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Natural Resources Research

, Volume 28, Issue 4, pp 1285–1298 | Cite as

Mapping Geochemical Anomalies Through Integrating Random Forest and Metric Learning Methods

  • Ziye Wang
  • Renguang ZuoEmail author
  • Yanni DongEmail author
Original Paper

Abstract

Extracting geochemical anomalies from geochemical exploration data is one of the most important activities in mineral exploration. Geochemical anomaly detection can be regarded as a binary classification problem. The similarity between geochemical samples can be measured by their distance. The key issue of this classification is to find the intrinsic relationship and distance between geochemical samples to separate geochemical anomalies from background. In this paper, a hybrid method that integrates random forest and metric learning (RFML) is used to identify geochemical anomalies related to Fe-polymetallic mineralization in Southwest Fujian Province of China. RFML does not require any specific statistical assumption on geochemical data, nor does it depend on sufficient known mineral occurrences as the prior knowledge. The geochemical anomaly map obtained by the RFML method showed that the known Fe deposits and the generated geochemical anomaly area have strong spatial association. Meanwhile, the receiver operating characteristic curves for the results of RFML and another method, namely maximum margin metric learning, indicated that the RFML method exhibited better performance, suggesting that RFML can be effectively applied to recognize geochemical anomalies.

Keywords

Mineral exploration Machine learning Random forest Metric learning Geochemical anomalies GIS Fujian 

Notes

Acknowledgments

We thank Prof. John Carranza, Dr. M. Yousefi and an anonymous reviewer whose comments and suggestions helped us improve this study. This study was jointly supported by the National Natural Science Foundation of China (41772344, 61801444), the Natural Science Foundation of Hubei Province (China) (2017CFA053), the Hong Kong Scholars Program (XJ2018012), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (CUG170687) and the Most Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences (MSFGPMR03-3).

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Copyright information

© International Association for Mathematical Geosciences 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Geological Processes and Mineral ResourcesChina University of GeosciencesWuhanChina
  2. 2.Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and GeomaticsChina University of GeosciencesWuhanChina
  3. 3.Department of Land Surveying and Geo-InformaticsThe Hong Kong Polytechnic UniversityHong KongChina

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