Bandwidth enhancement: Inverse Q filtering or time-varying Wiener deconvolution?

摘要

Dispersion and attenuation corrections can improve the resolution of seismic data. This significantly facilitates interpretation. In principle, inverse Q filtering and the time-varying Wiener deconvolution can achieve this. Inverse Q filtering is a deterministic process that requires knowledge of the quality factor Q, whereas the time-varying Wiener de-convolution is a statistical approach based on the estimation of the nonstationary propagating wavelet. Dispersion corrections based on phase-only inverse Q filtering is an inherently stable method that is robust in the presence of noise. Attenuation corrections via amplitude-only inverse Q filtering, on the other hand, is likely to lead to noise amplification as well as bandwidth enhancement. Dispersion corrections via the time-varying Wiener deconvolution are challenging because these require estimation of a nonstationary, frequency-dependent, nonminimum-phase wavelet. Fortunately, attenuation corrections via the Wiener deconvolution need only es