Application of Artificial Neural Network for Gold–Silver Deposits Potential Mapping: A Case Study of Korea
作者： Hyun-Joo Oh , Saro Lee
DOI: 10.1007/S11053-010-9112-2
关键词: Statistics 、 Spatial database 、 Mathematics 、 Mineral potential 、 Geographic information system 、 Geospatial analysis 、 Data mining 、 Training set 、 Artificial neural network 、 Mineral resource classification 、 Test set
摘要: The aim of this study is to analyze hydrothermal gold–silver mineral deposits potential in the Taebaeksan mineralized district, Korea, using an artificial neural network (ANN) and a geographic information system (GIS) environment. A spatial database considering 46 Au Ag deposits, geophysical, geological, geochemical data was constructed for area GIS. geospatial factors were used with ANN potential. randomly divided into training set (70%) test (30%) validate predicted map. Four different datasets determined from likelihood ratio weight evidence models applied effect training. Then, index (MPI) calculated trained back-propagation weights, maps (MPMs) GIS four cases. MPMs then validated by comparison occurrences. validation results gave respective accuracies 73.06, 73.52, 70.11, 73.10% some cases showed less sensitive than evidence. Overall, selected 10% low high value MPML MPMW higher accuracy (73.52 73.10%) those (73.06 70.11%, respectively) known MPIL MPIW.
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