Classification of Well Log Data Using Vanishing Component Analysis

This study reports the application of the novel supervised learning approach called vanishing component analysis (VCA) for the classification of lithologies from well log signal data. Geophysical well log data is always non-linear due to anisotropy and heterogeneity of the earth. The main purpose of...

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Bibliographic Details
Published in:Pure and applied geophysics 2020-06, Vol.177 (6), p.2719-2737
Main Authors: Hayat, Umar, Ali, Aamir, Murtaza, Ghulam, Ullah, Matee, Ullah, Ikram, Nolla de Celis, Álvaro, Rajpoot, Nasir
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Anisotropy
Classification
Data analysis
Discriminant analysis
Earth and Environmental Science
Earth Sciences
Geophysical data
Geophysics
Geophysics/Geodesy
Heterogeneity
Methods
Neural networks
Stability
Support vector machines
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Summary:This study reports the application of the novel supervised learning approach called vanishing component analysis (VCA) for the classification of lithologies from well log signal data. Geophysical well log data is always non-linear due to anisotropy and heterogeneity of the earth. The main purpose of this study is to test the applicability of the VCA algorithm on non-linear geophysical data of Siraj South-01, Middle Indus Basin, Pakistan for classification of lithologies/facies. We demonstrate the performance and stability of the novel approach on a case study before applying it on well log data. Our analysis demonstrates that VCA algorithm is able to linearly separate such a complex non-linear well log data and clearly distinguish between different classes of well log data coming from different rock units. Furthermore, we show that the average accuracies of the classification methods of linear support vector machines, eXtreme gradient boosting, random forest, neural network and linear discriminant analysis on the VCA feature space are much better than the average accuracy obtained by the same methods on the original data.
ISSN:0033-4553
1420-9136
DOI:10.1007/s00024-019-02374-2