{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T02:21:58Z","timestamp":1792030918074,"version":"4.4.0"},"reference-count":59,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T00:00:00Z","timestamp":1603152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003407","name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["PON-RI 2014-2020, Asse I \"Capitale Umano\" - Azione I.1 \"Dottorati innovativi con caratterizzazione industriale Ciclo XXXIII\" \u2013 project: \"GEOPROBARE: stochastic inversion of time-domain electromagnetic data\""],"award-info":[{"award-number":["PON-RI 2014-2020, Asse I \"Capitale Umano\" - Azione I.1 \"Dottorati innovativi con caratterizzazione industriale Ciclo XXXIII\" \u2013 project: \"GEOPROBARE: stochastic inversion of time-domain electromagnetic data\""]}],"id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["INFACT project (https:\/\/www.infactproject.eu\/) - grant agreement no. 776487"],"award-info":[{"award-number":["INFACT project (https:\/\/www.infactproject.eu\/) - grant agreement no. 776487"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009873","name":"Regione Autonoma della Sardegna","doi-asserted-by":"publisher","award":["POR-FESR Sardegna 2014-2020, Asse I Azione I.1.3 \"Creare opportunit\u00e0 di lavoro favorendo la competitivit\u00e0 delle imprese\" - project: \"Tecnologie di CARatterizzazione Monitoraggio e Analisi per il ripristino e la bonifica (CARMA)\""],"award-info":[{"award-number":["POR-FESR Sardegna 2014-2020, Asse I Azione I.1.3 \"Creare opportunit\u00e0 di lavoro favorendo la competitivit\u00e0 delle imprese\" - project: \"Tecnologie di CARatterizzazione Monitoraggio e Analisi per il ripristino e la bonifica (CARMA)\""]}],"id":[{"id":"10.13039\/501100009873","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014810","name":"Fondazione di Sardegna","doi-asserted-by":"publisher","award":["GETHERE (RAS\/FBS grant-number: F71\/17000190002)"],"award-info":[{"award-number":["GETHERE (RAS\/FBS grant-number: F71\/17000190002)"]}],"id":[{"id":"10.13039\/100014810","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The possibility to have results very quickly after, or even during, the collection of electromagnetic data would be important, not only for quality check purposes, but also for adjusting the location of the proposed flight lines during an airborne time-domain acquisition. This kind of readiness could have a large impact in terms of optimization of the Value of Information of the measurements to be acquired. In addition, the importance of having fast tools for retrieving resistivity models from airborne time-domain data is demonstrated by the fact that Conductivity-Depth Imaging methodologies are still the standard in mineral exploration. In fact, they are extremely computationally efficient, and, at the same time, they preserve a very high lateral resolution. For these reasons, they are often preferred to inversion strategies even if the latter approaches are generally more accurate in terms of proper reconstruction of the depth of the targets and of reliable retrieval of true resistivity values of the subsurface. In this research, we discuss a novel approach, based on neural network techniques, capable of retrieving resistivity models with a quality comparable with the inversion strategy, but in a fraction of the time. We demonstrate the advantages of the proposed novel approach on synthetic and field datasets.<\/jats:p>","DOI":"10.3390\/rs12203440","type":"journal-article","created":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T09:28:23Z","timestamp":1603186103000},"page":"3440","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["(Quasi-)Real-Time Inversion of Airborne Time-Domain Electromagnetic Data via Artificial Neural Network"],"prefix":"10.3390","volume":"12","author":[{"given":"Peng","family":"Bai","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering and Architecture, University of Cagliari, 09123 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3437-5826","authenticated-orcid":false,"given":"Giulio","family":"Vignoli","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering and Architecture, University of Cagliari, 09123 Cagliari, Italy"},{"name":"GEUS\u2014Geological Survey of Denmark and Greenland, 8000 Aarhus, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Viezzoli","sequence":"additional","affiliation":[{"name":"Aarhus Geophysics ApS, 8000 Aarhus, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jouni","family":"Nevalainen","sequence":"additional","affiliation":[{"name":"Oulu Mining School, University of Oulu, 90014 Oulu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8161-7667","authenticated-orcid":false,"given":"Giuseppina","family":"Vacca","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering and Architecture, University of Cagliari, 09123 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhdanov, M.S., Alfouzan, F.A., Cox, L., Alotaibi, A., Alyousif, M., Sunwall, D., and Endo, M. 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