A blind spectral inversion method for seismic data

In seismic processing, an error exists in wavelet extraction because of complex geological structures, resulting in the low accuracy of deconvolution and inversion. Blind deconvolution is an effective method for solving the problem mentioned above. Compared with deconvolution, spectral inversion reduces the tuning effect and calculates more accurate reflectivity based on the odd-even decomposition theorem of reflection coefficients. Therefore, we combined blind deconvolution and spectral inversion and proposed blind spectral inversion. Given an initial wavelet, we calculate the reflectivity based on spectral inversion and update the wavelet for the next iteration. The blind spectral inversion method inherits the wavelet robustness of blind deconvolution and high resolution of spectral inversion, which is suitable for reflectivity inversion. Applications to synthetic and field seismic datasets demonstrate that the blind spectral inversion method can accurately calculate the reflectivity even if there is an error in wavelet extraction.