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

doi:10.13745/j.esf.sf.2021.1.16

摘要/Abstract

摘要：

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。

关键词: 地质异常, 矿产定量预测, 风险与回报分析, 新达来草原覆盖区

Abstract:

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.

Key words: geo-anomaly, quantitative prediction of mineral resources, risk-return analysis, Xindalai grassland-covered area

中图分类号:

P612
P628

夏庆霖, 赵梦余, 王孝臣, 冷帅, 李童斐, 熊双才. 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[J]. 地学前缘, 2021, 28(3): 56-66.

XIA Qinglin, ZHAO Mengyu, WANG Xiaochen, LENG Shuai, LI Tongfei, XIONG Shuangcai. Quantitative prediction of molybdenum-copper polymetallic mineral resources in the Xindalai grassland-covered area of Inner Mongolia based on geological anomalies[J]. Earth Science Frontiers, 2021, 28(3): 56-66.

图/表 13

图1 新达来草原覆盖区地质矿产简图(据文献[4]修改) Ⅰ—华北板块北缘断裂;Ⅱ—西拉木伦河断裂;Ⅲ—二连—贺根山断裂;Ⅳ—乌努尔—鄂伦春断裂;Ⅴ—德尔布干断裂;Ⅵ—额尔古纳断裂;Ⅶ—大兴安岭主脊断裂。1—洪冲积砂砾;2—伊尔丁组;3—白音高老组;4—宝力高庙组;5—泥鳅河组;6—巴彦呼舒组;7—二叠纪碱长花岗岩;8—二叠纪二长花岗岩;9—二叠纪黑云母二长花岗岩;10—二叠纪黑云母花岗岩;11—二叠纪花岗闪长岩;12—二叠纪石英闪长岩;13—石炭纪黑云母花岗岩;14—石炭纪黑云母二长花岗岩;15—背斜及编号;16—向斜及编号;17—逆断层及编号;18—平移断层及编号;19—实测性质不明断层及编号;20—推测断层及编号;21—不整合线;22—侵入界线;23—韧性剪切带及编号;24—金矿产地;25—钼多金属矿产地;26—铅锌多金属矿产地;27—研究区位置。

Fig.1 Simplified metallogenic map of the Xindalai grassland-covered area. Modified after [4].

图2 新达来草原覆盖区矿化垂向分布图 ①—乌日尼图式钼多金属矿;②—陶木式铁多金属矿;③—准苏吉花式钼铜矿;④—乌兰德勒式钼铜矿。

Fig.2 Vertical distribution of orebodies in the Xindalai grassland-covered area

图3 不同过采样比率预测误差曲线

Fig.3 The prediction error curves with different over-sampling ratios

图4 地质要素缓冲区分布图 (a)—NW向、NE向和近EW向断层;(b)—花岗岩体与地层接触带;(c)—石英脉、花岗斑岩脉等岩脉;(d)—燕山期花岗岩体。

Fig.4 Distribution of buffer zones for different geological features

图5 化探元素主成分S-A异常图 (a)—PC1元素组合异常;(b)—PC2元素组合异常。

Fig.5 The S-A anomaly distribution map of main geochemical elements

图6 高精度地磁数据S-A异常图 (a)—ΔT背景;(b)—ΔT异常。

Fig.6 The S-A anomaly distribution map of high-precision geomagnetic data

图7 误差率与决策树数量关系曲线

Fig.7 The relation curves between prediction error and decision tree number for different testing conditions

表1 随机森林模型性能参数

Tab1e 1 RF model performance parameters

观测	预测		分类错误率/%
观测	不含矿	含矿	分类错误率/%
不含矿	156	8	4.88
含矿	1	233	0.43
OOB error	2.26%
AUC	0.972

图8 预测结果的ROC曲线

Fig.8 ROC curve for mineral resources prediction

图9 基于SMOTE数据集的随机森林方法提取的综合地质异常

Fig.9 Extracted comprehensive geo-anomalies using RF method based on the SMOTE dataset

图10 回报与风险分析结果图 (a)—回报值;(b)—风险值。

Fig.10 Results of return (a) and risk (b) analysis

图11 风险VS回报分析图 Ⅰ—低风险-低回报区;Ⅱ—低风险-高回报区;Ⅲ—高风险-低回报区;Ⅳ—高风险-高回报区。

Fig.11 Risk vs. return scatter plot

图12 基于地质异常的钼铜多金属找矿有利地段分布图

Fig.12 Distribution of preferable molybdenum-copper polymetallic mineral ore-finding areas based on geo-anomalies

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