The determination of a seismic wavelet includes amplitude and phase spectrumestimation. Wavelet estimation methods can be classified into three main categories: (1) directlydeterministic measuring the wavelet, (2) statistically extracting the wavelet from the seismic dataand (3) extracting the wavelet by using well-log and seismic data. The deterministic methodsrequire that a seismic-well tie already exists, while the statistical method extracts an averageFowavelet from a specified window of 3D seismic data (Edgar and van der Baan, 2011). Waveletestimation using well and seismic data incorporates the “prior” reflectivity information in therRwavelet estimation (Richard et al., 1988). Statistical wavelets can be estimated from only theevseismic data without appealing to well logs. Most of the statistical wavelet estimations are basediewon the assumption that seismic traces are a convolution of the earth’s reflectivity and atemporally and spatially invariant zero or minimum phase wavelet. Statistical wavelets assumethat the autocorrelations of amplitude spectra of the seismic data are approximately equal to theOnseismic wavelet (Yilmaz, 2001).lyThe determination of the phase spectrum of a seismic wavelet is as important as thedetermination of the amplitude spectrum of a seismic wavelet. Van der Baan (2008) illustratedthat the phase mismatch might result in incorrect horizon picking or seismic well tying. Manytechniques have been developed to identify the phase seismic wavelet phase spectrum.Compared to the amplitude spectrum estimation of seismic wavelets, determining the phasespectrum is far more difficult and significantly affects seismic inversions (Hampson, 2007).Wiggins (1978) estimated the phase of seismic wavelet through the minimum entropydeconvolution. This technique does not need to assume the phase characteristics of a seismicExploration Geophysics of 67wavelet. White (1988) proposed to estimate the phase of a seismic wavelet by integrating themaximum kurtosis theory.
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