Delineation of Integrated Anomaly with Generative Adversarial Networks and Deep Neural Networks in the Zhaojikou Pb-Zn Ore District,Southeast China |
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| Authors: | DUAN Jilin LIU Yanpeng ZHU Lixin MA Shengming GONG Qiuli Alla DOLGOPOLOVA Simone A LUDWIG |
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| Institution: | School of Earth Sciences, East China University of Technology, Nanchang 330013, China;State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China;Development Research Centre, China Geological Survey, Beijing 100037, China;Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, Langfang 065000, China;Department of Earth Sciences, Natural History Museum, London SW7 5BD, United Kingdom; Department of Computer Science, North Dakota State University, Fargo ND 58105, USA |
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| Abstract: | Geochemical maps are of great value in mineral exploration. Integrated geochemical anomaly maps provide comprehensive information about mapping assemblages of element concentrations to possible types of mineralization/ore, but vary depending on expert''s knowledge and experience. This paper aims to test the capability of deep neural networks to delineate integrated anomaly based on a case study of the Zhaojikou Pb-Zn deposit, Southeast China. Three hundred fifty two samples were collected, and each sample consisted of 26 variables covering elemental composition, geological, and tectonic information. At first, generative adversarial networks were adopted for data augmentation. Then, DNN was trained on sets of synthetic and real data to identify an integrated anomaly. Finally, the results of DNN analyses were visualized in probability maps and compared with traditional anomaly maps to check its performance. Results showed that the average accuracy of the validation set was 94.76%. The probability maps showed that newly-identified integrated anomalous areas had a probability of above 75% in the northeast zones. It also showed that DNN models that used big data not only successfully recognized the anomalous areas identified on traditional geochemical element maps, but also discovered new anomalous areas, not picked up by the elemental anomaly maps previously. |
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| Keywords: | deep learning deep neural networks generative adversarial networks geochemical map Pb-Zn deposit |
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