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

• Published in: Natural resources research (New York, N.Y.) Vol. 32; no. 1; pp. 79 - 98
• Main Authors: Yang, Fanfan, Wang, Ziye, Zuo, Renguang, Sun, Siquan, Zhou, Bao
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2023; Springer Nature B.V
• Subjects: 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

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...