A novel local maximum synchrosqueezing W transform for reservoir characterization

Time-frequency analysis (TFA) serves as a critical tool in seismic signal processing and interpretation, particularly for characterizing non-stationary signals. However, conventional TFA methods, such as the short-time Fourier transform (STFT) and continuous wavelet transform (CWT), suffer from inherent limitations, including energy smearing and insufficient time-frequency resolution, which hinder their ability to meet the demands of high-precision seismic interpretation. By detecting local maxima along the frequency direction, LMSST significantly improves energy concentration in time-frequency representations (TFRs). Meanwhile, the W transform is known for high resolution in low-frequency regions and a flexible windowing function, which surpasses conventional TFA methods. However, both methods face limitations when applied independently to complex seismic signals, particularly in scenarios demanding high precision and resolution. To overcome these challenges, the local maximum synchrosqueezing W transform (LMSSWT) is proposed. This approach combines the adaptive windowing of the W transform with the precise frequency reallocation of the LMSST, resulting in a more centralized and energy-concentrated time-frequency representation. Furthermore, an inverse LMSSWT is also derived to ensure completeness and accurate signal reconstruction. By synergizing the W transform’s adaptive windowing with the energy concentration of LMSST, LMSSWT overcomes key limitations in time-frequency analysis of complex seismic signals, offering a powerful tool for high-resolution reservoir prediction and hydrocarbon detection. The effectiveness and applicability of the proposed method are validated through synthetic tests and practical data applications.