{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T21:00:25Z","timestamp":1793048425115,"version":"4.5.0"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["12261131494"],"award-info":[{"award-number":["12261131494"]}]},{"name":"National Natural Science Foundation of China","award":["12171455"],"award-info":[{"award-number":["12171455"]}]},{"name":"National Natural Science Foundation of China","award":["RSF-NSFC 23-41-00002"],"award-info":[{"award-number":["RSF-NSFC 23-41-00002"]}]},{"name":"Russian Science Foundation","award":["12261131494"],"award-info":[{"award-number":["12261131494"]}]},{"name":"Russian Science Foundation","award":["12171455"],"award-info":[{"award-number":["12171455"]}]},{"name":"Russian Science Foundation","award":["RSF-NSFC 23-41-00002"],"award-info":[{"award-number":["RSF-NSFC 23-41-00002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Enhancing the reliability of inversion results has always been a prominent issue in the field of geophysics. In recent years, data-driven inversion methods leveraging deep neural networks (DNNs) have gained prominence for their ability to address non-uniqueness issues and reduce computational costs compared to traditional physically model-driven methods. In this study, we propose a GMNet machine learning method, i.e., a CNN-based inversion method for gravity and magnetic field data. This method relies more on data-driven training, and in the prediction phase after the model is trained, it does not heavily depend on a priori assumptions, unlike traditional methods. By forward modeling gravity and magnetic fields, we obtain a substantial dataset to train the CNN model, enabling the direct mapping from field data to subsurface property distribution. Applying this method to synthetic data and one-field data yields promising inversion results.<\/jats:p>","DOI":"10.3390\/rs16071115","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T10:03:59Z","timestamp":1711101839000},"page":"1115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Joint Gravity and Magnetic Inversion Using CNNs\u2019 Deep Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhijing","family":"Bai","sequence":"first","affiliation":[{"name":"Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7992-3999","authenticated-orcid":false,"given":"Yanfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenzhang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caixia","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5140-3617","authenticated-orcid":false,"given":"Dmitry","family":"Lukyanenko","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Physics, Lomonosov Moscow State University, Moscow 119991, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6497-3117","authenticated-orcid":false,"given":"Inna","family":"Stepanova","sequence":"additional","affiliation":[{"name":"Schmidt Institute of Physics of Earth, Russian Academy of Sciences, Moscow 123995, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6942-2138","authenticated-orcid":false,"given":"Anatoly G.","family":"Yagola","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Physics, Lomonosov Moscow State University, Moscow 119991, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"ref_1","unstructured":"Zhdanov, M.S. 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