Prospectivity modeling: From two-dimension to three-dimension

Yuan, Feng; Environments, Hefei Mine; Zhang, Mingming; Li, Xiaohui; Ge, Cui; Lu, Shan; JianShe, LI; YuZhang, Zhou; Lan, Xueyi

doi:10.18654/1000-0569/2019.12.18

Cited by 11 publications

(1 citation statement)

References 25 publications

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“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Gao

Wang

et al. 2023

Front. Earth Sci.

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The Weilasituo-bairendaba district is located at the eastern end of the Central Asian Orogenic Belt, which is an important component of the Cu-Pb-Zn polymetallic metallogenic belt on the Western slope of the Greater Xing’an Range in Inner Mongolia, China. The known Cu-Zn deposits such as the Weilasituo Cu-Zn deposit and the Bairendaba Ag-Pb-Zn deposit are the same tectonic-magmatic product. The district’s structure framework consists of the NE-trending regional faults, while the secondary faults provide channels and space for mineralization. The ore-bearing rocks are either Baoyintu Group gneisses or quartz diorites. The typical Cu-Zn deposits exhibit obvious Cu, Pb, Zn geochemical anomaly as well as obvious magnetic anomaly. The district-scale two-dimensional (2D) mineral prospectivity modeling has been reported. Nowadays, three-dimensional (3D) mineral prospectivity modeling is necessary and urgent. Integrated deposit geology and accumulated exploration data, the above four exploration criteria (regional fault, secondary fault, geochemical anomaly and magnetic susceptibility) are used for 3D mineral prospectivity modeling. Filtering (upward continuation, low pass filtering, two-dimensional empirical mode decomposition), magnetic inversion and 3D modeling techniques were used to construct geological models. Excellent machine learning algorithms such as random forest (RF) and XGBoost are applied. The two machine learning methods confirm each other to improve the accuracy of 3D mineral prospectivity modeling. In this paper, repeated random sampling and Bayesian optimization are combined to construct and tune models. This joint method can avoid the contingency caused by random sampling of negative samples, and can also realize automatic optimization of hyperparameters. The optimal models (RF28 and XGBoost11) were selected among thirty repeated training models for mineral prospectivity modeling. The obtained areas under the ROC curves of RF28 and XGBoost11 were 0.987 and 0.986, respectively. The prediction-area (P-A) plot and C-A fractal were used to delineate targets and grade targets. The targets were divided into Ⅰ-level targets and Ⅱ-level targets. The I- and II-targets are not only highly consistent with the known Cu-Zn deposits, but also exhibit obvious ore-forming geological features. The 3D targets are beneficial for Cu-Zn exploration in the Weilasituo-bairendaba district.

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