Résumé:
In seismic deconvolution, blind approaches must
be considered in situations where reflectivity sequence, source
wavelet signal, and noise power level are unknown. In the presence
of long source wavelets, strong interference among the reflectors
contributions makes the wavelet estimation and deconvolution
more complicated. In this paper, we solve this problem in a
two-step approach. First, we estimate a moving average (MA)
truncated version of the wavelet by means of a stochastic expectation–maximization (SEM) algorithm. Then, we use Prony’s
method to improve the wavelet estimation accuracy by fitting an
autoregressive moving average (ARMA) model with the initial
truncated wavelet. Moreover, a solution to the wavelet initialization problem in the SEM algorithm is also proposed. Simulation
and real-data experiment results show the significant improvement brought by this approach.
