Journal of Jilin University(Earth Science Edition) ›› 2017, Vol. 47 ›› Issue (4): 1319-1330.doi: 10.13278/j.cnki.jjuese.201704307

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Application of PPC Model Combined with RCGA to Identify and Extract Geochemical Anomaly

Xiao Fan1,2, Chen Jianguo3,4   

  1. 1. School of Earth Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China;
    2. Guangdong Key Laboratory of Geological Process and Mineral Resources Exploration, Sun Yat-sen University, Guangzhou 510275, China;
    3. Resources Faculty, China University of Geosciences, Wuhan 430074, China;
    4. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
  • Received:2016-12-12 Online:2017-07-26 Published:2017-07-26
  • Supported by:
    Supported by the National Natural Science Foundation of China (41502310) and the Guangdong Natural Science Foundation (2015A030310246)

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Abstract: In order to identify and extract geochemical anomalies, a novel method, which combines projection pursuit classification (PPC), a dimension-reducing technology on high-dimensional data, with the real coded genetic algorithm (RCGA), has been proposed in this paper. We analyzed and discussed the key technological issues in geochemical anomaly identification and extraction using RCGA-PPC model based on the features of geochemical data. At the same time, an application software package was developed for the RCGA-PPC model on MATLAB. As a case study, stream sediment geochemical data in Gejiu, Yunnan, China, were tested with the RCGA-PPC model and Sn, Cu, Pb, Zn, As, Cd, etc. ore forming or/and mineralization associate elements were selected as the variables to recognize anomalies. The results show that most of the local higher optimal projection values are well coincident with the deposits location. Therefore, it can be concluded that RCGA-PPC model could be a useful method for geochemical anomaly identification and extraction.

Key words: projection pursuit classification, real coded genetic algorithm, geochemical data processing, anomaly identification and extraction, Gejiu, Yunnan

CLC Number: 

  • P631.8
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