Blended acquisition is an important concept because it offers the unusual economic prospect of higher quality (increased sampling) at a reduced cost (shorter acquisition time). The technique consists of activating two or more sources almost simultaneously, which allows many shots to be recorded in the time normally taken to acquire just one. The main challenge is to separate each blended shot into its constituent unblended shots, or equivalently remove for each of the contributing sources the noise contamination due to the firing of the other sources. In this expanded abstract we present an iterative method based on rank reduction filtering which removes most of the crosstalk noise, even in the presence of a low signal to noise ratio. Its effectiveness is demonstrated on an artificially blended dataset and a real blended wide azimuth dataset.

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