Statistical decision in regional exploration; application of regression and Bayesian classification analysis in the southwest Wisconsin zinc area

Geoffroy, Jérôme; Wignall, T. K.

doi:10.2113/gsecongeo.65.7.769

Cited by 13 publications

(3 citation statements)

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“…But the method is based on a great number of samples, moreover the normality of the variables and a linear relation exist between the input and output variables. Bayesian (Geoffroy and Wignall, 1970) and weights of evidence model (Bonham-Carter et al, 1988) can provide us with more objective prediction based on known mineral occurrences. When exploration areas are unexplored, fuzzy set (An et al, 1991) can be used to predict the deposit and barren cells.…”

Section: Introductionmentioning

confidence: 99%

Rough set for quantitative analysis of characteristic information in metallogenic prediction

et al. 2009

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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.

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Section: Introductionmentioning

confidence: 99%

Rough set for quantitative analysis of characteristic information in metallogenic prediction

et al. 2009

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“…While binary and index overlay methods are simple, they have the disadvantage that the information in the map is limited to two or just a small range of classes and they are not suitable to model complex non-linear relationships. Statistical methods such as the multiple linear regression were the earliest methods used in mineral prospectivity mapping [6,7,18]. But the method is based on a range of assumptions [21], in particular, the normality of the variables and that a linear relationship exists between the input and output variables.…”

Section: Introductionmentioning

confidence: 99%

Comparing performance of interval neutrosophic sets and neural networks with support vector machines for binary classification problems

Kraipeerapun

Fung

2008

2008 2nd IEEE International Conference on Digital Ecosystems and Technologies

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Abstract:In the mining industry, effective use of geographic information systems (GIS) to identify new geographic locations that are favorable for mineral exploration is very important. However, definitive prediction of such location is not an easy task. In this paper, four different neural networks, namely, the Polynomial Neural Network (PNN), General Regression Neural Network (GRNN), Probabilistic Neural Network (PrNN) and Back Propagation Neural Network (BPNN) have been used to classify data corresponding to cells in a map grid into deposit cells and barren cells. These approaches were tested on the GIS mineral exploration data from the Kalgoorlie region of Western Australia. The performance of individual neural networks is compared based on simulation results. The results demonstrate various degrees of success for the networks and suggestions on how to integrate the results are discussed.

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“…While the binary and index overlay methods are simple, they have disadvantages such as limited information in the map and not being suitable to model complex non-linear relationships. Statistical methods such as the multiple linear regression was one of the earliest methods used in the mineral prospectivity mapping [5,6,16]. But the method is based on assumptions that the relationship between the input and output variables is linear.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Ensembles Based Approach for Mineral Prospectivity Prediction

Iyer

Fung

Brown

et al. 2005

TENCON 2005 - 2005 IEEE Region 10 Conference

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Abstract-In mining industry, accurate identification of new geographic locations that are favourable for mineral exploration is very important. However, definitive prediction of such locations is not an easy task. In recent years, the use of neural networks ensemble approach to the classification problem has gained much attention. This paper discusses the results obtained from using different neural network (NN) ensemble techniques for the mineral prospectivtity prediction problem. The proposed model uses the Geographic Information Systems (GIS) data of the location. The method is tested on the GIS data for the Kalgoorlie region of Western Australia. The results obtained are compared to some of the commonly known techniques: the majority combination rule, averaging technique, weighted averaging method tuned by Genetic Algorithm (GA) and a newly proposed rule based method. The results obtained using the different techniques are discussed.

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Statistical decision in regional exploration; application of regression and Bayesian classification analysis in the southwest Wisconsin zinc area

Cited by 13 publications

References 0 publications

Rough set for quantitative analysis of characteristic information in metallogenic prediction

Rough set for quantitative analysis of characteristic information in metallogenic prediction

Comparing performance of interval neutrosophic sets and neural networks with support vector machines for binary classification problems

Neural Network Ensembles Based Approach for Mineral Prospectivity Prediction

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