Geology Spatial Data Mining Method for Regional Metallogenic Prediction

详细信息   

• 责任者：He Binbin (Institute of Geo-Spatial Information Science and Technology ; University of Electronic Science and Technology of China ; Chengdu 611731 ; China) ; Cui Ying
• 刊名：Advances in Earth Science
• 出版年：2011
• 卷期：26(6)
• 关键词：成矿预测 ; metallogenic prediction ; 空间数据库 ; spacial database
• 页码：p.615-623
• 图表：6 illus., 2 tables, 32 refs.
• 分类号：1900
• 出版序号：2115
• issnNo：ISSN1001-8166
• isbnNo：CN62-1091/P

摘要

Spatial data mining (SDM) refers to extracting and "mining" the hidden,  implicit,  valid,  novel and interesting spatial or non-spatial patterns or rules from a large amount of,  incomplete,  noisy,  fuzzy,  random,  and practical spatial databases. At present, SDM mainly concentrates on the efficiency of mining algorithm itself. Another important issue-application in SDM-has not been paid much attention to. Geological data are typical spatial data, which include geological, geophysical, geochemical, and remote sensing data. The statistical and mathematical approaches that have developed recently for multi-resources geological spatial data integration include weights-of-evidence modeling, and the logistic regression modeling. The fuzzy logic model, artificial neural networks model and the fractal method have been applied in the prediction and assessment of mineral resources potential.