基于稳健有序独立成分分析(ROICA)的矿产预测

J4 ›› 2012, Vol. 42 ›› Issue (3): 872-880.

基于稳健有序独立成分分析(ROICA)的矿产预测

余先川^1,2|刘立文³|胡丹¹|王仲妮¹

1.北京师范大学信息科学与技术学院|北京100875;2.中国地质大学地质过程与矿产资源国家重点实验室|武汉430074;
3.广东省地勘局719地质大队|广东肇庆526020

收稿日期:2011-08-19 出版日期:2012-05-26 发布日期:2012-05-26
作者简介:余先川(1967-)|男|教授|博士生导师|主要从事矿产资源预测、空间数据挖掘、影像处理与识别研究|E-mail:yuxianchuan@163.com
基金资助:
国家自然科学基金项目 ( 41072245，11001019，40372129)；地质过程与矿产资源国家重点实验室开放基金项目(GPMR200601)；国家“863”计划项目（2007AA12Z156）；教育部新世纪优秀人才支持计划项目（NCET-06-0131）

Robust Ordinal Independent Component Analysis(ROICA) Applied to Mineral Resources Prediction

Yu Xian-chuan^1,2, Liu Li-wen³, Hu Dan¹,Wang Zhong-ni¹

1.College of Information Science and Technology, Beijing Normal University, Beijing100875, China;
2.State Key Laboratory of Geological Processes and Minerals Resources, China University of Geosciences, Wuhan430074,China;
3.The 719th Geological Team of Guangdong Geological Exploration Bureau|Zhaoqing526020,Guangdong,China

Received:2011-08-19 Online:2012-05-26 Published:2012-05-26

摘要/Abstract

摘要：

独立成分分析（ICA）充分考虑了以往主成份分析（PCA）没考虑到但又非常重要的数据高阶统计特性，但ICA分离后的信号具有顺序、符号、幅度的不确定性，而矿产资源预测又必须弄清分离后信号（地质变量）的地质意义;为此，提出了一种基于稳健有序独立成分分析(ROICA)的矿产预测方法。首先对地质数据进行稳健预处理，然后进行独立成分分析，借鉴因子分析的思想解决独立成分分析算法不确定性的局限，从而可以反映分离变量的有序性。将ROICA方法用于广东省肇庆双壬铜矿区进行矿产资源预测。实验结果表明：该区金银矿体的赋存空间位置与传统均值方差方法给出的异常位置明显不对应，没有指示意义；而经过稳健有序ICA处理后的Au、Ag独立成分异常与实际的Au、Ag矿体吻合度很高；Cu、Pb、Zn独立成分异常与Au、Ag矿关联性不大，反映研究区的Cu、Pb、Zn等其他元素或组合不具有Au、Ag矿的指示意义。ROICA方法可用于矿产资源预测。

关键词: 矿产资源预测, 独立成分分析, 因子分析, 稳健处理

Abstract:

Independent component analysis (ICA) has the advantage of handling the higher-order statistics which are ignored by principle component analysis (PCA). However, decomposed signals by ICA have the character of indeterminacy for the sequences, signs and scales which are of great importance in mineral (i.e. geochemistry) data. Therefore, a new mineral resources prediction approach based on robust ordinal independent component analysis(ROICA) is proposed. Firstly, the mineral data are processed by robust algorithm. Then, a ICA method is applied to the data and factor analysis is introduced to eliminate the indeterminacy among the decomposed independent components. The proposed new algorithm is applied to mineral resources prediction in Shuangren ore district, Guangdong province. Compared with the traditional meanvariance method, the chemical elements (Au and Ag) decomposed by the ROICA accord well with the practical distribution of gold and silver ore bodies while combination of chemical elements (e.g. Cu, Pb and Zn) has no indication significance as they have no association with gold and silver ore bodies. This method can be used for prediction of mineral resources.

Key words: mineral resources prediction, independent component analysis, factor analysis, robust preprocessing

中图分类号:

P628

余先川, 刘立文, 胡丹, 王仲妮. 基于稳健有序独立成分分析(ROICA)的矿产预测[J]. J4, 2012, 42(3): 872-880.

YU Xian-chuan, LIU Li-wen, HU Dan, WANG Zhong-ni. Robust Ordinal Independent Component Analysis(ROICA) Applied to Mineral Resources Prediction[J]. J4, 2012, 42(3): 872-880.

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