An evaluation criterion on the accuracy of time-varying wavelet extraction based on singular value decomposition

The accuracy evaluation of time-varying wavelet extraction plays an important role in seismic data processing. However, the conditional evaluation criterion is influenced seriously by noise. Therefore, we propose a time-varying wavelet accuracy criterion based on singular value decomposition (SVD). Since the Parsimony criterion, Kurtosis criterion and Absolute kurtosis criterion have good tolerability to noisy environment among the existing evaluation criteria for the non-stationary seismic wavelet extraction accuracy, the Parsimony criterion and SVD technology are combined to construct a SVD_P criterion which has better noise-tolerant ability; and the spectrum division is employed as the deconvolution method. The Parsimony criterion, Kurtosis criterion and SVD_P criterion are applied to the simulation experiment and field data processing to compare the precision of time-frequency domain time-varying wavelet extraction method and adaptive segmentation time-varying wavelet extraction method. The results show that all three criteria could provide valid evaluation of these two wavelet extraction method while the time-frequency domain wavelet extraction method is more accurate than the adaptive segmentation method. Additionally, the evaluation result of SVD_P criterion owns smallest error and highest evaluation precision. ©, 2015, Science Press. All right reserved.