TIME-VARYING SEISMIC WAVELET ESTIMATION FROM NONSTATIONARY SEISMIC DATA

Seismic wavelet estimation is an important part of seismic data processing and interpretation, whose reliability is directly related to the accuracy of deconvolution and inversion. The methods for seismic wavelet estimation can be classified into two basic types: deterministic and statistical. By combining the two types of the methods, using the spectral coherence method in the deterministic method and the skewness attribute method in the statistical method, the amplitude and phase of the time-varying wavelets are estimated separately. There is no assumption on the wavelet's time-independent nature or the phase characteristic. The advantage of this method is the ability to estimate time-varying phase. Phase-only corrections can then be applied by means of a time-varying phase rotation. Alternatively, amplitude and phase deconvolution can be achieved to enhance the resolution and support the fine reservoir prediction and description. We test this method on both synthetic and real data examples. Synthetic examples prove its feasibility while the real data example demonstrates its ability to estimate the time-varying characteristics of wavelets.