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Earth Science Informatics

, Volume 11, Issue 4, pp 553–566 | Cite as

Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China

  • Nannan Zhang
  • Kefa Zhou
  • Dong Li
Research Article
  • 131 Downloads

Abstract

Machine Learning technologies have the potential to deliver new nonlinear mineral prospectivity mapping (MPM) models. In this study, Back Propagation (BP) neural network Support Vector Machine (SVM) methods were applied to MPM in the Hatu region of Xinjiang, northwestern China. First, a conceptual model of mineral prospectivity for Au deposits was constructed by analysis of geological background. Evidential layers were selected and transformed into a binary data format. Then, the processes of selecting samples and parameters were described. For the BP model, the parameters of the network were 9–10 − 1; for the SVM model, a radial basis function was selected as the kernel function with best C = 1 and γ = 0.25. MPM models using these parameters were constructed, and threshold values of prediction results were determined by the concentration-area (C-A) method. Finally, prediction results from the BP neural network and SVM model were compared with that of a conventional method that is the weight- of- evidence (W- of- E). The prospectivity efficacy was evaluated by traditional statistical analysis, prediction-area (P-A) plots, and the receiver operating characteristic (ROC) technique. Given the higher intersection position (74% of the known deposits were within 26% of the total area) and the larger AUC values (0.825), the result shows that the model built by the BP neural network algorithm has a relatively better prediction capability for MPM. The BP neural network algorithm applied in MPM can elucidate the next investigative steps in the study area.

Keywords

Mineral prospectivity mapping Back propagation neural network Support vector machines Weight of evidence 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 41602339, U1503291), the Western Light Foundation of the Chinese Academy of Sciences (CAS; Grant No. XBBS-2014-19), the Xinjiang Uygur Autonomous Major Project (Grant No. 201330121-3), the National Basic Research Program of China (Grant No. 973Program2014CB440803) and the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19030204).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesXinjiangChina
  2. 2.Xinjiang Research Center for Mineral Resources, Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesXinjiangChina
  3. 3.Xinjiang Key Laboratory of Mineral Resources and Digital GeologyXinjiangChina
  4. 4.Key Laboratory of Coastal Zone Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of SciencesYantaiChina
  5. 5.University of Chinese Academy of SciencesBeijingChina

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