*Article* **3D Mineral Prospectivity Mapping of Zaozigou Gold Deposit, West Qinling, China: Machine Learning-Based Mineral Prediction**

**Yunhui Kong <sup>1</sup> , Guodong Chen 1,\*, Bingli Liu 1,2, Miao Xie <sup>1</sup> , Zhengbo Yu <sup>1</sup> , Cheng Li 1,3, Yixiao Wu <sup>1</sup> , Yaxin Gao <sup>1</sup> , Shuai Zha <sup>1</sup> , Hanyuan Zhang <sup>1</sup> , Lu Wang <sup>1</sup> and Rui Tang <sup>1</sup>**


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

**Keywords:** 3D mineral prospectivity mapping; quantitative mineral resources prediction model; maximum entropy model; Gaussian mixture model; Zaozigou gold deposit
