Matching Pursuit Based Sparse Time-Frequency Analysis for Seismic Signal

This paper first reviews the principle of short time Fourier transform (STFT) and analyzes the disadvantage of STFT. Then it establishes the relationship between the short time observation and the spectrum. Based on the relationship and the sparse constraint, a sparse time-frequency model is proposed to avoid the disadvantage of STFT, fitting the sparse prior of the local observed signal. After that, matching pursuit method is employed to solve the proposed model. Experiments are then carried out on the seismic signal, comparing with some state-of-the-art time-frequency methods. Results show that the proposed method is able to compete with the state-of-the-art time-frequency methods and is capable of obtaining high-resolution time-frequency distribution, which is of great importance to seismic signal spectrum decomposition.