3D Mineral Prospectivity Mapping of Zaozigou Gold Deposit, West Qinling, China: Machine Learning-Based Mineral Prediction
نویسندگان
چکیده
This paper focuses on researching the scientific problem of deep extraction and inference favorable geological geochemical information about mineralization at depth, based which a mineral resources prediction model is established machine learning approaches are used to carry out quantitative prediction. The main contents include: (i) discussing method 3D anomaly under multi-fractal content-volume (C-V) models, extracting 12 element anomalies constructing data volume for laying foundation distribution association; (ii) association characteristics primary halos inferring metallogenic factors compositional analysis (CoDA), including quantitatively associations corresponding ore-bearing structures (Sb-Hg) data-driven CoDA framework, identifying front halo (As-Sb-Hg), near-ore (Au-Ag-Cu-Pb-Zn) tail (W-Mo-Co-Bi), provide indicators haloes’ structural depth; (iii) establishing model, constructed by five as input variables: fracture buffer zone, structures, Au anomaly, Au-Ag-Cu-Pb-Zn ratio (As-Sb-Hg)/(W-Mo-Bi); (iv) three-dimensional MPM maximum entropy (MaxEnt) Gaussian mixture (GMM), delineating exploration targets depth. results show that C-V can identify extract in space reliably, methods MaxEnt GMM have high performance MPM.
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ژورنال
عنوان ژورنال: Minerals
سال: 2022
ISSN: ['2075-163X']
DOI: https://doi.org/10.3390/min12111361