奇异性理论在个旧锡铜矿产资源预测中的应用: 综合信息集成与靶区圈定

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

奇异性理论在个旧锡铜矿产资源预测中的应用: 综合信息集成与靶区圈定

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

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: Information Integration and Delineation of Mineral Exploration Targets

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

摘要

摘要:
在致矿弱异常提取和复合异常分解的基础上, 进行多元信息综合和集成, 绘制成矿后验概率图是矿床资源预测的基本过程.以个旧锡矿为例, 介绍一种新的信息集成模型和后验概率图的应用方法.结果表明, 个旧锡铜矿床分布受多个控矿要素控制, 包括地球化学异常、岩体、有利岩性以及构造条件等.通过证据权所提供的空间相关性统计量可以定量确定控矿要素的最佳控矿距离, 并以此为依据形成二态信息图层.对每个图层的叠加可看作一次找矿信息的累积和更新, 因此整个信息图层的集成过程可以看作多次信息叠加过程(multiplicative cascade process).由此绘制的后验概率图具有自相似性、奇异性和分形谱系, 空间分布服从多重分形统计分布.因此, 后验概率图的绘制可以作为致矿地质异常圈定的信息综合和集成方法.
- 矿床预测 /
- 矿产资源定量评价 /
- GIS /
- 非线性理论 /
- 多重分形模型
Abstract:
This paper is based on several research projects conducted in Gejiu Sn and Cu mineral districts on regional mineral resource assessment, 3D mineral resource assessment and prediction of deeply buried mineral deposits.New non-linear theory and methods of multifractal singularity have been applied in these projects to map weak anomalies related to deeply buried ores and mineralization halos.The weights of evidence model with corrected posterior probability using a newly developed correction method is applied to integrate various geo-evidential layers to map mineral potentials in the Gejiu area.The binary evidential layers include two layers defined on the basis of geochemical data, with one on distance from intersection of faults and the other on Gejiu Formation.Each addition of a binary layer can be considered as a partition of the study area and redistribution of posterior probability which can be treated as a general multiplicative cascade process and the final product may show multifractal properties with multiple singularities.A power-law model was established between posterior probability and undiscovered mineral deposits for the area.
- mineral deposit prediction /
- mineral resource quantitative assessment /
- GIS /
- non-linear theory /
- mulifractal model

HTML全文

图 1 采用证据权方法确定的控矿要素二态图层

Fig. 1. Binary patterns obtained by using weights of evidence method

下载: 全尺寸图片幻灯片

图 2 采用证据权方法计算矿床空间分布与构造交汇点距离的相关关系

图中给出了小于某一阀值的区域与矿床的分布关系; t为学生统计量, 大小反映相关性的显著性.最佳距离确定为6km

Fig. 2. Plot showing the relationship between the distance from the intersections of faults and t-value calculated using weights of evidence method

下载: 全尺寸图片幻灯片

图 3 布格重力异常处理结果

a.重力上延3km并同时做垂向二阶导数处理; b.重力上延7km并同时做垂向二阶导数处理.图中暗色多边形表示岩体的出露范围

Fig. 3. Results of processing and interpreting gravity anomaly

下载: 全尺寸图片幻灯片

图 4 锡铜矿床预测后验概率图

采用证据权校正模型和4个证据图层(地球化学异常Ⅰ和Ⅱ、个旧组地层、构造交汇点距离) 为输入; 模型范围1688个面积单元, 11个矿床单元(由圆圈符号表示); 先验概率为0.65%

Fig. 4. Posterior probability map calculated from integrating four evidential layers in Figs. 1a to 1d

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图 5 证据权校正模型

a.实际观察后验概率与计算后验概率的关系图; b.校正前后模型.累积面积与后验概率满足分形分布(power-law); 三角形表示观察的后验概率

Fig. 5. Corrected weights of evidence model

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图 6 研究区矿床数预测模型

预测矿床数是预测后验概率的函数.采用图中模型可以计算在不同的概率意义下的预测矿床数, 如图中数据显示的在后验概率分别为1/10000、1/1000、1/100、1/10意义下的预测矿床数分别为30、17、10、6

Fig. 6. Prediction model for undiscovered mineral deposits

下载: 全尺寸图片幻灯片

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