Analysis of Regional Potential Mapping Factors of Metal Deposits using Machine Learning

머신러닝을 이용한 광역 금속 광상 배태 잠재성 평가 인자 분석

  • Park, Gyesoon (Convergence Research Center for Development of Mineral Resources, Korea Institute of Geoscience and Mineral Resources)
  • 박계순 (한국지질자원연구원, DMR융합연구단)
  • Received : 2020.06.11
  • Accepted : 2020.08.24
  • Published : 2020.08.31


The genesis of ore bodies is a very diverse and complex process, and the target depth of mineral exploration increases. These create a need for predictive mineral exploration, which may be facilitated by the advancement of machine learning and geological database. In this study, we confirm that the faults and igneous rocks distributions and magnetic data can be used as input data for potential mapping using deep neural networks. When the input data are constructed with faults, igneous rocks, and magnetic data, we can build a potential mapping model of the metal deposit that has a predictive accuracy greater than 0.9. If detailed geological and geophysical data are obtained, this approach can be applied to the potential mapping on a mine scale. In addition, we confirm that the magnetic data, which provide the distribution of the underground igneous rock, can supplement the limited information from the surface igneous rock distribution. Therefore, rather than simply integrating various data sets, it will be more important to integrate information considering the geological correlation to genesis of minerals.


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