Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet
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 diag...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/2/439 |
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| author | Yufeng Fu Qiuming Cheng Linhai Jing Bei Ye Hanze Fu |
| author_facet | Yufeng Fu Qiuming Cheng Linhai Jing Bei Ye Hanze Fu |
| author_sort | Yufeng Fu |
| collection | DOAJ |
| description | 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–near-infrared–shortwave-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. |
| first_indexed | 2024-03-09T11:20:45Z |
| format | Article |
| id | doaj.art-f1a80b6f83bf4296b227c47fb8f97a26 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:20:45Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f1a80b6f83bf4296b227c47fb8f97a262023-12-01T00:20:50ZengMDPI AGRemote Sensing2072-42922023-01-0115243910.3390/rs15020439Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, TibetYufeng Fu0Qiuming Cheng1Linhai Jing2Bei Ye3Hanze Fu4School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, ChinaSchool of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaSchool of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, ChinaSchool of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, ChinaSeveral 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–near-infrared–shortwave-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.https://www.mdpi.com/2072-4292/15/2/439Duolong ore districtporphyry metallogenic systemhyperspectral information extractiondeep learningmineral prospectivity mapping |
| spellingShingle | Yufeng Fu Qiuming Cheng Linhai Jing Bei Ye Hanze Fu Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet Remote Sensing Duolong ore district porphyry metallogenic system hyperspectral information extraction deep learning mineral prospectivity mapping |
| title | Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet |
| title_full | Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet |
| title_fullStr | Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet |
| title_full_unstemmed | Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet |
| title_short | Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet |
| title_sort | mineral prospectivity mapping of porphyry copper deposits based on remote sensing imagery and geochemical data in the duolong ore district tibet |
| topic | Duolong ore district porphyry metallogenic system hyperspectral information extraction deep learning mineral prospectivity mapping |
| url | https://www.mdpi.com/2072-4292/15/2/439 |
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