3D modeling of detachment faults in the Jiaodong gold province, eastern China: A Bayesian inference perspective and its exploration implications

Abstract

The world-class Jiaodong gold province, which is located in the Jiaodong Peninsula, eastern North China Craton, hosts > 5,000 t of gold resources. The major gold mineralization in this area is mainly controlled by NE-NNE faults, such as the Zhaoping, Jiaojia, and Sanshandao faults. While intensive efforts have been devoted to the investigation of the architecture of the three detachment faults, the fault geometry at depth remains highly uncertain and hotly controversial due to the scarcity of observations, non-uniqueness of the geophysical inversion, and underutilization of the geological knowledge. Along these lines, the deep three-dimensional (3D) geometry of detachment faults from a Bayesian inference perspective is reconstructed in this work. In the Bayesian framework, the multi-source information and the prior geological knowledge are combined into an integrated probabilistic model and the posterior probability distribution of detachment faults is inferred. The resulting posterior distribution permits an estimation of the uncertainty (information entropy) of the 3D fault geometry and the expected shape undulations compared with a prior reference model. More specifically, it demonstrates that specific and detailed 3D models of faults could be inferred by tracking surfaces with maximum information entropy of the geological units. The reconstructed models exhibit detailed 3D geometry of the fault surfaces and their reliability is validated by a recent drill hole at ∼−2,300 m depth. In terms of the reconstructed 3D models, the dip variations are analyzed and the possible stress directions at deep zones of the three faults are discussed. The shape features of the reconstructed faults are also quantified, which indicates that the fault sections with a steep-to-gentle transition in dip angle are closely associated with the gold mineralization. The 3D gold prospectivity mapping with the spatial features as predictor variables, highlights seven deep prospecting targets, which are generally consistent with the gently dipping zones and beneath the known orebodies.