováno v: Applied Sciences
Volume 10
Issue 7
Applied Sciences, Vol 10, Iss 2279, p 2279 (2020)

Machine learning (ML) algorithmic developments and improvements in Earth and planetary science are expected to bring enormous benefits for areas such as geospatial database construction, automated geological feature reconstruction, and surface dating

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8663ac1b9605339ce6aee22f73d4537b

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.