Dr. Changliang Fu Profile

Dr. Changliang Fu

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Proceedings Article | 10 February 2023 Paper

GIS-based mineral prospectivity mapping: a systematic study on machine learning at Hezuo-Meiwu District, Gansu Province

Ya Guo, Shuai Zhang, Changliang Fu, Yi Yang

Proceedings Volume 12552, 125523A (2023) https://doi.org/10.1117/12.2667272

KEYWORDS: Minerals, Machine learning, Gold, Geographic information systems, Deep learning

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Mineral prospectivity mapping (MPM) has been an essential part of mineral exploration; various algorithms have been introduced for detecting mineralization related anomalies from multi-geoinformation including geology, geochemistry, geophysics and remote sensing dataset. With much attention paid to technical development of methods used in MPM, this study proposed new insights into the mineral prospectivity mapping based on our previous studies regarding the applications of different machine learning algorithms for prospects demarcation of the Hezuo-Meiwu District, West Qinling Orogen, China. With applied algorithms, such as maximum entropy model (MaxEnt), random forest (RF), deep auto-encoder network (DAE), convolutional auto-encoder network (CAE), convolutional neural network (CNN) etc., the thesis of this paper highlights the importance of datasets collected and proposed a shift to research on interpretable learning.

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