Inversion‐based deblending using migration operators

In this paper, we compare the denoising‐ and inversion‐based deblending methods using Stolt migration operators. We use Stolt operator as a kernel to efficiently compute apex‐shifted hyperbolic Radon transform. Sparsity promoting transforms, such as Radon transform, can focus seismic data into a sparse model to separate signals, remove noise or interpolate missing traces. Therefore, Radon transforms are a suitable tool for either the denoising‐ or the inversion‐based deblending methods. The denoising‐based deblending treats blending interferences as random noise by sorting the data into new gathers, such as common receiver gather. In these gathers, blending interferences exhibit random structures due to the randomization of the source firing times. Alternatively, the inversion‐based deblending treats blending interferences as a signal, and the transform models this signal by incorporating the blending operator to formulate an inversion problem. We compare both methods using a robust inversion algorithm with sparse regularization. Results of synthetic and field data examples show that the inversion‐based deblending can produce more accurate signal separation for highly blended data.