Non-stationary Iterative Time-Domain Deconvolution for Enhancing the Resolution of Shallow Seismic Data
ErgunErhan
2019
<p>The resolution
of near-surface seismic reflection data is often limited by attenuation and
scattering in the shallow subsurface which reduces the high frequencies in the
data. Compensating for attenuation and scattering, as well as removing the
propagating source wavelet in a time-variant manner can be used to improve the
resolution. Here we investigate continuous non-stationary iterative time-domain
deconvolution (CNS-ITD), where the seismic wavelet is allowed to vary along the
seismic trace. The propagating seismic wavelet is then a combination of the
source wavelet and the effects of attenuation and scattering effects, 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. Using the matrix-vector
equations, the estimated propagating wavelets are assigned to the related
columns of a seismic wavelet matrix, and these are then interpolated to the
time location where the maximum of the envelope of the trace occurs within the
iterative time-domain deconvolution. Advantages of using this data-driven,
time-varying approach include not requiring prior knowledge of the attenuation
and scattering structure and allowing for the sparse estimation of the
reflectivity within the iterative deconvolution. We first apply CNS-ITD to
synthetic data with a time-varying attenuation, where the method successfully
identified the reflectors and increased the resolution of the data. We then
applied CNS-ITD to two observed shallow seismic reflection datasets where
improved resolution was obtained. </p>