Alternate Iterative Synchronous Inversion of Acoustic Impedance and Quality Factor for Nonstationary Seismic

During the propagation of seismic waves in the Earth, the influence of Earth absorption leads to energy attenuation and phase distortion. Traditional seismic acoustic impedance (AI) inversion typically utilizes a constant wavelet to construct a wavelet library for AI inversion, resulting in outcomes that struggle to effectively portray the spatial variation characteristics of underground reservoirs. While using an inverse Q filter for energy compensation before AI inversion can enhance precision, accurately extracting the Q factor before the inverse Q filter is complex. Moreover, the inverse Q filter often amplifies noise interference, impacting the accuracy of the inversion. Therefore, we propose an alternating iterative synchronous inversion method for AI and the quality factor. Compared with the conventional method, the impedance information and attenuation parameters of seismic data can be obtained directly, which improves the efficiency and accuracy of inversion. The method can be divided into two steps. Firstly, we construct the L _1-2 -norm regularization of the AI inversion objective function, which is solved using the difference of convex algorithm (DCA) and the alternating direction method of multipliers (ADMM). Subsequently, the Markov Chain Monte Carlo (MCMC) method is employed to solve the nonstationary forward equation, with the fitting of the centroid frequency of seismic data and synthetic seismic records. Through continuous iteration of AI and the quality factor until the error is below the specified threshold or reaches the designated number of iterations, stable AI and average Q factors are obtained. Numerical simulations demonstrate the reliability of the proposed method, showcasing higher accuracy compared to stable wavelet inversion and constant Q inversion. Real data applications validate the method’s effectiveness, providing more dependable results for reservoir fluid identification. The obtained average Q factors can be employed for seismic data processing and interpretation.