TIME-VARYING SEISMIC WAVELET ESTIMATION FROM NONSTATIONARY SEISMIC DATA

Abstract

Seismic wavelet estimation is an important part of seismic data processing and interpretation, whose reliability is directly related to the results of deconvolution and inversion. The methods for seismic wavelet estimation can be classified into two basic types: deterministic and statistical. By combining the deterministic spectral coherence method and the statistical skewness attribute method, the amplitude and phase of the time-varying wavelet 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 illustrate the method with both synthetic and real data examples. Synthetic examples certify its feasibility while real data example demonstrates the ability to estimate the time-varying characteristic of wavelets.