Sun Yat-sen University | China
Xiao Fan is an Associate Professor and PhD advisor at the School of Earth Sciences and Engineering, Sun Yat-sen University, recognized for his contributions to mineral exploration, mathematical geoscience, and data-driven Earth system analysis. He received his bachelor’s and doctoral degrees in mineral resource exploration from China University of Geosciences (Wuhan), with joint doctoral training at the University of Ottawa. His research focuses on mineral system modeling, multi-physics numerical simulation, fractal and singularity analysis, machine-learning-based mineral prospectivity, and knowledge-driven geoscientific data integration. As a core member of major national innovation teams, he has led or participated in several national and provincial research projects and contributed to the creation of an advanced UAV-based geophysical–geochemical acquisition and processing platform. He has published 54 documents with 986 citations from 706 sources and holds an h-index of 19, along with multiple patents, software copyrights, and highly cited works in leading SCI journals. He serves on editorial boards and scientific committees, including roles as youth editor and guest editor, and has organized sessions for the International Geological Congress. His work advances quantitative mineral prediction and computational geoscience, contributing impactful models and methods that enhance understanding of mineralization processes and exploration efficiency.
Featured Publications
(2025). “Three-Dimensional Prospectivity Modeling of Jinshan Ag-Au Deposit, Southern China by Weights-of-Evidence.” Journal of Earth Science.
(2025). “Lithologic mapping of intermediate-acid intrusive rocks in the eastern Tianshan Gobi Desert using machine learning and multi-source data fusion.” Earth Science Frontiers.
(2025). “A DFT study on mechanisms of indium adsorption on sphalerite (100), (110), and (111) surfaces: Implications for critical metal mineralization.” Ore Geology Reviews.
(2025). “Data-driven expeditious mapping and identifying granites in covered areas via deep machine learning: A case study on the implications for geodynamics and mineralization of Eastern Tianshan.” Lithos.
(2025). “Numerical Modeling and Exploration Data Coupled-driven Mineral Prospectivity Mapping: A Case Study of Fankou Pb-Zn Deposit.” Geotectonica et Metallogenia.