奇异性理论在个旧锡铜矿产资源预测中的应用: 成矿弱信息提取和复合信息分解

成秋明; 赵鹏大; 陈建国; 夏庆霖; 陈志军; 张生元; 徐德义; 谢淑云; 王文磊

奇异性理论在个旧锡铜矿产资源预测中的应用: 成矿弱信息提取和复合信息分解

成秋明^{1, 2,},
赵鹏大¹,
陈建国¹,
夏庆霖¹,
陈志军¹,
张生元³,
徐德义^{1, 4},
谢淑云^{1, 5},
王文磊²

1.
中国地质大学地质过程与矿产资源国家重点实验室, 湖北武汉 430074, 北京 100083
2.
加拿大约克大学地球空间科学与工程系, 加拿大多伦多 M3J1P3
3.
石家庄经济学院资源与环境工程研究所, 河北石家庄 050031
4.
中国地质大学经济管理学院, 湖北武汉 430074
5.
中国地质大学地球科学学院, 湖北武汉 430074

基金项目:

国家杰出青年科学研究基金项目 40525009

国家自然科学基金重点项目 40638041

国家863项目 2006AA06Z115

教育部创新团队基金 IRT0755

地质调查项目 121201063390110

详细信息

作者简介:
成秋明(1960-), 男, 教授, 博士生导师, 教育部长江学者特聘教授, 国家杰出青年基金获得者, 国际数学地球科学协会最高奖———克伦宾奖章获得者, 主要从事矿产普查与勘探、数学地质、地理信息系统以及矿产资源评价的教学和研究.E-mail: qiuming@cug.edu.cn

中图分类号: P628
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出版历程
- 收稿日期: 2008-12-16
- 刊出日期: 2009-03-25

Application of Singularity Theory in Prediction of Tin and Copper Mineral Deposits in Gejiu District, Yunnan, China: Weak Information Extraction and Mixing Information Decomposition

1.
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, Beijing 100083, China
2.
Department of Earth and Space Science and Engineering, York University, Toronto M3J1P3, Canada
3.
Institute of Natural Resource and Environment Engineering, Shijiazhuang University of Economics, Shijiazhuang 050031, China
4.
School of Economics and Management, China University of Geosciences, Wuhan 430074, China
5.
Faculty of Earth Sciences, China Univer sity of Geosciences, Wuhan 430074, China

摘要

摘要:
本研究的目的是应用非线性理论和高新信息处理技术获取矿产资源预测综合信息, 开展以有色金属和贵金属矿产资源潜力评价和预测靶区圈定, 提交个旧及周边地区矿产资源潜力分布图.围绕该研究任务, 重点开展了如何应用奇异性理论和方法, 对比个旧东西矿区的异同.由于区域构造和岩体分布等空间变化性, 导致东西区成矿背景存在较大差异, 受出露地表或近地表矿体分布和矿山开采的影响, 东西区的成矿异常强度和大小都存在较大差异, 东区总体呈高背景而西区为低背景, 因而, 对东西区的成矿信息对比研究和异常圈定相对困难.采用局部奇异性分析方法从地球化学分形密度的角度圈定了局部异常, 在东西区均较好地反映了致矿地球化学异常的分布, 同时采用广义自相似分析方法分解了综合地球化学异常和背景.结果表明, 东西区地球化学背景差异悬殊, 而局部异常具有显著的自相似性.据此在东西区同时圈定的局部异常具有内在的相似性和表现形式上的多样性, 以此为依据所圈定的靶区均具有找矿意义.
- 非线性理论 /
- 空间信息技术 /
- 矿产资源预测 /
- 分形与多重分形模型
Abstract:
New non-linear theory and methods of multifractal singularity are applied to map weak anomalies related to deeply buried ores and mineralization halos.The main contents of singularity theory and methodology include three main principles and models: local singularity analysis (for example Δ-analysis) for enhancing weak anomalies caused by buried ores, generalized self-similarity analysis (for example C-A and S-A models) for anomaly decomposition and for anomaly and background separation, and fractal spectrum analysis (for example mapping posterior probability) for characterizing spatial distribution of singularities and anomalies.This paper uses the local singularity analysis to map the anomalies not only in the eastern area where known mineral deposits are found but also in the western area where few mineral deposits have been found.It also uses the generalized self-similarity analysis method S-A model in integration of the principle component analysis in separating element association anomalies in both eastern and western areas.The results show that the eastern and western areas depict significant background difference and the local anomalies obtained using both local singularity model and S-A model show generalized self-similarity in eastern and western areas.Several target areas are delineated on the basis of these local anomalies.
- non-linear theory /
- spatial information technology /
- mineral resource prediction /
- fractal and multifractal model

HTML全文

图 1 云南个旧锡多金属矿集区简化地质图

数据来自中国地质调查局, 三角符号表示锡矿床, 黑线条表示断层

Fig. 1. Simplified geology of Gejiu district

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图 2 (a) 基于窗口方法估计As局部奇异性指数分布图; (b) As地球化学原始数据图

栅格像原空间分辨率为2 km.三角符号表示锡矿床, 黑线条表示断层

Fig. 2. (a) Distribution of singularity obtained for As, (b) map of raw data of As

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图 3 (a) As的奇异值(α) 与频数直方图; (b) α阈值与t值的关系

划分不同α阈值, 应用证据权法获取t值, t值大小反映了小于α空间区域与锡矿床的空间相关性(Cheng and Agterberg, 2008)

Fig. 3. (a) Histogram of singularity index of As, (b) relationship between t-value and singularity index value

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图 4 (a) 采用局部奇异性方法圈定的As异常区; (b) 采用传统的统计方法圈定的异常区

图中黄色圆点表示锡矿分布位置, 黑线条表示断层, 格子模式的多边形表示岩体, 黑色区域为确定的异常区

Fig. 4. (a) Anomalies delineated using singularity method, (b) anomalies delineated using traditional statistical method

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图 5 对Sn、As、Cu、Pb、Zn和Cd元素奇异值(α值) 进行主成分分析结果

第一主成分中的得分图-组合异常分布图.圆点符号表示锡矿床, 黑线条表示断层

Fig. 5. Principal component analysis of singularity of Sn, As, Cu, Pb, Zn and Cd

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图 6 对元素Sn、As、Cu、Pb、Zn和Cd的对数变换值进行主成分分析

a.特征值分布, b.第一主成分中的载荷, 即各元素对第一主成分的贡献

Fig. 6. Principal component analysis of log-transformed values of Sn, As, Cu, Pb, Zn and Cd

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图 7 (a) Sn、As、Cu、Pb、Zn和Cd元素对数变换后获得的第一主成分载荷图; (b) 第一主成分中载荷的S-A曲线图

分界点对应能谱S=1 919.图b中直线表示异常呈分形分布, 右边为背景分布.分界点由计算机自动生成

Fig. 7. (a) Score map on the first principal component, (b) S-A plot

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图 8 (a) 分解第一主成分载荷背景场; (b) 分解第一主成分载荷异常场

黑线表示断裂构造

Fig. 8. (a) Background values of scores on the first principal component, (b) anomalies of scores on the first principal component

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图 9 对图 8b中异常增加常数5后求局部奇异性指数

窗口半径为23 km; 黑线表示断裂构造

Fig. 9. Singularity obtained from anomalies of fig.8b plus 5

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