Unsupervised learning-assisted decomposition for Wigner–Ville distribution and its application in reservoir characterization

ABSTRACT Wigner–Ville distribution (WVD) is a commonly used seismic time-frequency (TF) analysis tool. Nevertheless, it is prone to cross-term interference, distorting TF interpretation. Inverse problem (IP)-based approaches have been used to suppress these cross-terms by decomposing seismic signals into a linear combination of predefined dictionary elements. Although effective in reducing interference, these methods are computationally expensive, requiring iterative optimization processes that can be impractical for large-scale seismic data. To address this challenge, we proposed an unsupervised learning-assisted decomposition for WVD network (ULDWVDNet) that leverages deep learning (DL) to enhance the efficiency of IP-based WVD. The proposed model comprises an IP-based network that integrates DL for signal decomposition and reconstruction. The DL-based decomposition model decomposes the analyzed signal into a linear combination and improves its calculation effectiveness, whereas the reconstructed model maintains the accuracy of the decomposed results. Afterward, we applied the WVD to each decomposed signal to achieve a high-resolution TF result. The Marmousi II synthetic data are first used to train the proposed model, and the TF results demonstrate the effectiveness of our proposed model. Moreover, we applied the proposed model to 3D real seismic data for hydrocarbon reservoir detection. The field data results reveal that the ULDWVDNet can produce more accurate and reliable interpretation results.