Robust time-frequency analysis of seismic data using general linear chirplet transform

Abstract

Time-frequency analysis (TFA) has been widely used in seismic processing and interpretation. A good time-frequency representation can preferably characterize geologic spatial distribution and detect hydrocarbon reservoir anomalies. This paper applies a robust seismic TFA method based on the general linear chirplet transform (GLCT). The GLCT method is an extended form of LCT, which is a unifying framework encompassing the short time Fourier transform (STFT) and the continuous wavelet transform (CWT) using the chirplet atom as the kernel function instead of the sinusoidal wave or wavelets. By rotating the chirplet atom at each time-frequency point, GLCT method could adaptively choose the best atom to fit the local time-frequency feature of seismic signals. The algorithm follows such a simple logic and produces a broadband time-frequency spectrum free of cross-term interference, resulting in good performance characterizing the instantaneous spectral variations. Synthetic data analysis demonstrates that the GLCT method is able to reach a higher energy concentration in the time-frequency plane than conventional methods. Robustness analysis indicates that GLCT produces more stable results that outperform not only STFT, CWT, but also high-resolution methods such as the synchrosqueezing transform and complete ensemble empirical mode decomposition in the case of noisy data. The application to field data illustrates that the isofrequency attributes extracted by GLCT through spectral decomposition could effectively image subtle stratigraphic structures of the subsurface paleotopography and highlight the frequency anomalies associated with hydrocarbons. Sometimes, these anomalies might be otherwise inundated in the background noise. Our method can be a validation tool for seismic facies interpretation improvement and direct hydrocarbon indication in practice.