A numerical study on deblending of land simultaneous shooting acquisition data via rank‐reduction filtering and signal enhancement applications

ABSTRACT We propose a workflow of deblending methodology comprised of rank‐reduction filtering followed by a signal enhancing process. This methodology can be used to preserve coherent subsurface reflections and at the same time to remove incoherent and interference noise. In pseudo‐deblended data, the blending noise exhibits coherent events, whereas in any other data domain (i.e. common receiver, common midpoint and common offset), it appears incoherent and is regarded as an outlier. In order to perform signal deblending, a robust implementation of rank‐reduction filtering is employed to eliminate the blending noise and is referred to as a joint sparse and low‐rank approximation. Deblending via rank‐reduction filtering gives a reasonable result with a sufficient signal‐to‐noise ratio. However, for land data acquired using unconstrained simultaneous shooting, rank‐reduction–based deblending applications alone do not completely attenuate the interference noise. A considerable amount of signal leakage is observed in the residual component, which can affect further data processing and analyses. In this study, we propose a deblending workflow via a rank‐reduction filter followed by post‐processing steps comprising a nonlinear masking filter and a local orthogonalization weight application. Although each application shows a few footprints of leaked signal energy, the proposed combined workflow restores the signal energy from the residual component achieving significantly signal‐to‐noise ratio enhancement. These hierarchical schemes are applied on land simultaneous shooting acquisition data sets and produced cleaner and reliable deblended data ready for further data processing.