气候变化领域集成服务门户(CCPortal): Auto encoder generative adversarial networks - based mineral prospectivity mapping in Lhasa area, Tibet

CCPortal

DOI	10.1016/j.gexplo.2023.107326
	Auto encoder generative adversarial networks - based mineral prospectivity mapping in Lhasa area, Tibet
	Xie, Miao; Liu, Bingli; Wang, Lu; Li, Cheng; Kong, Yunhui; Tang, Rui
发表日期	2023
ISSN	0375-6742
EISSN	1879-1689
卷号	255
英文摘要	When conducting Mineral Potential Mapping (MPM) using multiple sources of data such as geology, geochem-istry, and geophysics, it often encounters the challenges of complex and diverse data distributions within these datasets. Enhancing the capability to extract nonlinear data features and further uncover metallogenic infor-mation is a crucial research objective. This study utilizes the C-A multifractal approach to extract anomalous information related to metallogenic elements, employs compositional data analysis methods for quantitatively extracting geochemical associations of mineralization, and utilizes GIS spatial analysis techniques to quantita-tively extract predictive indicators from various data sources, including geology, geophysics, and remote sensing, to construct an MPM prediction dataset. Building upon the foundation of AutoEncoder (AE), this study introduces a discriminator and employs the AEGAN (Auto Encoder Generative Adversarial Network) algorithm, which combines AutoEncoder and Generative Adversarial Network (GAN), for metallogenic prospectivity prediction in the Lhasa region. Compared to AE algorithms, AEGAN combines the strengths of AE and GAN, significantly improving the model's ability to reconstruct input data through the interaction between the generator and discriminator. Additionally, this study designs comparative experiments with AE, and the results demonstrate that the AEGAN model can more accurately identify the correlation between high anomaly areas and poly -metallic deposits, providing a more precise delineation of anomalous extents. The Area Under the Receiver Operating Characteristic Curve (AUC) further validates the superior performance of the AEGAN model. These findings indicate that the AEGAN model exhibits outstanding capabilities in learning the internal connections and features among multiple data sources, holding significant potential for practical applications in mineral exploration.
关键词	C -A fractal Compositional data analysis Deep learning Mineral prospectivity mapping
英文关键词	BIG DATA ANALYTICS; GEOCHEMICAL ANOMALIES; REGION; ZN; PB; RECOGNITION; DISTRICT; BELT
WOS研究方向	Geochemistry & Geophysics
WOS记录号	WOS:001097376900001
来源期刊	JOURNAL OF GEOCHEMICAL EXPLORATION
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283414
作者单位	Chengdu University of Technology; Peking University; China Geological Survey; Chinese Academy of Geological Sciences
推荐引用方式 GB/T 7714	Xie, Miao,Liu, Bingli,Wang, Lu,et al. Auto encoder generative adversarial networks - based mineral prospectivity mapping in Lhasa area, Tibet[J],2023,255.
APA	Xie, Miao,Liu, Bingli,Wang, Lu,Li, Cheng,Kong, Yunhui,&Tang, Rui.(2023).Auto encoder generative adversarial networks - based mineral prospectivity mapping in Lhasa area, Tibet.JOURNAL OF GEOCHEMICAL EXPLORATION,255.
MLA	Xie, Miao,et al."Auto encoder generative adversarial networks - based mineral prospectivity mapping in Lhasa area, Tibet".JOURNAL OF GEOCHEMICAL EXPLORATION 255(2023).