气候变化领域集成服务门户(CCPortal): A deep-learning-based mineral prospectivity modeling framework and workflow in prediction of porphyry-epithermal mineralization in the Duolong ore District, Tibet

CCPortal

DOI	10.1016/j.oregeorev.2023.105419
	A deep-learning-based mineral prospectivity modeling framework and workflow in prediction of porphyry-epithermal mineralization in the Duolong ore District, Tibet
	Liu, Cai; Wang, Wenlei; Tang, Juxing; Wang, Qin; Zheng, Ke; Sun, Yanyun; Zhang, Jiahong; Gan, Fuping; Cao, Baobao
发表日期	2023
ISSN	0169-1368
EISSN	1872-7360
卷号	157
英文摘要	Machine learning (ML) is emerging as a highly effective technique for mineral exploration. However, mineral exploration poses several unique challenges to ML application, such as uncertain geological information in remote regions and imbalanced labeled training data. In this study, we developed a deep-learning framework - a self-attention back-propagation neural network (SA-BPNN) - which is used to automatically explore re-lationships among diverse features and improve the capability of information extraction. Moreover, we proposed a mineral prospectivity modeling workflow involving quantitative data + ML + expert experience for porphyry-epithermal deposits. Using quantitative data obtained from hyperspectral remote sensing, geochem-istry, and geophysics, we predicted ore-prospecting targets by applying the SVM, SA-BPNN, and U-Net models. Thereafter, we combined the model-based prediction with geological data to delineate the target areas. The model-based prediction by SVM, SA-BPNN, and U-Net occupy 1.73%, 1.40%, and 2.21% of the study area and contain 100%, 100%, and 80% of the known Cu-Au mineralization in the Duolong ore district in Tibet, respectively. The proposed SA-BPNN method, thus, achieved superior performance for mineral prospectivity modeling compared with alternative methods.
关键词	Machine learning Mineral prospectivity modeling Porphyry-epithermal deposits Self-attention Neural network Support vector machine
英文关键词	CU-AU DEPOSIT; NUJIANG METALLOGENIC BELT; NEURAL-NETWORKS; U-PB; GEOCHEMICAL CHARACTERISTICS; HYDROTHERMAL ALTERATION; CONCENTRATION AREA; COPPER-DEPOSIT; EXPLORATION; SUPPORT
WOS研究方向	Geology ; Mineralogy ; Mining & Mineral Processing
WOS记录号	WOS:000979976600001
来源期刊	ORE GEOLOGY REVIEWS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283248
作者单位	China Geological Survey; Institute of Geomechanics, Chinese Academy of Geological Sciences; Chinese Academy of Geological Sciences; China Geological Survey; Chinese Academy of Geological Sciences; Chengdu University of Technology; Liaocheng University
推荐引用方式 GB/T 7714	Liu, Cai,Wang, Wenlei,Tang, Juxing,et al. A deep-learning-based mineral prospectivity modeling framework and workflow in prediction of porphyry-epithermal mineralization in the Duolong ore District, Tibet[J],2023,157.
APA	Liu, Cai.,Wang, Wenlei.,Tang, Juxing.,Wang, Qin.,Zheng, Ke.,...&Cao, Baobao.(2023).A deep-learning-based mineral prospectivity modeling framework and workflow in prediction of porphyry-epithermal mineralization in the Duolong ore District, Tibet.ORE GEOLOGY REVIEWS,157.
MLA	Liu, Cai,et al."A deep-learning-based mineral prospectivity modeling framework and workflow in prediction of porphyry-epithermal mineralization in the Duolong ore District, Tibet".ORE GEOLOGY REVIEWS 157(2023).