Estimation of near‐surface Q factor under constraint of layered velocity based on uphole survey data

Abstract

To improve the resolution of seismic data, it is important to accurately estimate the near‐surface quality factor, Q, which provides a measure of seismic wave attenuation. In view of the unique advantages provided by uphole surveys when investigating near‐surface structures, they are widely employed to estimate the near‐surface Q factor. However, the Q factor estimated using the traditional spectral ratio method is not always precise and provides larger oscillations in the estimated Q factors due to errors associated with first‐break picking and velocity estimation. Following on the traditional logarithmic spectral ratio method, a new method called the logarithmic spectral ratio integral method was proposed to estimate the layer Q factor using uphole survey data. It calculates first the weighted integral of the logarithmic spectral ratio in an effective frequency interval between non‐adjacent traces, then makes a linear regression between the inter‐trace travel moveout and the weighted integral of logarithmic spectral ratio under the constraint of velocity stratification. The result of model analysis shows that under an ideal condition (without first‐break picking errors), the layer Q values estimated by the logarithmic spectral ratio integral method are fairly consistent with the true layer‐specific Q values in the model. In addition, the Q values estimated from field‐measured data and data from forward modelling with 10% random noise added, both have smaller mean relative errors than the results using traditional spectral ratio method and the double‐linear regression method. A case study is employed and the results show that the layer Q factor estimated using the new method correlates well with the velocity stratification and is thus applicable for use with various uphole survey observation systems. Furthermore, all results indicate that the logarithmic spectral ratio integral method delivers a more precise and stable estimation of the layered Q than the other methods, and the anti‐noise characteristics are stronger.