![]() ![]() Dealing with different time windows gave an ability to create a time-variant kernel matrix, which was used to remove matrix effects from seismic data. The Gabor transform derived the signal's time–frequency analysis and estimated wavelet properties from different windows. To carry out this aim, stochastic Gabor inversion in the time domain was used. The aim of this study was to apply non-stationary deconvolution to eliminate time variant wavelet effects from the signal and to convert the estimated reflection series to the absolute AI by getting bias from well logs. Trying to convert those reflection series to AI, also merging with the low frequency initial model, can help us. Although stationary reflectivity inversion methods are trying to estimate reflectivity series, because of incorrect assumptions their estimations will not be correct, but may be useful. Those are pitfalls of stationary reflectivity inversion. To increase temporal resolution and interpretation ability, amplitude compensation and phase correction are inevitable. However, all stationary deconvolution methods are designed following that assumption. To carry out seismic inversion correctly it is important to not assume that the seismic signal is stationary. To change amplitude to AI, removal of wavelet effects from the seismic signal in order to get a reflection series, and subsequently transforming those reflections to AI, is vital. To delineate subsurface lithology to estimate petrophysical properties of a reservoir, it is possible to use acoustic impedance (AI) which is the result of seismic inversion. ![]()
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