Abstract

Quantitative assessment of deep mineral resources is important for the mineral exploration decisions of governments and companies. Complex structural controls make the volume method difficult to apply to hydrothermal gold deposits. Quantitative assessment of Au resources in hydrothermal goldfields remains a challenging task. Here we proposed a quantitative assessment approach for hydrothermal gold deposits via an improved volume method combined with 3D geology modeling. A highly reliable 3D model of ore-controlling structure was first constructed to obtain the spatial association between Au mineralization and structure. On this basis, we made an optimal quantitative estimation of the ore-bearing ratio and similarity index, which are the most critical parameters in the volume method, before evaluating gold resources. Specifically, (1) an ore-bearing ratio prediction model was established by support vector machine based on measured data and 3D models, which was further applied in predicting the ore-bearing ratio of the evaluation area at depth and peripherals; (2) the calculation of the similarity index was improved by novelly using the Mahalanobis distance of the regional geochemical data with regard to the similar geochemical anomalies of gold mineralization in adjacent space. This proposed method was applied to quantitatively assess the gold resources in the Jiaojia and Dayingezhuang goldfields in the Jiaodong gold province, China, where gold deposits show significant structure controls on mineralization. The results of gold grade prediction are statistically significantly correlated to measured values by the Wilcoxon signed rank test, showing the model for gold grade prediction can be extensible in the deep parts. Our approach produced similarity indices for the model and evaluation areas at Jiaojia and Dayingezhuang that accurately reflected the geology complex. The above lines suggest that the proposed approach can be used to estimate the gold resources of Jiaodong. Potential gold resources in the Jiaojia and Dayingezhuang districts were estimated to be 1221.82 t and 254.95 t.

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