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.
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We thank the anonymous referees for their valuable comments and suggestions. The authors thank the Directorate General of Petroleum Concessions (DGPC), Pakistan, being a data source for this work. The authors would also like to thank Hiroshi Tsukahara for making the Matlab code available and for useful scientific discussions.
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The authors declare that they do not have any conflict of interest.
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Appendix: Matlab Code for the Toy Example
Appendix: Matlab Code for the Toy Example
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Hayat, U., Ali, A., Murtaza, G. et al. Classification of Well Log Data Using Vanishing Component Analysis. Pure Appl. Geophys. 177, 2719–2737 (2020). https://doi.org/10.1007/s00024-019-02374-2
- Vanishing component analysis
- feature extraction
- classification of lithologies
- classification algorithms
- geophysical logs