Multichannel block sparse Bayesian learning reflectivity inversion with lp-norm criterion-based Q estimation

The generation of attenuated seismic reflection data can be described via a nonstationary convolution model with quality factor Q. In according with the linear matrix-matrix multiplication operation that is deduced to depict the multichannel nonstationary seismic received signal, we present a spatially correlated reflectivity inversion approach with the Q estimation based on block sparse Bayesian learning (bSBL) and an lp-norm criterion. In contrast to pre-existingtime-variant deconvolution, the proposed technique can eliminate the wavelet-filtering and Q-filtering effect simultaneously and retrieve an optimal reflectivity matrix without providing Q value by anticipation. Through introducing the lp-norm criterion and scanning Q strategy, we could capture the optimal Q to calculate the blurring operator in the inversion function. The inverted reflectivity result will be satisfied with presupposition that multi-trace reflectivity is comparatively sparse corresponding to its minimum lp-norm when the optimal Q structure is applied to build the attenuation equation. In reflectivity inversion area, the relationship among reflection spikes in adjacent traces as a priori information is represented by a covariance matrix of reflectivity model in the bSBL framework and assists in solving procedure to promote the inversion precision. To diminish the influence of man-made parameter selection, the hyperparameters of reflectivity and noise model are estimated in virtue of expectation-maximization (EM) algorithm. The contribution of spatial correlation could guarantee a higher quality of reflectivity image. New method merges the multichannel reflectivity inversion and Q estimation into a single processing and avoids the drawback of the conventional Q extraction technique. Synthetic and field data sets prove the practicality of the developed technique and indicate the favorable anti-noise capability.