Matching pursuit-based sparse spectral analysis: Estimating frequency-dependent anomalies from nonstationary seismic data

Abstract

Spectral decomposition has been widely used to detect frequency-dependent anomalies associated with hydrocarbons. By ignoring the time-variant feature of the frequency content of individual reflected wavelets, we have adopted a sparse time-frequency spectrum and developed a matching pursuit-based sparse spectral analysis (MP-SSA) method to estimate the sparse time-frequency representation of the seismic data. Further, we evaluate a generalized nonstationary convolution model concerning propagation attenuation and frequency-dependent reflectivity, and we mathematically evaluate the sparse time-frequency spectrum of the nonstationary seismic data as being equal to the product of the Fourier spectrum of the source wavelet, frequency-dependent reflection coefficient, and the cumulative attenuation during seismic wave propagation. Therefore, the reflectivity spectrum, which is a combination of the frequency-dependent reflectivity and the propagation attenuation, can be determined by dividing the sparse time-frequency spectrum of the seismic data by the Fourier spectrum of the source wavelet. Application of the matching pursuit-based decomposition methods to synthetic nonstationary convolutional data illustrates that the adopted MP-SSA spectrum shows a higher time resolution than the matching pursuit-based Wigner-Ville distribution and the matching pursuit-based instantaneous spectral analysis spectra. Notably, the MP-SSA method can avoid spectral smearing, which may introduce distortions to the frequency-dependent anomaly estimation. Application of the amplitude versus frequency analysis based on MP-SSA to field data illustrates the potential of using the sparse reflectivity spectral intercept and gradient to detect the hydrocarbon reservoirs.