Application of non-stationary iterative time-domain deconvolution

Abstract

In this study, non-stationary iterative time-domain deconvolution (CNS-ITD) is investigated. The propagating wavelets are first estimated in several overlapping Gabor windows of the data. Matrix-vector operations in the time-domain are then performed by estimating a small number of columns of the wavelet matrix by interpolation within a sparse iterative estimation for the largest reflectivities. The iteration process is stopped when a minimum root mean square (RMS) residual or a maximum number of iterations is reached. Although initially formulated on the basis of work in earthquake seismology, CNS-ITD is a matching pursuit type of approach performed continuously in the time-domain for the non-stationary case. The results can then be convolved with a higher frequency wavelet in order to make the results stationary in time and to increase the resolution of the data. We first apply CNS-ITD to synthetic data with a time-varying attenuation, where the method successfully identifies the largest reflectors in the data. We then apply CNS-ITD to two observed shallow seismic datasets where improved resolution is obtained.