Journal article
Lithofacies classification from well log data using neural networks, interval neutrosophic sets and quantification of uncertainty
World Academy of Science, Engineering and Technology. Proceedings, Vol.23(11), pp.162-166
2006
Abstract
This paper proposes a novel approach to the question of lithofacies classification based on an assessment of the uncertainty in the classification results. The proposed approach has multiple neural networks (NN), and interval neutrosophic sets (INS) are used to classify the input well log data into outputs of multiple classes of lithofacies. A pair of n-class neural networks are used to predict n-degree of truth memberships and n-degree of false memberships. Indeterminacy memberships or uncertainties in the predictions are estimated using a multidimensional interpolation method. These three memberships form the INS used to support the confidence in results of multiclass classification. Based on the experimental data, our approach improves the classification performance as compared to an existing technique applied only to the truth membership. In addition, our approach has the capability to provide a measure of uncertainty in the problem of multiclass classification.
Details
- Title
- Lithofacies classification from well log data using neural networks, interval neutrosophic sets and quantification of uncertainty
- Authors/Creators
- P. Kraipeerapun (Author/Creator)C.C. Fung (Author/Creator)K.W. Wong (Author/Creator)
- Publication Details
- World Academy of Science, Engineering and Technology. Proceedings, Vol.23(11), pp.162-166
- Publisher
- World Academy of Science, Engineering and Technology
- Identifiers
- 991005540343107891
- Murdoch Affiliation
- School of Information Technology; Centre for Water, Energy and Waste
- Language
- English
- Resource Type
- Journal article
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