Metallogenic prediction based on the deep interest evolution network: A case study of supergenetic calcrete-hosted uranium deposits in Western Australia
ZHANG Chang-Jiang1,2(), HE Jian-Feng1,2,3(), NIE Feng-Jun1,2, XIA Fei1,2, LI Wei-Dong1,2, WANG Xue-Yuan1,2,3, ZHANG Xin1,2, ZHONG Guo-Yun1,2,3
1. Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System, East China University of Technology, Nanchang 330013, China 2. School of Information Engineering, East China University of Technology, Nanchang 330013, China 3. Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology, East China University of Technology, Nanchang 330013, China
Recommendation system algorithms, having recently garnered significant attention in the field of digital Earth science, are expected to be widely applied in metallogenic prediction. Traditional metallogenic prediction studies fail to fully mine the various types of semantic information in massive geoscience data. The deep interest evolution network (DIEN), as a recommendation system algorithm, can fully mine semantic information to predict user preferences. Therefore, this study employed the DIEN model as the prediction model and the semantic information extracted from bedrock interpretation as the ore-controlling elements according to the database provided by the Western Australian government. The model was trained to perform metallogenic prediction for the study area. The prediction results indicate that 92.95% of uranium ore occurrences fell within the medium-high probability zone in the prediction map, with some unknown zones also showing high prediction probabilities. After removing known uranium ore occurrences in some zones, the retrained model still yielded medium-high prediction probabilities in these zones. The results suggest that the DIEN can effectively mine semantic information in metallogenic prediction studies, and the DIEN model exhibits strong predictive capacity for the study area, providing a novel approach for metallogenic prediction studies.
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