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ZHOU Shuguang, ZHOU Kefa, CUI Yao, WANG Jinlin, WANG Wei, DING Jianli. Application of Logistic Regression Methods in Geochemical Data Analysis and Mineral Exploration: Example from Karamay Region[J]. Northwestern Geology, 2016, 49(1): 234-240.
Citation: ZHOU Shuguang, ZHOU Kefa, CUI Yao, WANG Jinlin, WANG Wei, DING Jianli. Application of Logistic Regression Methods in Geochemical Data Analysis and Mineral Exploration: Example from Karamay Region[J]. Northwestern Geology, 2016, 49(1): 234-240.
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Application of Logistic Regression Methods in Geochemical Data Analysis and Mineral Exploration: Example from Karamay Region

  • 1.

    Xinjiang Research Center for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China;Xinjiang University, Urumqi 830046, Xinjiang, China;University of Chinese Academy of Sciences, Beijing 100049, China

  • 2.

    Xinjiang Research Center for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China

  • 3.

    Xinjiang University, Urumqi 830046, Xinjiang, China

Received Date:  03 February 2015
Revised Date:  13 May 2015
Publish Date:  15 March 2016
    Abstract
  • Geochemical data is essential for mineral exploration, and one of the main challenges is how to identify the anomaly that was related to the formation or locations of mineral deposits. Many techniques have been developed to identify geochemical anomalies in the past years, but most of these techniques are designed for univariate data. To identify geochemical anomalies from multivariate geochemical data and to get gold deposits related information, logistic regression method is used to analyze geochemical data (sixteen hydrothermal/epithermal elements are included) of this study area. The results demonstrate that the developed logistic regression model is effective for geochemical anomalies identification and gold prediction, because the model can not only identify the geochemical anomalies where there are known gold deposits, but also identify other strong geochemical anomalies where there is no known deposit. Therefore, the logistic regression method is recommended to be used to geochemical anomalies identification and mineral prediction.
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