Resource prediction and assessment based on 3D/4D big data modeling and deep integration in key ore districts of North China

Abstract

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 GeoCube 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 GeoCube3.0, multiple integration modules (general weights of evidence (WofE), Boost WofE, Fuzzy WofE, 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 WofE method was used to identify the target optimization in 3D spaces (at depths of 3000-5000 m) of the Jiaojia-Sanshandao, Southern Zhaoping, and Wulong districts. (3) The general WofE method is based on the Bayesian theory in 3D space and provides robust integration and target optimization that are suitable for the Jiaojia-Sanshandao and Southern Zhaoping districts in the Jiaodong area; it can also be applied to the Wulong district in the Liaodong area using a quantitative genetic model and an exploration model. Random forest is a multi-objective integration and target optimization method for 3D spaces, and it is suitable for the complex exploration model in the Qingchengzi district of the Liaodong area. The genetic model and exploration criteria associated with the exploration model of the Qingchengzi district were constrained by the common characteristics of the gold fault structure, magmatic rock emplacement in North China, and the strata fold and interlayer detachment structure. (4) Based on the gold...

Resource-environment joint forecasting using big data mining and 3D/4D modeling in Luanchuan mining district, China

“…The methodology of geoscience big data in the study area is related to mineral resource prediction and evaluation , including 3D geological modeling, forward calculation, and constrained inversion of the 3D geophysics interpretation using the geological model and metallogenic model with Loop 3D methods, geochemistry, and remote sensing interpretation, etc., combined with self-developed GeoCube3.0 software with seven integration methods [6,14].…”

“…A series of metallogenic theories and technologies were constructed, for example, Ye et al [2] built theory and method of prospecting prediction in mineral exploration zone, and the ore-forming dynamic background, process, and quantitative evaluation of large and superlarge deposits [3][4][5]. The 13 th five-year national key research and development plan "deep resources exploration and exploitation" has promoted the deep (less than 3000 m) resource prospecting, prediction, and evaluation of key districts in China [6][7][8][9][10][11][12][13][14][15].…”

“…Geological Cloud 3.0 developed by China Geology Survey can build realtime and multi-scale (region, district, camp, deposit) geological mapping and the 3D borehole, orebody and mining modeling. Based on the metallogenic system and mineral system theories, 3D/4D modeling has been used to construct an exploration model of mineral deposits [13,14].…”

3D mineral exploration Cu-Zn targeting with multi-source geoscience datasets in the Weilasituo-bairendaba district, Inner Mongolia, China

“…With the development of computational technology and the continuous accumulation of geosciences datasets, mineral exploration has developed from near-surface to subsurface, from 2D to 3D, from qualitative to quantitative (Yuan et al, 2019;Zhang Z Q et al, 2021). 3D mineral prospectivity modeling is developed based on the 3D geological modeling and they are both widely applied in the mineral exploration (Houlding, 1994;Li et al, 2015;Xiao et al, 2015;Li et al, 2016;Wang G W et al, 2017;Yang et al, 2017;Mao et al, 2019;Wang et al, 2021;Zhang Z Q et al, 2021;Gao et al, 2023). Since the 1990s, various knowledge-and data-driven learning models have been applied to conduct mineral prospectivity modeling.…”

“…Knowledge-driven methods include evidential belief functions (An et al, 1992) and fuzzy logic (Bonham-Carter, 1994). Data-driven learning methods include the WofE model, the fuzzy WofE model, neural networks, random forests (RF), logistic regression, support vector machines, the certainty factor model, evidence theory and the prospecting cost-benefit strategy (Zuo and Carranza, 2011;Li et al, 2015;Xiao et al, 2015;Li et al, 2016;Zhang et al, 2016;Hariharan et al, 2017;Yang et al, 2017;Wang et al, 2021;Zhang C J et al, 2021). Based on the supervised algorithms, some derivative algorithms such as the semi-supervised random forests, one-class support vector machine and isolation forest have occurred (Chen and Wu, 2017;Chen and Wu, 2019;Wang et al, 2020).…”