Non-stationary Iterative Time-Domain Deconvolution for Enhancing the Resolution of Shallow Seismic Data

Abstract

The resolution\nof near-surface seismic reflection data is often limited by attenuation and\nscattering in the shallow subsurface which reduces the high frequencies in the\ndata. Compensating for attenuation and scattering, as well as removing the\npropagating source wavelet in a time-variant manner can be used to improve the\nresolution. Here we investigate continuous non-stationary iterative time-domain\ndeconvolution (CNS-ITD), where the seismic wavelet is allowed to vary along the\nseismic trace. The propagating seismic wavelet is then a combination of the\nsource wavelet and the effects of attenuation and scattering effects, and can\nbe estimated in a data-driven manner by performing a Gabor decomposition of the\ndata. For each Gabor window, the autocorrelation is estimated and windowed\nabout zero lag to estimate the propagating wavelet. Using the matrix-vector\nequations, the estimated propagating wavelets are assigned to the related\ncolumns of a seismic wavelet matrix, and these are then interpolated to the\ntime location where the maximum of the envelope of the trace occurs within the\niterative time-domain deconvolution. Advantages of using this data-driven,\ntime-varying approach include not requiring prior knowledge of the attenuation\nand scattering structure and allowing for the sparse estimation of the\nreflectivity within the iterative deconvolution. We first apply CNS-ITD to\nsynthetic data with a time-varying attenuation, where the method successfully\nidentified the reflectors and increased the resolution of the data. We then\napplied CNS-ITD to two observed shallow seismic reflection datasets where\nimproved resolution was obtained.