A time-varying wavelet extraction using local similarity

Abstract

Seismic wavelet extraction is an integral part of high-resolution seismic analysis. However, most extraction methods ignore the time-varying characteristic of wavelets introduced by attenuation, scattering, and other physical processes during propagation. We have developed a time-varying wavelet extraction method based on local similarity. This method estimates the amplitude spectra by spectral modeling in the time-frequency domain. We estimated the phase of each spectrum in two steps: First, the phase range was estimated by the bispectrum of the high-order cumulants, and then the phase spectrum at every point was extracted with additional local similarity optimization. The extracted nonstationary wavelet improved the resolution of the wavelet estimation in the adjacent layers. We have determined the practicability and reliability of the proposed method using a numerical simulation, and we have compared the results of this method with those of the adaptive segmentation method.