Enhancing The Resolution Of Near Surface Seismic Reflection Data Using Time Variant Iterative Time Domain Deconvolution

Attenuation in the shallow subsurface affects the resolution of near surface seismic reflections by limiting the higher frequencies in the data. Compensating for attenuation and removing the source wavelet can be used to improve the resolution, and iterative time domain deconvolution is a useful tool to do this. A time-varying propagating wavelet is a combination of the source wavelet and the effects of attenuation and can be estimated in a data-driven manner by performing a Gabor decomposition of the data. For each Gabor window, the autocorrelation is estimated and windowed about zero lag to estimate the propagating wavelet. By correlating the estimated wavelet and the non-stationary windowed trace, iterative time domain deconvolution can be used to increase the resolution of the seismic image. Several advantages of using this data driven, time-varying approach include not requiring initial knowledge of the attenuation structure and allowing for different numbers of reflectors within each Gabor window. We first apply this to synthetic data with time varying attenuation applied and time-varying iterative time domain deconvolution successfully identified the strongest reflectors and increased the resolution of the data. We then apply this approach to several observed shallow seismic reflection datasets.