A blind deconvolution method based on the time-varying wavelet
JIANG Yumeng1,2, CAO Siyuan1,2, CHEN Siyuan1,2, CAI Mingjun3, ZHANG Jialiang3
1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China;
2. Institute of Geophysics, China University of Petroleum, Beijing 102249, China;
3. Dagang Oilfield Company, PetroChina, Tianjin 300280, China
Abstract：Due to the nonstationary and blind seismic data, the actual seismic wavelet is usually unknown and its waveform will change with the propagation time. Therefore, it is difficult to guarantee the accuracy of the inversion results of conventional deconvolution methods based on the stationary wavelet assumption. To this end, nonstationary seismic data were transformed into the time-frequency domain by generalized S-transform. Based on autocorrelation theory, the wavelet was extracted point by point and the time-varying wavelet matrix was reconstructed, instead of extracting only one time-invariant wavelet from one trace of seismic record. Then it was applied to the inversion model for blind deconvolution of nonstationary seismic data. The results of model tests and actual data proces-sing demonstrate that, the proposed data-driven method considers the time-varying characteristics of seismic signals, and it can be more suitable for actual seismic data and get more accurate time-va-rying wavelet results and the reflection coefficient profiles with higher resolution, compared with the traditional time-invariant wavelet inversion method.
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