Q-factor estimation from surface seismic data in the time-frequency domain: A comparative analysis

Abstract

The quality factor Q is generally used to describe seismic attenuation that leads to amplitude decay (AD) and wavelet distortion. Time-frequency transforms are commonly used to measure quality factor Q on surface seismic data. These methods capture frequency changes over time using a fixed or variable sliding time window. Other adaptive transforms also can provide time localization, and they often are superior for Q estimation. In this study, we compare three time-frequency transforms and indicate how the choice of a fixed- or variable-time window or an adaptive transform affects the accuracy and robustness of Q-factor estimation. We use the short-time Fourier and continuous wavelet transforms as fixed- and variable-window transforms, respectively. The synchrosqueezed wavelet transform is used as an adaptive transform. We compare four Q-factor estimation methods in the time-frequency domain, such as the AD, spectral ratio, centroid frequency shift, and compound time-frequency variable methods. Furthermore, we study some of the difficulties associated with these estimation methods, such as quantitative attenuation sensitivity, noise robustness, regression bandwidth influence, and key parameter selection for each time-frequency transform. Real data examples are used to investigate the robustness of Q-factor estimation with different methods using different time-frequency transforms and the statistics of how well the attenuation measurements match the expected seismic attenuation behavior. Furthermore, in these real data examples, we are able to use the Q estimates to compensate for attenuation through inverse Q filtering.