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

One of the goals of seismic processing is to improve the temporal resolution of seismic data. Seismic data usually have not enough temporal resolution because of band-limited nature of source signatures. In this paper we introduce a method in which we extrapolate deconvolved seismic spectrum for recovery of missed frequencies. 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. The selected part of spectrum is decomposed into some principal components. Each principal component has periodic pattern without trend, a narrow band frequency spectrum, and well defined characteristics to be extrapolated. After extrapolation of each principal component, the spectrum is reconstructed by combining the associated extrapolated principal components.