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The Connolly (1999) elastic impedance (EI) equation is a function of P-wave velocity, S-wave velocity, density, and incidence angle. Conventional inversion methods based on this equation can only extract P-velocity, S-velocity, and density data directly and the elastic impedance at different incidence angles are not at the same scale, which makes comparison difficult. We propose a new elastic impedance equation based on the Gray et al. (1999) Zoeppritz approximation using Lame parameters to address the conventional inversion method’s deficiencies. This equation has been normalized to unify the elastic impedance dimensions at different angles and used for inversion. Lame parameters can be extracted directly from the elastic impedance data obtained from inversion using the linear relation between Lame parameters and elastic impedance. The application example shows that the elastic parameters extracted using this new method are more stable and correct and can recover the reservoir information very well. The new method is an improvement on the conventional method based on Connolly’s equation.

Although the accuracy requirement of matching pursuit algorithm has been met, it is not widely used due to the heavy computational cost and low operational efficiency. By solving the complex amplitude to obtain both the amplitude and phase parameters simultaneously, the number of parameters for establishing time-frequency atomic library is reduced to two under the condition that the phase accuracy is preserved. So the dual-parameter dynamic fast matching pursuit algorithm improves the computational efficiency to some degree. However, scanning the frequency during decomposition will also consume a lot of time. In order to overcome the above shortcomings, we propose a fast exponential matching pursuit algorithm based on the Ricker wavelet. Among decomposition, the best fitting adjustment parameters are obtained by firstly fitting the exponential distribution function and the pending signal at the maximum amplitude of the envelope. Then the dominant frequency of the wavelet is calculated according to the similarity between the Ricker wavelet and the exponential distribution. Finally, the signal is decomposed into a combination of time-frequency atoms. Since this method does not need to scan the dominant frequency, the computational efficiency has been highly improved. The numerical and field data tests show that the decomposition speed is greatly improved using the fast exponential matching pursuit algorithm based on the Ricker wavelet under the condition of accuracy preservation. So this method is feasible, noise resistent and highly efficient.

Wavelets are a relatively recent development in applied mathematics. With the local time-frequency of wavelet transforms, we can deal much more with the signal reflected by thin-mutual strata. Therefore, we have put forward a method to distinguish the seismic sequence using the wavelet analysis theory. Through the seismic sequence analysis using wavelet transforms, we can obtain vertical and horizontal phase profiles from real seismic data. We will distinguish deposition cycle and material components in the vertical phase profile, and compare and track deposition sequence and structure interface horizontally in the horizontal phase profile.

On the basis of frequency time (F-T) analysis for a single seismic trace, this paper studies the 3D F-T analysis of a seismic profile using the wavelet transform. Once the seismic profile is transformed into a 3D X-T-F data cube, seismic sequence analysis can be done on the 3D cube. Distinguishing the seismic sequence cycle and determining the sequence interface location can be realized in the F-T slice. Tracing continuity characteristics of the sequence interface and picking geological body boundaries and tectonic faults can be implemented in the X-T slice. It becomes the basis for 4D F-T analysis of the 3D seismic data cube and for quantitative interpretation on 4D X-Y-T-F cubes.