{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:36:26Z","timestamp":1760229386490,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Central Universities"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The identification of impact craters on the Moon and other planetary bodies is of great significance to studying and constraining the dynamical process and evolution of the Solar System. Traditionally, this has been performed through the visual examination of images. Due to the effect of overburden, some structural features cannot be effectively identified from optical images, resulting in limitations in the scope, efficiency and accuracy of identification. In this paper, we investigate the viability of convolutional neural networks (CNNs) to perform the detection of impact craters from GRAIL-acquired gravity data. The ideal values of each hyperparameter in U-net architecture are determined after dozens of iterations of model training, testing and evaluation. The final model was evaluated by the Loss function with the low value of 0.04, indicating that the predicted output of the model reached a relatively high fitting degree with the prior labelled output. The comparative results with different methods show that the proposed method has a clear detection of the target features, with an accuracy of more than 80%. In addition, the detection results of the whole image account for 83% of the number of manually delineated gravity anomalies. The proposed method can still achieve the same quality for the identification of the gravity anomalies caused by impact craters under the condition that the resolution of GRAIL gravity data are not superior. Our results demonstrate that the U-net architecture can be a very effective tool for the rapid and automatic identification of impact craters from gravity map on the Moon, as well as other Solar System bodies.<\/jats:p>","DOI":"10.3390\/rs14122783","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2783","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The Identification of Impact Craters from GRAIL-Acquired Gravity Data by U-Net Architecture"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4482-4338","authenticated-orcid":false,"given":"Zhaoxi","family":"Chen","sequence":"first","affiliation":[{"name":"School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China"}]},{"given":"Zidan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1038\/nature03676","article-title":"Origin of the cataclysmic Late Heavy Bombardment period of the terrestrial planets","volume":"435","author":"Gomes","year":"2005","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/0019-1035(70)90059-X","article-title":"Note: Lunar cratering chronology","volume":"13","author":"Hartmann","year":"1970","journal-title":"Icarus"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2001JE001583","article-title":"Mass flux in the ancient Earth-Moon system and benign implications for the origin of life on Earth","volume":"107","author":"Ryder","year":"2002","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wilhelms, D.E., Mccauley, J.F., and Trask, N.J. (1987). The Geologic History of the Moon: U.S. Geological Survey Professional Paper 1348 302 (U.S. Geological Survey).","DOI":"10.3133\/pp1348"},{"key":"ref_5","unstructured":"Wilhelms, D.E., and Byrne, C.J. (2022, June 06). Stratigraphy of Lunar Craters. Available online: http:\/\/www.imageagain.com\/Strata\/StratigraphyCraters.2.0.htm."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2011JE003951","article-title":"Lunar impact basins: Stratigraphy, sequence and ages from superposed impact crater populations measured from Lunar Orbiter Laser Altimeter (LOLA) data","volume":"117","author":"Fassett","year":"2012","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.icarus.2013.03.018","article-title":"Ages of large lunar impact craters and implications for bombardment during the Moon\u2019s middle age","volume":"225","author":"Kirchoff","year":"2013","journal-title":"Icarus"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1126\/science.aar4058","article-title":"Earth and Moon impact flux increased at the end of the Paleozoic","volume":"363","author":"Mazrouei","year":"2019","journal-title":"Science"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, C., Zhao, H., Bruzzone, L., Benediktsson, J.A., Liang, Y., Liu, B., Zeng, X., Guan, R., Li, C., and Ouyang, Z. (2020). Lunar impact crater identification and age estimation with Chang\u2019E data by deep and transfer learning. Nat. Commun., 11.","DOI":"10.1038\/s41467-020-20215-y"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"20377","DOI":"10.1029\/1999JE001110","article-title":"Imaging of lunar surface maturity","volume":"105","author":"Lucey","year":"2000","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_11","first-page":"C163","article-title":"Lunar crater morphology and relative-age determination of geologic units-part 2. Applications","volume":"700-C","author":"Pohn","year":"1970","journal-title":"U.S. Geol. Surv. Prof. Pap."