Tomographic wavelet estimation via higher order statistics

Georgios B. Giannakis, Jerry M. Mendel

Research output: Contribution to conferencePaperpeer-review


Efficient estimation of nonminimum-phase (NMP) ARMA wavelets is a very important part of seismic deconvolution. "Phase-blind," autocorrelation-based approaches, used up to now, are restricted to minimum-phase (MP) wavelets. In this paper we develop two methods for NMP wavelet identification. We model the reflectivity sequence as a non-Gaussian, stationary and independent random process (rp). The estimated wavelet best fits the amplitude sensitive autocorrelation sequence and a phase-sensitive higher order statistics sequence of the (perhaps noisy) seismogram. The parameter estimates are consistent and robust with respect to additive Gaussian white noise. We test our methods and compare them with existing NMP wavelet estimation techniques, using synthetic examples.

Original languageEnglish (US)
Number of pages3
StatePublished - 1986
Externally publishedYes
Event1986 Society of Exploration Geophysicists Annual Meeting, SEG 1986 - Houston, United States
Duration: Nov 2 1986Nov 6 1986


Other1986 Society of Exploration Geophysicists Annual Meeting, SEG 1986
Country/TerritoryUnited States


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