期刊
ORE GEOLOGY REVIEWS
卷 102, 期 -, 页码 811-817出版社
ELSEVIER
DOI: 10.1016/j.oregeorev.2018.10.006
关键词
Big data; Deep learning; Mapping mineral prospectivity; GIS
资金
- National Key Research and Development Program of China [2016YFC0600508]
- National Natural Science Foundation of China [41372007, 41522206]
- Natural Science Foundation of Hubei Province (China) [2017CFA053]
- Open Research Project of The Key Laboratory of Intelligent Geo-Information Processing 295 [KLIGIP-2017A03]
Identification of anomalies related to mineralization and integration of multi-source geoscience data are essential for mapping mineral prospectivity. In this study, we applied big data analytics and a deep learning algorithm to process geoscience data to identify and integrate anomalies related to skarn-type Iron mineralization in the southwestern Fujian metallogenic zone of China. Based on the geological setting and environment for the formation of skarn-type Iron mineralization, 42 relevant variables, including two geological, one geophysical, and 39 geochemical variables, were analyzed and integrated for detecting anomalies related to mineralization using a deep autoencoder network. The results indicate that the mapped prospectivity areas have a strong spatial relationship with the locations of known mineralization and demonstrate that big data analytics supported by deep learning methods is a potential technique to be considered for use in mineral prospectivity mapping.
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