{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T08:04:19Z","timestamp":1784534659791,"version":"3.55.0"},"reference-count":73,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Second Tibetan Plateau Scientific Expedition and Research","award":["2019QZKK0806"],"award-info":[{"award-number":["2019QZKK0806"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research","award":["41972308"],"award-info":[{"award-number":["41972308"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019QZKK0806"],"award-info":[{"award-number":["2019QZKK0806"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41972308"],"award-info":[{"award-number":["41972308"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Several large-scale porphyry copper deposits (PCDs) with high economic value have been excavated in the Duolong ore district, Tibet, China. However, the high altitudes and harsh conditions in this area make traditional exploration difficult. Hydrothermal alteration minerals related to PCDs with diagnostic spectral absorption features in the visible\u2013near-infrared\u2013shortwave-infrared ranges can be effectively identified by remote sensing imagery. Mainly based on hyperspectral imagery supplemented by multispectral imagery and geochemical element data, the Duolong ore district was selected to conduct data-driven PCD prospectivity modelling. A total of 11 known deposits and 17 evidential layers of multisource geoscience information related to Cu mineralization constitute the input datasets of the predictive models. A deep learning convolutional neural network (CNN) model was applied to mineral prospectivity mapping, and its applicability was tested by comparison to conventional machine learning models, such as support vector machine and random forest. CNN achieves the greatest classification performance with an accuracy of 0.956. This is the first trial in Duolong to conduct mineral prospectivity mapping combined with remote imagery and geochemistry based on deep learning methods. Four metallogenic prospective sites were delineated and verified through field reconnaissance, indicating that the application of deep learning-based methods in PCD prospecting proposed in this paper is feasible by utilizing geoscience big data such as remote sensing datasets and geochemical elements.<\/jats:p>","DOI":"10.3390\/rs15020439","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T03:11:02Z","timestamp":1673493062000},"page":"439","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0821-4591","authenticated-orcid":false,"given":"Yufeng","family":"Fu","sequence":"first","affiliation":[{"name":"School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuming","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China"},{"name":"State Key Lab of Geological Processes and Mineral Resources, China University of Geosciences, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linhai","family":"Jing","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bei","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanze","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bolouki, S.M., Ramazi, H.R., Maghsoudi, A., Pour, A.B., and Sohrabi, G. 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