39 Papers
6 Citations
Fan Yang is an academic researcher from China University of Geosciences (Beijing). The author has contributed to research in topics: Craton & Zircon. The author has an hindex of 13, co-authored 27 publications. Previous affiliations of Fan Yang include University of Adelaide & Lanzhou University.
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Papers
3D geological modeling for prediction of subsurface Mo targets in the Luanchuan district, China
Gongwen Wang,Ruixi Li,Emmanuel John M. Carranza,Shouting Zhang,Changhai Yan,Yanyan Zhu,Jianan Qu,Dongming Hong,Yaowu Song,Jiangwei Han,Zhenbo Ma,Hao Zhang,Fan Yang +12 more
TL;DR: In this article, a 3D district-scale geoscience information for the Luanchuan Mo district was integrated for understanding the development of its regional geology and ore-forming processes and for decision-making about potential targets for mineral exploration.
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Mesozoic magmatism in the eastern North China Craton: Insights on tectonic cycles associated with progressive craton destruction
TL;DR: In this paper, a suite of intrusive and volcanic suites including granite, gabbro, diorite, basalt, andesite, volcanic tuff, agglomerate, siliceous tuff and trachyte from the southern Yishui and Juxian domains were investigated to gain insights into the Mesozoic tectonic evolution of the eastern NCC in relation to decratonization.
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Data-driven Mineral Prospectivity Mapping by Joint Application of Unsupervised Convolutional Auto-encoder Network and Supervised Convolutional Neural Network
Shuai Zhang,Shuai Zhang,Emmanuel John M. Carranza,Hantao Wei,Keyan Xiao,Fan Yang,Fan Yang,Jie Xiang,Shihong Zhang,Yang Xu +9 more
TL;DR: In this paper, two simple unsupervised convolutional auto-encoder networks were constructed to distinguish patches of tif image (i.e., nine predictive evidence maps forming a tif-format image) with nine channels that have high reconstructed errors, which represent prospective areas.
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Integration of auto-encoder network with density-based spatial clustering for geochemical anomaly detection for mineral exploration
TL;DR: Differences between the anomaly maps indicate that the compositional nature of geochemical data affects the performance of multivariate geochemical anomaly detection, although the assessment, by receiver operating characteristics analysis, of the geochemical anomalies derived using the different methodologies described implies that the detected geochem anomalies are related to Au mineralization.
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Maximum Entropy and Random Forest Modeling of Mineral Potential: Analysis of Gold Prospectivity in the Hezuo–Meiwu District, West Qinling Orogen, China
TL;DR: In this paper, the authors compared the performance of the random forest (RF) and maximum entropy (MaxEnt) algorithms using gold deposit occurrences within the Hezuo-Meiwu district, West Qinling Orogen, China.
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