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Both methodologies provided interesting RAI profiles compared with well-log data, at low computational cost and with a simple parameterization. In this case, LI required more iterations for the conjugate-gradient least-squares solver, but the final results were not much affected. An important result of our tests is that an increase in the low-frequency gap in the data leads to slightly deteriorated CI quality. Our investigations confirm the theoretical and practical similarities of CI and LI regardless of the numerical strategy used in LI. A field-data example confirms the analysis using synthetic-data experiments. The numerical tests using the Marmousi2 model demonstrate that the CI and LI techniques can provide an RAI estimate of similar accuracy. Moreover, we investigate the sensitivity of both methods regarding the frequency content present in synthetic data. Then, we analyze the LI performance with two distinct solvers for the associated linear system. In our experiments, combining CI and LI cannot provide superior RAI results to those produced by each approach applied individually. We proceed to check whether the use of the CI results as the initial solution for LI can improve the RAI estimation. This allows us to conclude that both procedures are theoretically equivalent. We start by formulating the two approaches in a theoretically comparable manner. One is colored inversion (CI), which requires well-log information, and the other is linear inversion (LI), which requires knowledge of the seismic source wavelet. We have revisited and compared two alternative methods to transform stacked seismic data into RAI. Because of the frequency-band-limited nature of seismic data, seismic amplitude inversion cannot determine AI itself, but it can only provide an estimate of its variations, the relative AI (RAI). Acoustic impedance (AI) is a widely used seismic attribute in stratigraphic interpretation.

Colored inversion hampson russell