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Geochemical Features and Metallogenic Prediction of the Caixiashan‐Weiquan Area in the East Tianshan Region
Author(s) -
Lu LU,
Lixin ZHU,
Keyan XIAO,
Shengming MA,
Jiangning YIN,
Mingzuan XU
Publication year - 2012
Publication title -
acta geologica sinica ‐ english edition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 61
eISSN - 1755-6724
pISSN - 1000-9515
DOI - 10.1111/j.1755-6724.2012.00714.x
Subject(s) - geology , mineralization (soil science) , artificial neural network , mineral exploration , geochemistry , mining engineering , mineral , sediment , mineralogy , pattern recognition (psychology) , artificial intelligence , geomorphology , soil science , computer science , soil water , materials science , metallurgy
The Caixiashan‐Weiquan area is an important ore concentration area in the eastern Tianshan metallogenic belt. Firstly, this paper studies geochemical features of 1564 samples of 1:2 stream sediments of the Matoutan mapsheet, where the Caixiashan and Weiquan deposits are located. Processing, analysis and explanation of exploration geochemical data play an important role in the procedure of finding the ore, which are related to whether the measured elements content of geochemical samples can effectively guide the work of mineral exploration. As a highly nonlinear dynamical system, the neural network is more analogous to the human brains in terms of principles and features compared with conventional geochemical approaches. It can adapt itself to the environment, sum up laws, complete pattern recognition. Secondly, the authors used the Kohonen neural network to classify all samples based on 10 mineralization elements of stream sediment samples in order to determine possible mineral ores, reduce the scope of ore targets and study indicator elements of the ninth group of samples, which is the mostly closest to the deposit The results show that the neural network can delineate metallogenic prospective areas and is effective in the discovery of deep geochemical information.