Catalog Home Page

Lithofacies classification from well log data using neural networks, interval neutrosophic sets and quantification of uncertainty

Kraipeerapun, P., Fung, C.C. and Wong, K.W. (2006) Lithofacies classification from well log data using neural networks, interval neutrosophic sets and quantification of uncertainty. World Academy of Science, Engineering and Technology. Proceedings, 23 (Novemb). pp. 162-166.

[img]
Preview
PDF - Published Version
Download (311kB)
Link to Published Version: http://www.waset.org/journals/waset/v23/v23-31.pdf
*Subscription may be required

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.

Publication Type: Journal Article
Murdoch Affiliation: School of Information Technology
Publisher: World Academy of Science, Engineering and Technology
URI: http://researchrepository.murdoch.edu.au/id/eprint/812
Item Control Page Item Control Page

Downloads

Downloads per month over past year