## 1 Introduction  
Seismic waves experience amplitude loss and phase distortion due to viscoelastic properties of the subsurface media ([PERSON] et al., 2022) [1]. It is necessary to accurately estimate seismic attenuation (always expressed as \(1/Q\)) for compensation of energy loss and phase distortion and reservoir prediction. This is because the Q values not only relate to the attenuation which needs to be compensated by inverse Q filtering [2; 3; 4] or Q compensation migration [5; 6; 7; 8], but also Q is related to the variation in reservoir properties, such as rock porosity and pore fluids [9; 10; 11]. Depending on the application domain, the existing methods for calculating the interlayer Q value can be divided into three categories: firstly, the time-domain Q value estimation methods; secondly, frequency domain Q value estimation methods; thirdly, the time-frequency Q value estimation methods. In the time domain, the methods are the pulse amplitude decay method, pulse rise time method, wavelet simulation method, resolved signal method, etc. [PERSON] and [PERSON] (1974) [12] proposed the rise time principle based on the widening of the frequency band of seismic waves; the particular advantage of the rise time method is that the required record length is very short. [PERSON] and [PERSON] (1979) [13] further studied this basis and proposed a new algorithm for calculating the attenuation factor, called the rise time method. This method simplifies the implementation conditions but the error in the attenuation factor calculated by the slope is larger. A linear model for the attenuation of waves is presented in this method, and the \(Q\) value calculated is exactly independent of frequency. [PERSON] (1985) [14] proposed a wavelet simulation method to obtain the optimal Q value by finding the maximum correlation between the orthogonal simulation signal and the actual detection signal. [PERSON] et al. (2001) [15] processed wave velocity imaging and rise time imaging simultaneously through the attenuation imaging method in the time domain to efficiently calculate Q value. This method can remove the effect of source wave from the calculation process. [PERSON] (1989) [16] has compared and analyzed several common Q estimation methods, including amplitude decay, analytic signal, wavelet simulation, phase simulation, spectrum simulation, rise time, pulse amplitude, spectral ratio, and spectral simulation; no onemethod is generally superior. The time domain Q estimation methods are not recommended because they have some problems in distinguishing the intrinsic attenuation from other types of attenuation. It is difficult to distinguish the intrinsic attenuation from other types of attenuation. Q estimation methods developed in frequency domain are widely used as an advantage of their ability in avoiding the effects from non-intrinsic attenuation. These transform the reflections into the frequency domain, and the estimated Q values are based on the variation of their amplitude spectra. The spectral ratio method (SR) estimates Q values between large events, which is one of the most commonly used Q value estimation methods. However, when seismic data contain noise, the spectral ratio method has poor stability and the result depends on the selected frequency band. [PERSON] (1982) [17] improved this spectral ratio method by considering the relationship between intrinsic attenuation and geometric diffusion attenuation. [PERSON] and [PERSON] (1984) [18] established the transfer function of the attenuation factor in the frequency domain by using the fitting technique, which improved the accuracy of the calculation of the attenuation factor. [PERSON] and [PERSON] (1998) [19] used the spectral ratio method to estimate Q values in the pre-stack reflection. The Q value was estimated in the wave channel set by establishing the relationship between the wavelet log spectrum and the Q value. The spectral ratio method has been subsequently improved by many other authors.