应用SVD约束的迭代反演混叠噪声压制方法

超高效混叠采集技术可实现多组震源同时激发产生地震波场，大幅度提高采集效率，但往往会导致相邻震源之间产生波场交叉而相互干涉，显著降低地震数据的信噪比。为此，提出一种基于奇异值分解（SVD）约束迭代反演的混叠噪声压制方法。首先以选定区域的混叠噪声奇异值对混叠数据的奇异值向量进行约束，然后利用迭代反演的策略逐步更新混叠数据的奇异值向量，最终在共炮检距域或动校正后的共中心点域实现混叠噪声与有效信号的分离。数据测试结果表明，该方法可在压制混叠噪声的同时，充分地保护有效信号。

Blended noise suppression based on SVD constrained iterative inversion

The blending acquisition technology with ultra-high productivity emerged as a promising way of significantly increasing the efficiency of seismic acquisition. However, blended noises from simultaneous sources smear effective energy. We propose a blended noise suppression approach based on singular value decomposition (SVD) constrai-ned iterative inversion. In the proposed approach, the key step is that the maximum singular value of the blended noise in a user-defined window is used as a constraint to iteratively update the singular value vector of the blended data in common offset gathers or the common midpoint gathers after normal moveout, where the SVD has a superior capability to represent the coherency of the seismic data. In this way, the singular value vector corresponding to the effective energy is recursively po-lished by the iteratively updated constraint in the inversion framework to obtain final de-blended data. Applications to simulated field data have proved that the method is effective for suppressing noises while protecting signals.