Rough set for quantitative analysis of characteristic information in metallogenic prediction

Purpose - The purpose of this paper is to extract the characterized mineralization information from large numbers of data obtained from geologic exploration based on rough set; analyze the inherent relation between mineral information genes and metallogenic probability, and offer the scientific basi...

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Bibliographic Details
Published in:Kybernetes 2009-10, Vol.38 (10), p.1801-1811
Main Authors: Yuan, Yan-bin, Zhu, Ya-qiong, Zhou, You, Sælthun, N.R., Cui, Wei, Huang, Jiejun
Format: Article
Language:English
Subjects:
Approximation
Cybernetics
Data collection
Fuzzy sets
Geology
Information systems
Mineralization
Minerals
Set theory
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Summary:Purpose - The purpose of this paper is to extract the characterized mineralization information from large numbers of data obtained from geologic exploration based on rough set; analyze the inherent relation between mineral information genes and metallogenic probability, and offer the scientific basis for target prediction.Design methodology approach - Mineral information includes all kinds of relative metallogenic information. In order to extract comprehensive metallogenic prediction information, it is necessary to filter initial observation information to emphasize the factors that are most advantageous to metallogenic prognosis. Rough set can delete irrespective or unimportant attributes on the premises of no information missing and no classification ability changing, without supplementary information or prior knowledge, which has important theoretic and practical value for metallogenic prognosis.Findings - The association and importance of geological information referring to prospecting are found out through attribute reduction based on rough set.Originality value - The analysis of geological and mineral information based on rough set is a novel approach for high-dimensional complex non-deterministic polynomial problems which are predominant in geological research. The research successfully extracts characterized mineralization information to offer the scientific basis for target prediction.
ISSN:0368-492X
1758-7883
DOI:10.1108/03684920910994321