Post-stack Quality Factor Estimation Using Regularized Spectral Ratio Method after Similarity-weighted Stacking

The estimation of quality factor, Q, can affect several stages in the whole hydrocarbon exploration process including seismic imaging, interpretation, and reservoir characterization. The most widely used approach for estimating Q is the spectral ratio method (SRM). However, the spectral division in SRM may not be stable due to the spectral nulls. The shaping regularized inversion that treats the spectral division as a regularized least-squares inversion problem can help solve the spectral-nulls problem and make the spectral division stable. In the case of very noisy seismic data, the time-frequency maps can not be optimally obtained and thus the Q estimation performance will be strongly affected even with regularized inversion. I propose a post-stack Q estimation approach that applies the regularized spectral ratio method (RSRM) on the stacked trace using a similarity-weighted stacking technique. Field data example shows that the pre-stack data is not reliable for estimating Q while the stacked data using similarity-weighted stacking can obtain a reasonable result.