Mixed-phase wavelet and quality factor estimation for seismic data based on particle swarm optimization
With starting wavelet and quality factor(Q),high resolution processing and inversion for non-stationary seismic data can be deployed via solving the related equation that contains the seismic wavelet-and attenuated-filtering.In order to obtain an accurate reflectivity or elastic parameters(velocity and density)model.We proposed a new estimation method for starting wavelet and Q model by combining the encoder-decoder of wavelet root distribution and binary-to-decimal conversion of Q model.In virtue of global optimization algorithm and seismic-to-well tie as criterion function,this method can output the rational mixed-phase wavelet and Q value simultaneously and avoid the drawback of the conventional Q extraction.Synthetic and real data test can illustrate the superb performance of proposed method.In contrast with the calculated zero-phase or constant phase wavelet,mixed-phase of wavelet is updated by fitting nearby well seismic trace with well-log data,which corresponds with the propagated wavelet in reality.The attenuation factors can be evaluated in complex noise case.Compared with conventional in-vert Q filter and constant phase scanning method,the accurate wavelet and Q model provided by the ap-proach are used to calculate deconvolution results,which possess higher quality.For nonstationary seis-mic data,the outputted results can reveal more details in subsurface.The results provide basic parame-ters for the processing of high-resolution seismic data.
mixed-phaseseismic waveletquality factor(Q)particle swarm optimizationnonstation-ary seismic data
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