基于地质异常的内蒙古新达来草原覆盖区钼铜多金属矿产定量预测

内蒙古新达来草原覆盖区位于古亚洲成矿域的二连—东乌旗钼铜多金属成矿带西段,分布有乌兰德勒、准苏吉花等与中酸性侵入体有关的内生金属矿床,成矿地质条件优越.然而,因受牧草和第四系大面积覆盖的影响,该区各种成矿/示矿信息具有间接、叠加、隐蔽、微弱、不完整等特点,给找矿勘查带来更大的不确定性和风险.因此,必须加强矿产定量预测研...

Quantitative prediction of molybdenum-copper polymetallic mineral resources in the Xindalai grassland-covered area of Inner Mongolia based on geological anomalies

The Xindalai grassland-covered area of Inner Mongolia, in the western part of the Erlian-Dongwuqi molybdenum-copper polymetallic belt of the Paleo-Asian metallogenic domain, distributes endogenous granophile metal deposits with favorable ore-forming conditions, such as the Wulandele copper-molybdenum deposit and the Zhunsujihua molybdenum deposit. However, due to the influence of large herbage and Quaternary overlays, all kinds of mineralization/ore indicators in this area are indirect, mixed, concealed, weak or incomplete, causing considerable uncertainty and hazard to ore prospecting and exploration. Therefore, it is necessary to develop a quantitative prediction model to guide ore prospecting in the overlay area. In this paper, guided by the geological anomaly theory developed originally by Zhao et al., we analyzed the diversity of mineralization in Xindalai and its adjacent areas and summarized the vertical distribution of mineralization data. Using the S-A multifractal filtering model, the overlay interference on soil geochemical and high-precision magnetic survey data is reduced, and weak anomalies from deep sources are identified and extracted. The extracted information on faults, Jurassic granitic rocks, dykes and rock mass-wall rock contact zones, as well as on PC1-PC2 element combination anomalies, high-precision geomagnetic anomalies, and geographic location (X-Y coordinates) of metallogenic and non-metallogenic units, were taken as input variables using the random forest (RF) prediction method. The SMOTE sampling technology was used to overcome training sample insufficiency caused by limited number of ore deposits/occurrences in the grassland-covered area. Eventually the comprehensive geo-anomalies closely related to mineralization were quantified after five hundred iterations in the RF simulation. The simulation results show that the average OOB error and ACU value were 2.26% and 0.972, respectively, and 88.46% of known ore deposits/occurrences correspond to geo-anomalies with metallogenic advantage ≥0.783, demonstrating the effectiveness of the prediction method. In order to further reduce the exploration risk, we performed a risk-return analysis to show that 25 out of 26 ore deposits/occurrences were distributed in the positive return range, and only 3 ore occurrences were associated with medium to high risk values. On this basis, we used the metallogenic advantages of geo-anomalies associated with low-risk, high-return areas to re-map the grassland-covered area and ultimately delineated the preferable ore-finding district.