Successive variational mode decomposition-based enhanced Wigner-Ville distribution for seismo-volcanic events identification

Abstract

Seismicity offers valuable information on the internal activity of volcanoes and the interpretation of seismic signals is crucial for volcano monitoring. As the seismic signal is multi-component & non-stationary, the time-frequency analysis like Wigner-Ville distribution (WVD) has played a significant role, but the presence of cross-term limits the applicability of WVD and other bilinear time-frequency representations (TFRs). This paper introduces a successive variational mode decomposition (SVMD)-based cross-term reduction method in WVD. The proposed method is robust and accurate due to reduced sensitivity to initialization parameters, lesser computational burden, and automatic selection of the number of decomposed modes. SVMD-based WVD outperforms other related signal decomposition-based methods for removing cross-terms and reconstructing auto-terms, as demonstrated by similarity and concentration-based performance measures for synthetic multi-component and seismic signals. SVMD-based WVD is also applied for the seismo-volcanic events identification system development and evaluated on the Llaima volcano dataset to find the applicability of the proposed method in real-world field data. Novel TFR-based features are extracted based on 3-D segmentation of TFR and 2-D normalized correlation between the segments. Feature ranking and SVM classifier optimization result in an overall classification accuracy of 97.10%, surpassing previous approaches on the same dataset.