气候变化领域集成服务门户(CCPortal): Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet

CCPortal

DOI	10.3390/rs15020439
	Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet
	Fu, Yufeng; Cheng, Qiuming; Jing, Linhai; Ye, Bei; Fu, Hanze
发表日期	2023
EISSN	2072-4292
卷号	15 期号:2
英文摘要	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.
关键词	Duolong ore district porphyry metallogenic system hyperspectral information extraction deep learning mineral prospectivity mapping
英文关键词	CU-AU DEPOSIT; HYPERSPECTRAL IMAGE; DISCRIMINATION; GEOCHRONOLOGY; EXPLORATION; ASTER; PERFORMANCE; GENESIS; MODEL; ROCKS
WOS研究方向	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000918842100001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283266
作者单位	China University of Geosciences - Beijing; China University of Geosciences - Beijing; Chinese Academy of Sciences; The Institute of Remote Sensing & Digital Earth, CAS
推荐引用方式 GB/T 7714	Fu, Yufeng,Cheng, Qiuming,Jing, Linhai,et al. Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet[J],2023,15(2).
APA	Fu, Yufeng,Cheng, Qiuming,Jing, Linhai,Ye, Bei,&Fu, Hanze.(2023).Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet.REMOTE SENSING,15(2).
MLA	Fu, Yufeng,et al."Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet".REMOTE SENSING 15.2(2023).