Time—Frequency Mixed-Domain Impedance Inversion for Nonstationary Seismic Based on Frequency-Dependent Q

Acoustic impedance (AI) reflects the physical properties of subsurface materials and is a key parameter in reservoir evaluation and hydrocarbon exploration. Traditional poststack AI inversion methods typically assume a time-invariant wavelet, neglecting the nonstationary effects caused by attenuation and absorption during wave propagation. Therefore, performing attenuation compensation prior to inversion can effectively enhance seismic recording resolution and improve inversion accuracy. Nonetheless, attenuation compensation frequently encounters challenges such as incomplete correction and the amplification of noise. In this context, impedance inversion of nonstationary seismic records that accounts for attenuation effects can effectively avoid cumulative errors and noise amplification caused by attenuation compensation, thereby enhancing both the efficiency and accuracy of the inversion process. Existing nonstationary inversion methods, typically based on constant-Q models and reliant solely on time-domain information, struggle to handle realistic frequency-dependent Q variations. To address these limitations, we propose a poststack acoustic impedance inversion method in the time-frequency mixed domain, applicable to frequency-dependent Q attenuation. First, we derive a nonstationary convolution model in both time and frequency domains based on the frequency-dependent Q model. Then, we construct an impedance inversion objective function in the time-frequency mixed domain and solve it using the alternating direction method of multipliers (ADMM). The ADMM algorithm enables efficient iterative optimization by decomposing the objective into subproblems and solving them alternately. Tests on synthetic and field data demonstrate that the proposed method can effectively perform AI inversion from nonstationary seismic records with frequency-dependent Q attenuation. By incorporating frequency-domain information, it enhances the accuracy and resolution of the AI inversion.