Three-dimensional prospectivity modeling of the Jiaojia-type gold deposit, Jiaodong Peninsula, Eastern China: A case study of the Dayingezhuang deposit

Abstract

Abstract The Jiaojia-type gold deposits, hosting >80% of gold resource in the Jiaodong Peninsula, Eastern China, are characterized by veinlet- and disseminated-style mineralization associated with the Mesozoic detachment faults. In this study, we performed multi-constraint geological modeling and spatial analysis involving 3D buffer analysis, shape analysis, and field analysis for the Dayingezhuang gold deposit to quantitatively assess the gold distribution and its association with geological features. The obtained spatial data were further integrated into three dimensional (3D) prospectivity modeling by fuzzy weights-of-evidence (WofE) and continuous WofE methods to evaluate mineral potential. Our results determine a quantitative correlation between phyllic alteration thickness and tectono-geochemical anomaly to construct the geometric models of alteration zone. The hydrothermal intensity extracted from the models shows a bimodal distribution and it is significantly high in the center of No. 2 orebodies, indicating an overprinting gold mineralization. The spatial analysis on the Zhaoping fault reveals that the most probable locations for gold deposition were determined to be in segments of the Zhaoping fault with a slope of 20° to 40°, dip angle changes of −5°, and undulation of near 0\u202fm. All of the features likely result from structural controls on fluid flow and infiltration, as well as variations in the stability of Au-bearing complexes related to fault morphology. 3D prospectivity models generated by continuous transformed spatial evidence values with lower bias and uncertainty yielded a higher predictive efficiency than classified evidential layers. Our study not only highlights that gold enrichment of the Jiaojia-type deposit is essentially controlled by shape features of detachment faults, but also emphasizes the applicability of 3D prospectivity modeling in identifying potential gold mineralization at depth in the Jiaodong Peninsula.