One traditional technique of Q estimation is based on a method of spectral ratios in which the spectral estimates are made at each time sample. This procedure employs a recursive DFT to compute the spectral estimate, converts the power spectrum to db, finds the dominant frequency, and computes the Q estimate from the slope using only those spectral values which exceed a specified threshold of the high end of the spectrum relative to that frequency.

The problem with any DFT-based method is of course what length of window to choose. Different window lengths generally will affect to shape of the power spectrum and subsequent Q and will also change the inherent resolution obtained.

With waveletQTM we utilize a wavelet-based approach to computing the power spectrum ensuring the issue of window support as a function of wavelet scale (frequency) is properly taken care of. Calculation of the slope is then computed in a manner similar to the DFT method using a robust fitting algorithm. After estimation of the raw Q field appropriate smoothing is done in a 3D manner.

Effective Q (Data courtesy of Nexen Energy ULC)