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1029\/JB077i002p00279","article-title":"Technique for rapid determination of relative ages of lunar areas from orbital photography","volume":"77","author":"Soderblom","year":"1972","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TGRS.2018.2806371","article-title":"Lunar Crater Detection Based on Terrain Analysis and Mathematical Morphology Methods Using Digital Elevation Models","volume":"56","author":"Chen","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4751","DOI":"10.1109\/JSTARS.2015.2481407","article-title":"Automatic Extraction and Identification of Lunar Impact Craters Based on Optical Data and DEMs Acquired by the Chang\u2019E Satellites","volume":"8","author":"Kang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1016\/j.icarus.2013.06.028","article-title":"Crater detection, classification and contextual information extraction in lunar images using a novel algorithm","volume":"226","author":"Vijayan","year":"2013","journal-title":"Icarus"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Emami, E., Bebis, G., Nefian, A., and Fong, T. (2015). Automatic Crater Detection Using Convex Grouping and Convolutional Neural Networks, Springer.","DOI":"10.1007\/978-3-319-27863-6_20"},{"key":"ref_17","unstructured":"Salamuni\u0107car, G., and Lon\u010dari\u0107, S. (2010, January 18\u201325). Method for crater detection from digital topography data: Interpolation based improvement and application to Lunar SELENE LALT data. Proceedings of the 38th COSPAR Scientific Assembly, Bremen, Germany."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wetzler, P., Honda, R., Enke, B., Merline, W., Chapman, C., and Burl, M. (2005, January 5\u20137). Learning to detect small impact craters. Proceedings of the 7th IEEE Workshop on Application of Computer Vision, Breckenridge, CO, USA.","DOI":"10.1109\/ACVMOT.2005.68"},{"key":"ref_19","first-page":"10","article-title":"Precision size-frequency distributions of craters for 12 selected areas of the lunar surface","volume":"2","author":"Greeley","year":"1970","journal-title":"Earth Moon Planets"},{"key":"ref_20","unstructured":"Kirchoff, M., Sherman, K., and Chapman, C. (2011, January 2\u20137). Examining lunar impactor population evolution: Additional results from crater distributions on diverse terrains. Proceedings of the EPSC-DPS Joint Meeting, Nantes, France."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.icarus.2014.02.022","article-title":"The variability of crater identification among expert and community crater analysts","volume":"234","author":"Robbins","year":"2014","journal-title":"Icarus"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_23","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http:\/\/www.deeplearningbook.org."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hassanien, A.E., and Gaber, T. (2017). Handbook of Research on Machine Learning Applications and Trends, IGI Global.","DOI":"10.4018\/978-1-5225-2229-4"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.icarus.2018.06.022","article-title":"Lunar crater identification via deep learning","volume":"317","author":"Silburt","year":"2018","journal-title":"Icarus"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1126\/science.1231507","article-title":"Gravity field of the moon from the gravity recovery and interior laboratory (GRAIL) mission","volume":"339","author":"Zuber","year":"2012","journal-title":"Science"},{"key":"ref_27","unstructured":"Siddiqi, A.A. (2018). Beyond Earth: A Chronicle of Deep Space Exploration, 1958\u20132016."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.1231530","article-title":"The Crust of the Moon as Seen by GRAIL","volume":"339","author":"Wieczorek","year":"2013","journal-title":"Science"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation, Springer International Publishing.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1016\/j.asr.2014.08.018","article-title":"A machine learning approach to crater detection from topographic data","volume":"54","author":"Di","year":"2014","journal-title":"Adv. Space Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1007\/s11433-009-0284-x","article-title":"New features of the Moon revealed and identified by CLTM-s01","volume":"52","author":"Huang","year":"2009","journal-title":"Sci. China Ser. G Phys. Mech. Astron."},{"key":"ref_32","first-page":"27","article-title":"A Previously Unreported and Unclassified Dome Near Archimedes","volume":"43","author":"Lena","year":"2001","journal-title":"Scrolling Astron."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2783\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:27:27Z","timestamp":1760138847000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2783"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,10]]},"references-count":32,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122783"],"URL":"https:\/\/doi.org\/10.3390\/rs14122783","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,6,10]]}}}