Improvement of Temporal Resolution of Seismic Data Using Singular Spectrum Analysis And Autoregressive Methods

Temporal resolution of seismic data is proportional to the seismic band width. Seismic data still have not enough temporal resolution because of the band-limited nature of available data even if it is deconvolved. Lower and higher frequencies of seismic data spectrum are missing and cannot be recovered by the usual deconvolution methods. Because of absorption, high frequencies belonging to the spectrum are missing and recovery of lower frequencies is also a big deal (Lindseth, 1979). Many different deconvolution techniques have been developed to process the data obtained from various sources ranging of seismic data. especially, since for many years in seismic processing, they have been used to improve the temporal resolution of seismic data. In this paper we introduce a method that is the generalization of the autoregressive (AR) spectral extrapolation based method originally applied by Hakan Karsli (2006), which extrapolates the deconvolved seismic spectrum for recovery of missed frequencies. When reflectors are numerous, the seismic spectrum is complicated and extrapolation by AR-based methods is uncertain. The introduced method takes a certain part of both real and imaginary parts of the spectrum, where S/N is high compare to the rest of the spectrum, and extrapolates lower and higher portions of the spectrum using Singular Spectrum Analysis (SSA) and Autoregressive model. Experience shows that a 3–10 dB drop from the maximum amplitude of the spectrum of the source wavelet represents a high SNR portion of the spectrum. Because of the existence of unwanted noise, the usual regression algorithms do not lead to favorable results. In second step of extrapolation algorithm we decompose selected spectrum by SSA.