摘要
The North China district has been subjected to significant research with regard to the ore-forming dynamics,processes,and quantitative forecasting of gold deposits;it accounts for the highest number of gold reserves and annual products in China.Based on the top-level design of geoscience theory and the method adopted by the National Key R&D Project(deep process and metallogenic mechanism of North China Craton(NCC)metallogenic system),this paper systematically collects and constructs the geoscience data(district,camp,and deposit scales)in four key gold districts of North China(Jiaojia-Sanshandao,Southern Zhaoping,Wulong,and Qingchengzi).The settings associated with the geological dynamics of gold deposits were quantitatively and synthetically analyzed,namely:NCC destruction,metallogenic events,genetic models,and exploration models.Three-dimensional(3D)and four-dimensional(4D)geological modeling was performed using the big data on the districts,while the district-scale 3D exploration criteria were integrated to construct a quantitative exploration model.Among them,FLAC3D modelling and the Geo Cube software(version 3.0)were used to implement the numerical simulation of the 3D geological models and the constraints of the fluid saturation parameters of the Jiaojia fault to reconstruct the 4D fault structure models of the Jiaojia fault(with a depth of 5000 m).Using Geo Cube3.0,multiple integration modules(general weights of evidence(Wof E),Boost Wof E,Fuzzy Wof E,Logistic Regression,Information Entropy,and Random Forest)and exploration criteria were integrated,while the C-V fractal classification of A,B and C targets in four districts was carried out.The research results are summarized in the following four areas:(1)Four gold districts in the study area have more than three targets(the depth is 3000 m),and the class A,B and C targets exhibit a good spatial correlation with gold bodies that are controlled by mining engineering at depths greater than 1000 m.(2)The Boost Wof E method was used to identify the target optimi
基金
supported by the National Key R&D Program of China(Grant Nos.2016YFC0600107&2016YFC0600108)。
