LAUSR

As acquired seismic data is usually incomplete and noisy, simultaneous reconstruction and denoising is an extremely important step for the accurate interpretation of seismic data and subsequent processing. We propose a hybrid low-rank and sparsity constraint method with Hankel structure preservation to improve the performance of simultaneous reconstruction and denoising. The proposed method combines the advantages of high efficiency pertaining to sparsity-promoting transforms and the strong data adaptability of rank reduction methods. Meanwhile, a structure-preserving matrix is constructed to preserve the predefined Hankel structure of the twofold Hankel matrix to further improve the accuracy and efficiency of simultaneous reconstruction and denoising. Moreover, weighted nuclear norm minimization (WNNM) is introduced to adaptively assign weights to different singular values. Experimental results in both synthetic and field seismic data compared with other state-of-the-art methods demonstrate the superior performance of the proposed method.