Robust Q-compensated multidimensional impedance inversion using seislet-domain shaping regularization

Abstract

Acoustic impedance (AI) inversion plays a vital role in seismic interpretation because AI contains valuable information on lithology and contributes to reservoir characterization. However, the effect of anelastic attenuation dissipates the energy and distorts the phase of seismic waves during their propagation in the earth. Such attenuation-induced effects will degrade the quality of AI inversion unless some preprocessing routines are performed in advance (e.g., inverse Q filtering). In order to invert for AI from nonstationary seismic data directly and enhance the lateral continuity, we have developed a robust Q-compensated multidimensional AI inversion method. The proposed method incorporates the Q-filtering operator into the conventional convolution model and provides an improved iteration scheme, which can avoid some of the errors introduced by those compensation-related processing routines. Furthermore, with the help of seislet nonlinear shaping regularization, the proposed method can accelerate the convergence rate during inversion and further improve the lateral continuity and accuracy of the final inverted results in the presence of noise. Synthetic and field data have been used to validate the effectiveness and robustness of the proposed method. The results demonstrate that compared with the conventional method, the proposed method can retrieve AI from nonstationary seismic data directly with improved efficiency and remove those possible artifacts caused by ambient noise.