Time-Frequency Distribution And Investigation In Spectral Analysis Of Seismic Signal

Spectral analysis is the basis of seismic data processing. Because seismic signal is a complex non-stationary series, a suitable time-frequency distribution is the key for ensuring the quality of seismic data time-frequency analysis.For aiming at the application of the seismic data processing, several basic time-frequency representations are presented entirely. The original derivation, basic principle and the computational implementation of S transform(ST) are introduced in detail.In order to enhance the analytic power of ST on different non-stationary signal processing, a generalized S transform (GST) is achieved by adding two adjustable parameters to reconstruct the Gaussian window function of normal ST. The parameters change the way in which the Gaussian window function of ST varies inversely with the frequency. GST possesses adjustable time-frequency resolution. It has higher practicability and adaptability than ST in actual application. Two time-frequency filters based on its excellent time-frequency distribution and analogical multi-resolution are presented in time-frequency domain. The filters are used to extract specific signal components or filtering noise on the time-frequency plane, which can enhance signal and suppress noise.The simulation demonstrates that GST is able to exactly describe the time-frequency localization of seismic signal. Utilizing GST as a preferable tool for time-frequency spectral analysis of seismic signal, the paper respectively explores several applications in real seismic data processing including the instantaneous spectral decomposition, detecting the low frequency shadow related to the gas concentration, and the stratigraphic analysis with high resolution of thin bed. A number of examples illustrate that GST potentially can be used to determine the location and property of the interested stratum, directly indicate hydrocarbon, enhance resolution of seismic data, subtly describe the geological structure, improve visualization of stratigraphic feature and noise suppression. They offer reliable spectral method for interpretation.