Abstract：The stratigraphic filtering and viscoelasticity attenuation can cause narrower seismic frequency band,lower dominant frequency,and phase distortion.The Q estimation and inverse Q filtering are important tools for attenuation compensation and resolution improvement.We propose a Q estimation methods for surface seismic datasets.The propagating wavelet is subject to stratigraphic filtering,viscoelastic attenuation,and concomitant dispersion,which causing time-varying characteristics.Firstly,the nonstationary seismic forward modeling is achieved in the time domain by wavelet convolve matrix,which shows the time-varying characteristics of propagating wavelet.Secondly,based on the estimation of time-varying propagating wavelets,we study the Q estimation from wavelets time-varying characteristics both in the time domain and frequency domain.The optimization method is used to scanning Qin order to minimize the difference between two wavelets at two different depths.The time-varying wavelet spectral match (TWSM) needs no spectral ratio or linear regression,therefore has better noise resistance than the logarithmic spectral ratio (LSR) method.The time-varying wavelet time-domain match (TWTM) has higher accuracy because of no spectral estimation.Tests on both synthetic and real data demonstrate that the two methods can achieve a fast Q estimation,and have better robust Q estimation on low SNR data than the time-varying wavelet logarithmic spectral ratio (TWLSR).The synthetic data examples also show relation between the Q estimation accuracy and the time interval of two wavelets,which means the Q estimation has resolution limitation.
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