Estimation of Q factors from reflection seismic data for a band-limited and stabilized inverse Q filter driven by an average-Q model

Reliable Q estimation is desirable for model-based inverse Q filtering to improve seismic resolution. On the one hand, conventional methods estimate Q from the amplitude spectra or frequency variations of individual wavelets at different depth (or time) levels, which is vulnerable to the effects of spectral interference and ambient noise. On the other hand, most inverse Q filtering algorithms are sensitive to noise, in order not to boost them, sometimes at the expense of degrading compensation effect. In this paper, the average-Q values are obtained from reflection seismic data based on the Gabor transform spectrum of a seismic trace. We transform the 2-D time-variant frequency spectrum into the 1-D spectrum, and then estimate the average-Q values based on the amplitude attenuation and compensation functions, respectively. Driven by the estimated average-Q model, we also develop a modified inverse Q filtering algorithm by incorporating a time-variant bandpass filter (TVBF), whose high cut off frequency follows a hyperbola along the traveltime from a specified time. Finally, we test this modified inverse Q filtering algorithm on synthetic data and perform the Q estimation procedure on a real reflection seismic data, followed by applying the modified inverse Q filtering algorithm. The synthetic data test and the real data example demonstrate that the algorithm driven by average-Q model may enhance the seismic resolution, without degrading the signal-to-noise ratio.