Ideas and Methods for Mineral Resources Integrated Prediction in Covered Areas
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摘要: 围绕覆盖区矿产综合预测的基本思路和关键方法问题，讨论了非线性矿产预测理论和方法如何在覆盖区矿产综合预测中发挥作用，重点介绍了成矿奇异性理论与非线性矿产预测方法在覆盖区矿产预测“弱信息”提取、“复合叠加信息”分解、“缺失和不完整信息”融合等关键问题中的应用.结合正在实施的中国地质调查局“覆盖区矿产综合预测”计划项目，介绍了三方面的研究进展：(1)覆盖层中地球化学元素迁移机理与覆盖层对地表地球化学异常的屏蔽和衰减作用；(2)如何识别由于覆盖层影响而造成的“弱”且“复杂”的地球化学异常；(3)如何综合具有缺失或不完整的多元勘查信息以达到提高覆盖区矿产综合预测精度、降低预测不确定性的目的.以东天山戈壁沙漠覆盖区海相火山岩型铁矿、大兴安岭南部草原覆盖区铁多金属矿、武夷山层控矽卡岩型铁矿等矿床预测为例，介绍了综合预测方法的应用过程和应用效果.研究结果表明，奇异性理论和分析方法可以有效地用于提取水系沉积物地球化学和地球物理(重、磁)弱异常，合理分解复合叠加异常，在此基础上，采用地球化学和地球物理异常的数据融合技术，分别建立了推断铁镁质火山岩、中酸性侵入岩、矽卡岩和热液蚀变等成矿或控矿地质要素的综合信息模型，以及基于综合预测要素建立的覆盖区矿产预测后验概率证据权模型和模糊逻辑模型.应用结果显示，介绍的预测方法不仅可以在出露区圈定成矿远景区，而且在戈壁沙漠覆盖区及第三、第四系松散沉积物覆盖区等均可圈出具有重要资源潜力的远景区，这些远景区往往会被传统的预测方法所遗漏.Abstract: In this paper, it is shown that the element concentration in the stream sediments in the covered areas can be very low due to decay and mask effects even if a thin layer of overburden exists. The examples introduced in the paper for prediction of mineral deposits of skarn types have demonstrated that the nonlinear singularity and generalized self-similarity theories and methods can be used to map anomalies for locating undiscovered mineral deposits in areas covered by transported regolith. Three main aspects of difficulties facing mineral exploration and mineral deposit prediction in covered areas are discussed in the paper which include weak anomalies detection and recognition, decomposition of complex and mixing anomalies due to multiple geo-processes, and application of evidential layers with missing or incomplete information due to covers. Various models have been proposed for prediction of various objects including felsic intrusions, skarn and hydrothermal alterations and local geochemical anomalies. Several datasets, including 1∶200 000 scale geological maps, stream sediment geochemical data, aeromagnetic and gravity data were applied for delineation of potential target areas for Fe mineral deposits of volcanic skarn and hydrothermal types in the areas covered by desert and Ternary to Quaternary sediments.
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图 1 钻孔岩心上段覆盖层中Zn元素含量密度分布趋势
样品间隔约20 cm，数据采集采用便携式XRF仪(Niton XL3t950)；横坐标为离开下伏蚀变岩石风化面的距离，纵坐标为元素含量点图表示测量数据；线条为最小二乘拟合；a.双对数坐标；b.原始坐标
Fig. 1. Plots showing the relations between concentration of Zn and depth from the surface of underlying weathered rocks
图 3 覆盖区矿产综合预测理论和方法体系流程
Fig. 3. Flowchart showing the processes of integrated prediction of mineral deposits in covered areas
图 4 矿床成因模型
a.哈密市雅满苏式铁矿床成因模式图(刘德权等，1996年)；b.大兴安岭地区朝不楞矽卡岩型铁多金属矿床成矿模式示意图(从勘探剖面改编)；c.福建武夷山马坑层控矽卡岩型铁矿的典型矿床成因模式图(赵一鸣等, 1983).图 4a中，1.流纹质晶屑凝灰岩；2.安山质沉凝灰岩；3.钾细碧玢岩；4.石榴石矽卡岩(含凝灰岩残留体)；5.流纹质凝灰岩(钠长石化)；6.灰岩；7.破火山口充填的次玄武玢岩；8.流纹质玻屑凝灰岩；9.铁矿体；10.火山喷发不整合面(线).图 4b中，1.第四系沙土；2.第三系红土；3.角岩；4.大理岩；5.矽卡岩；6.铁矿体；7.花岗岩.图 4c中，1.粉砂岩和细砂岩；2.泥质板岩；3.结晶灰岩和大理岩；4.编制石英砂(砾)岩、石英岩；5.变凝灰岩或灰质粉砂岩；6.片理化变质粉砂岩；7.磁铁矿体；8.矽卡岩；9.钾化；10.花岗质混合岩化；11.辉绿闪长岩；12.花岗岩类
Fig. 4. Mineral deposits models
图 5 数据处理与中酸性岩体预测模型和方法流程
a.地球物理数据处理模型和流程；b.地球化学数据处理模型和流程；c.中酸性岩体预测推断地球物理和地球化学综合模型
Fig. 5. Flowchart showing the processes of geophysical and geochemical data preprocessing and integrated prediction model for locating felsic igneous rocks associated with skarn mineral deposits
图 6 预测和推断酸性岩浆岩分布
a，c, d为地球物理和地球化学融合模型；b, d, e表示主成分得分.图中的格子图案表示地质图上所显示的已知中酸性岩体，这些已知的岩体出露范围可以作为对岩体预测模型结果的检验
Fig. 6. Prediction of felsic igneous rocks using integrated geophysical and geochemical models obtained by conjunction of weak anomaly extraction techniques and principal component analysis
图 7 矿化蚀变带的综合预测模型与方法流程
Fig. 7. Flowchart showing the processes of integrated prediction of mineralization associated alteration zones
图 8 组合元素地球化学异常的确定
a.主成分载荷图；b.分形滤波S-A图式；c.主成分得分图；d.S-A方法分解得到的地球化学组合异常图.空心圆表示矿床分布
Fig. 8. Geochemical anomalies of combination of trace elements
图 9 矿床预测综合模型和远景区圈定方法
a.信息综合模型与后验概率计算模型；b.远景区圈定与资源定量预测模型
Fig. 9. Flowchart showing the processes of integrated model for prediction of mineral deposits
图 10 东天山某区域铁矿预测后验概率结果
a.后验概率图；b.预测矿床数概率模型
Fig. 10. Results of posterior probability for iron mineral deposits in Dongtianshan district
图 11 朝不愣矽卡岩型铁矿预测模型
Fig. 11. Flowchart showing the processes of integrated prediction of skarn iron mineral deposits
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