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 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 t...

[1]  Tamás D. Gedeon,et al.  Uncertainty in Mineral Prospectivity Prediction , 2006, ICONIP.

[2]  Yi Lin.  Information, prediction and structural whole: an introduction , 2001 .

[3]  Yi Lin,et al.  PREDICTION OF NATURAL DISASTERS , 2000 .

[4]  Lance Chun Che Fung,et al.  Quantification of Uncertainty in Mineral Prospectivity Prediction Using Neural Network Ensembles and Interval Neutrosophic Sets , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[5]  Kaidi Liu,et al.  Unascertained Rationals and Subjective Uncertain Information , 2002 .

[6]  G. J. Woodsworth,et al.  Multiple regression as a method of estimating exploration potential in an area near terrace, b.c , 1970 .

[7]  Li Shuang.  APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS TO GEOSCIENCES:REVIEW AND PROSPECT , 2003 .

[8]  Vira Chankong,et al.  Rough set-based approach to rule generation and rule induction , 2002, Int. J. Gen. Syst..

[9]  Yong Wu,et al.  The second stir and incompleteness of quantitative analysis , 2000 .

[10]  Wooil M. Moon,et al.  Integration of Geophysical, Geological and Remote Sensing Data Using Fuzzy Set Theory , 1991 .

[11]  Tom Gedeon,et al.  Artificial neural networks: A new method for mineral prospectivity mapping , 2000 .

[12]  Chen Yong-qing.  Developing Current Situation of Metal Resources Exploration Techniques and Some Thought , 2002 .

[13]  J. G. De Geoffroy,et al.  A probabilistic appraisal of mineral resources in a portion of the Grenville Province of the Canadian shield , 1971 .

[14]  J. G. De Geoffroy,et al.  Statistical decision in regional exploration; application of regression and Bayesian classification analysis in the southwest Wisconsin zinc area , 1970 .

[15]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[16]  Yi Lin.  Introduction: discontinuity – a weakness of calculus and beginning of a new era , 1998 .

[17]  Shoucheng OuYang,et al.  Evolution science and infrastructural analysis of the second stir , 2001 .

[18]  LU Yan-sheng.  Rule Induction Algorithm Based on Rough Sets for Incomplete Information System , 2006 .