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究,为覆盖区找矿工作指明方向。本文以赵鹏大院士等提出的地质异常理论为指导,分析新达来草原覆盖区及邻区成矿多样性,总结矿化垂向分布规律,进而通过S-A多重分形滤波模型降低覆盖层对土壤地球化学测量数据和地面高精度地磁数据的干扰,识别和提取深部源引起的弱异常,并将提取的断层、侏罗纪中酸性岩体、岩脉、岩体与围岩接触带、PC1和PC2元素组合异常、高精度磁异常,以及成矿单元与非成矿单元的地理位置X-Y坐标作为随机森林模型的输入预测变量。本文采用SMOTE采样技术克服覆盖区矿床(点)数量少所造成的训练样本不足的缺陷,通过500次随机森林模拟来定量表征与成矿关系密切的综合地质异常,模拟结果的平均袋外误差为2.26%,AUC值达到0.972,且成矿有利度≥0.783的地质异常中分布有88.46%的已知矿床和矿点,说明该方法的有效性。为了进一步降低勘查风险,本文进行了风险与回报分析,发现除1个矿化点外,其余25个矿床和矿点均分布在回报值的正值区域,另有3个矿点和矿化点位于风险值的中高等级区域。在此基础上,利用处于低风险-高回报区的地质异常成矿有利度重新作图,最终圈定出找矿有利地段。

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.