非线性矿床模型与非常规矿产资源评价

成秋明

非线性矿床模型与非常规矿产资源评价

成秋明^{1, 2,}

1.
中国地质大学地球系统和矿产资源工程实验室, 湖北武汉 430074
2.
York 大学地球大气系, 加拿大多伦多 M3J1P3

基金项目:

加拿大自然科学基金“研究和开发矿产资源和环境评价GIS空间分析和多重分形方法和系统”项目 NSERC-OGP0183993

详细信息

作者简介:
成秋明（1960-）, 男, 教授, 1994年毕业于加拿大渥太华大学, 获博士学位, 现为中国地质大学教育部长江学者特聘教授, 加拿大York大学教授, 主要从事矿产与勘探、数学地质、地理信息系统、矿产资源与环境评价的教学和研究.E-mail: qiuming@yorku.ca

中图分类号: P628
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出版历程
- 收稿日期: 2003-04-21
- 刊出日期: 2003-07-25

Non-Linear Mineralization Model and Information Processing Methods for Prediction of Unconventional Mineral Resources

CHENG Qiu-ming^{1, 2
,}

1.
L aboratory of Earth Systems and Mineral Resources Engineering, China University of Geosciences, Wuhan 430074, China
2.
Department of Earth and Atmospheric Science, York University, Toronto, Canada M3J1P3

摘要

摘要: 探讨了建立非线性矿床模型对难识别的非常规矿产资源评价的可能性.首先评述了非线性理论在成矿动力学和矿产勘查中的应用, 以及非常规矿床与非常规矿产资源评价的研究现状.然后引进了以岩浆结晶分异成矿作用为例的多维分形非线性成矿系统模型.在此基础上揭示了非线性成矿系统必然导致元素富集和聚集的奇异分布及矿床分布的广义自相似性规律.广义自相似性刻画了成矿的外在多样性和内在相似性.介绍了2种最新研究的“奇异分析”和“广义自相似性”异常分解方法.采用文中所建议的非线性矿床模型方法对加拿大北部Gowganda地区的热液型Co, Ni, Ag, As, Pb 5种元素矿产进行了预测和评价.结果表明, 以“广义自相似性”和“奇异分析”为基础的非线性矿床模型及GIS信息综合技术对非常规难识别矿产资源评价是有效的.
- 非线性矿床模型 /
- 广义自相似性理论 /
- 奇异分析方法 /
- 成矿系数 /
- GIS信息综合
Abstract: A non-linear mineralization model was proposed on the basis of a classical igneous differentiation mineralization model which can describe the generation of multi-fractal distribution of element concentration as well as grade-tonnage fractal/multi-fractal model. The work has also led to a model to explain the common properties of mineralization and mineralization-associated geochemical anomaly diversity and generalized self-similarity of the anomalies. Generalized self-similarity is related to the generalized scaling invariance which can characterize the external diversity and internal similarity of natural phenomena including mineralization and occurrence of mineral deposits. The models based on core principal of generalized self-similarity and singularity analysis have been applied to a case study of Co-Ni-Ag-As-Pb mineral resources assessment in the Gowganda area of Abitibi district, northern Ontario, Canada. The results have demonstrated that the non-linear models proposed in the current research are effective for delineating week lake sediment and water geochemical anomalies caused by deeply buried sources or week anomalies superimposed to low background values.
- non-linear mineralization model /
- generalized self-similarity /
- singularity analysis method /
- mineralization index /
- GIS data integration

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图 1 研究区地质简图

较浅的点图案反映的是元古代Lorrain组石英砂岩、粉砂岩、角砾岩及Gowganda组泥质变质岩地层; 较大的点图案反映的是太古代酸性到基性超基性火山变质岩地层; 灰色带状的图案反映的是切割侵入太古代和元古代变质岩的后期Nipissing辉绿岩脉; 三角符号表示Co, Ni, As, Ag, Pb 5种元素矿床和矿点分布

Fig. 1. Simplified geology map

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图 2 湖泊沉积物地球化学取样^[26]

Fig. 2. Fig.2 Location map of 1 172 lake sediment samples^[26]

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图 3 湖泊沉积物Ag地球化学(a) 及奇异指数分布(b)

采用GeoDAS GIS软件多维分形插值方法^[28].网格大小为150 m, 图像大小387×404, 窗口内最少12个样品, 查寻半径为1 500 m; a.正规化后的Ag数值.三角符号表示Co, Ni, As, Ag, Pb 5种元素矿床和矿点分布; b.图中灰度表示α < 2的程度.三角符号表示Co, Ni, As, Ag, Pb 5种元素矿床和矿点分布

Fig. 3. Ag values (a) and singularity (b) in lake sediment samples

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图 4 第三因子得分(a) 及其对应的S-A关系(b)

a.采用GeoDAS GIS软件中网格化数据主成分分析方法^[25].组成该因子的主要元素包括Cu, Pb, Co, Ag等.图中灰度表示因子得分.三角符号表示Co, Ni, As, Ag, Pb 5种元素矿床和矿点分布; b.采用GeoDAS GIS软件中S-A分形方法^[28].横坐标表示ln能谱密度S, 纵坐标表示ln面积A (≥S).阀值ln S=9.6将能谱密度分为2组具有不同广义自相似性的范围.这2个不同的分布范围将形成不同的滤波器: 小于ln S=9.6的范围为异常滤波器, 大于ln S=9.6的范围为背景滤波器.采用逆傅立叶变换并利用这两种滤波器可将第三因子得分图分解, 结果见图 5

Fig. 4. Scores on the third PCA contributed mainly (a) and power-law relationships between power spectrum energy density Sand the area A (≥S) on frequency domain (b)

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图 5 采用S-A方法和图 4b中给出的异常滤波器得到的第三因子组合异常(a) 及背景(b)

图中灰度表示异常相对强度.三角符号表示Co, Ni, As, Ag, Pb 5种元素矿床和矿点分布

Fig. 5. Anomaly (a) and background (b) map calculated using S-A method with the anomaly filter defined in

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图 6 矿床分布潜力后验概率

采用GeoDAS GIS模糊证据权方法, 综合了第三主因子异常、航磁异常、航空放射性异常及重力异常等信息层^[27].图中灰度表示后验概率.三角符号表示Co, Ni, As, Ag, Pb 5种元素已知矿床和矿点分布

Fig. 6. Posterior probability for potential occurrence of mineral deposits of five-element type

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