Abstract

In practical seismic imaging, attenuation and dispersion of seismic waves due to subsurface anelasticity remain persistent challenges. These effects degrade seismic resolution and distort wavelet shapes, leading to inaccuracies in seismic interpretation (Feng Cheng et al., 2018; Łapinkiewicz et al., 2023). The Q filter applies or removes corrections for dispersion and attenuation of seismic wave. Quality Factor (Q) can quantitatively measure the attenuation of seismic waves and it can compensate for seismic wave amplitude attenuation and correct the phase distortion. Ideally the subsurface is not perfectly elastic, higher frequencies are absorbed more rapidly and travel faster than lower frequencies. This results in frequency dependent seismic attenuation where the amplitude of high frequency components diminishes with increasing travel time and dispersion where wave velocity becomes frequency dependent, distorting the wavelet shape. To address these challenges, generally Q-filters are applied in a travel-time-dependent manner, typically on CMP gathers prior to NMO correction. Depending on the processing objective, amplitude-only filtering is used to compensate for frequency dependent amplitude loss, while phase-only filtering corrects the dispersion by restoring the original phase characteristics of the wavelet. In this paper we discussed on spectral-ratio (SR) method, which estimates Q by directly comparing the amplitude spectra of two seismic waveforms recorded at different times. The spectral-ratio method can yield unstable results in the presence of noise (Łapinkiewicz et al., 2023). To enhance reliability, inverse Q values were estimated and applied only after the removal of random noise and multiple reflections. This ensures that the Q-compensation is applied to clean seismic data, thereby improving the accuracy of attenuation correction and phase restoration.