Quantification of Uncertainty Associated with Evidence Layers in Mineral Prospectivity Mapping Using Direct Sampling and Convolutional Neural Network

Mineral prospectivity mapping (MPM) mainly focuses on searching prospective areas for a particular type of mineral deposits. However, MPM is typically subject to uncertainties originated from conceptual mineral deposit models, geoscience data, and prediction models. This study utilizes a hybrid mode...

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Vydáno v:	Natural resources research (New York, N.Y.) Ročník 32; číslo 1; s. 79 - 98
Hlavní autoři:	Yang, Fanfan, Wang, Ziye, Zuo, Renguang, Sun, Siquan, Zhou, Bao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.02.2023 Springer Nature B.V
Témata:	Algorithms Artificial neural networks case studies Chemistry and Earth Sciences China Computer Science Computer simulation Earth and Environmental Science Earth Sciences Fossil Fuels (incl. Carbon Capture) Geography gold Mapping Mathematical Modeling and Industrial Mathematics Mineral deposits Mineral exploration Mineral Resources Neural networks Original Paper Physics prediction Prediction models risk Risk analysis river valleys Sampling Statistics for Engineering Sustainable Development Uncertainty Yangtze River China Yangtze River Direct sampling Mineral prospectivity mapping Uncertainty analysis Convolutional neural network
ISSN:	1520-7439, 1573-8981
On-line přístup:	Získat plný text
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