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

doi:10.1117/12.2667272

10 February 2023 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

Author Affiliations +

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 125523A (2023) https://doi.org/10.1117/12.2667272
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

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.

Citation Download Citation

Ya Guo, Shuai Zhang, Changliang Fu, and Yi Yang "GIS-based mineral prospectivity mapping: a systematic study on machine learning at Hezuo-Meiwu District, Gansu Province", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 125523A (10 February 2023); https://doi.org/10.1117/12.2667272

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