Abstract
Information of importance in reflection seismic method resides in the reflectivity series. In order to extract this information from obtained data, a deconvolution filter is the core of seismic data processing. Since whitening deconvolution was first introduced by Robinson1), it has been used in the oil industry on a routine basis. Whitening deconvolution is based on following assumptions: (1) A reflectivity series is an uncorrelated random series. (2) A seismic wavelet is minimum phase. Deconvolution outputs, however, ofter show that the minimum phase assumption on seismic wavelet is incorrect. In this paper we deeply investigate in depth this assumption and consider several types of deconvolution methods. Predictive deconvolution can be applied to both minimum and non-minimum phase wavelets and it is equivalent to phase correction method2), 3) for Vibroseis data, in a mathematical sense. In the case of whitening deconvolution, to obviate the minimum phase assumption, a statistical wavelet estimation approach, 4) using property of reflectivity series, is explained.
Published Version
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