Abstract：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.
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