An Uncertainty-Quantification Machine Learning Framework for Data-Driven Three-Dimensional Mineral Prospectivity Mapping-Reference-Cited by-同舟云学术

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An Uncertainty-Quantification Machine Learning Framework for Data-Driven Three-Dimensional Mineral Prospectivity Mapping

Published:2024-05-16 Issue:4 Volume:33 Page:1393-1411
ISSN:1520-7439
Container-title:Natural Resources Research
language:en
Short-container-title:Nat Resour Res

Author:

Zhang Zhiqiang,Wang Gongwen^ORCID,Carranza Emmanuel John M.,Du Jingguo,Li Yingjie,Liu Xinxing,Su Yongjun

Funder

Hebei Natural Science Foundation

Open Project Program of Hebei Province Collaborative Innovation Center for Strategic Critical Mineral Research

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11053-024-10349-x.pdf

Reference67 articles.

1. Agterberg, F. P. (1989). Computer programs for mineral exploration. Science, 245, 7681.

2. Anderson, E. D., Monecke, T., Hitzman, M. W., Zhou, W., & Bedrossian, P. A. (2017). Mineral potential mapping in an accreted island-arc setting using aeromagnetic data: An example from Southwest Alaska. Economic Geology, 112, 375–396.

3. Bergstra, J., Yamins, D. & Cox, D. D. (2013). Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms. In Proceedings of the 12th Python in science conference, 13, 20. Citeseer.

4. Breiman, L. (2001). Random forests. Machine Learning, 45, 5s32.

5. Burkin, J. N., Lindsay, M. D., Occhipinti, S. A., & Holden, E. J. (2019). Incorporating conceptual and interpretation uncertainty to mineral prospectivity modelling. Geoscience Frontiers, 10(4), 1383–1396.

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