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- W2901146596 abstract "This paper proposes a pre-processing scheme based on variational mode decomposition (VMD) to regularize target lithological log from seismic signals for reservoir characterization (RC) using support vector regression (SVR) algorithm. The regularization scheme has been incorporated by a systematic framework on a real hydrocarbon field dataset from India. The performance of SVR algorithm is quantified in terms of four metrics -correlation coefficient (CC), root mean square error (RMSE), absolute error mean (AEM), and scatter index (SI). The tuned SVR parameters are used to predict synthetic porosity logs over the study area from three-dimensional (3D) seismic volumes. The predicted porosity logs are spatially filtered using a two-dimensional (2D) median filter for better visualization. The designed framework can be used for RC in future to predict and visualize lithological properties from seismic attributes over a hydrocarbon field." @default.
- W2901146596 created "2018-11-29" @default.
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- W2901146596 date "2018-07-01" @default.
- W2901146596 modified "2023-10-18" @default.
- W2901146596 title "A Variational Mode Decomposition Based Novel Preprocessing Method for Reservoir Characterization Using Support Vector Regression" @default.
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- W2901146596 doi "https://doi.org/10.1109/igarss.2018.8517948" @default.
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