Journal of Geo-information Science ›› 2019, Vol. 21 ›› Issue (6): 928-936.doi: 10.12082/dqxxkx.2019.190032

Previous Articles     Next Articles

Prediction Method of Tungsten-molybdenum Prospecting Target Area based on Deep Learning

Huihui CAI1,2(), Wei ZHU3,*(), Zixuan LI4,5, Yuanyuan LIU2, Longbin LI3, Chang LIU2   

  1. 1. China University of Geosciences (Beijing),Beijing 100083, China
    2. Development and Research Center of China Geological Survey, Beijing 100037, China
    3. Shanxi Center of Mineral Geological Survey, Xi'an 710068, China
    4. Department of Information Engineering, China University of Geosciences, Wuhan 430074, China
    5. National Engineering Research Center of Geographic Information System, Wuhan 430074, China;
  • Received:2019-01-18 Revised:2019-04-22 Online:2019-06-15 Published:2019-06-15
  • Contact: Wei ZHU;
  • Supported by:
    Development and promotion of intelligent geological survey system, No.DD20160355


With the exploration of minerals from shallow mines to deep concealed mines, from easy-to-identify mines to difficult-to-identify mines, the difficulty of prospecting is increasing, and geological experts are paying more and more attention to the application of new theories, new methods, and new technologies. As a frontier field and technology of artificial intelligence, deep learning has a unique advantage in realizing the intelligent forecasting and evaluation of mineral resources. The method uses normalized geochemical data as the training data to extract outliers by a neural network called Autoencoder and identify the favorable mineralization areas, and then realizes the qualitative prediction of mineral resources prospecting prospect. The research results show that after classifying the original data of 957 single elements geochemical anomalies and labeling of the model, the whole process automatically completes the learning and prediction in the "black box" of the computer, compared with the traditional prediction research method, this method of research is highly automated and objective. In addition, this paper uses the known mine sites to construct the training dataset, and uses the random forest method to predict the mineral resources prospecting target area in the prediction area, which provides a scientific basis for further narrowing the scope of the prospecting target area.

Key words: random forest method, deep learning, tung-molybdenum polymetallic mineral resources, big data, prediction, evaluation, Western Zhenan, Shaanxi