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Geophysical Prospecting: Iterative method for the separation of blended seismic data: discussion on the algorithmic aspects

Recently much attention has been given to the possibility of shooting in an overlapping fashion, the so‐called blended or simultaneous acquisition. In conventional acquisition the time intervals between successive sources are large enough to avoid interference in time. In blending, a temporal overlap between source responses is allowed. This additional degree of freedom in survey design has the potential to significantly reduce seismic acquisition costs while maintaining or improving the data quality. Deblending is the procedure of retrieving data as if they were acquired in the conventional, unblended way. This is an essential step in the case where standard processing flows are applied. Several methods have been proposed to perform data separation with the majority of them falling into two categories. The first category consists in methods that filter out the blending noise by arranging seismic data in some domain, whereas inversion techniques fall into the second category. We recently introduced an iterative estimation and subtraction algorithm that integrates elements of both categories. This method exploits the fact that the character of the blending noise differs in different domains, e.g., it is coherent in the common source domain but incoherent in the common receiver, common‐offset or common midpoint domains. Up to now, our method relies on the interaction with a human operator. The automation of the thresholding process is addressed in this paper, leading to a hands‐off algorithm for the separation of blended data, optimized for both efficiency and effectiveness. We found that one of the major limiting factors is the edge artefacts generated by the coherence‐pass filter. The effectiveness of the coherence‐pass filter has a considerable influence on the convergence of our deblending algorithm. This is shown by testing different coherence‐pass filters on marine data as well as introducing errors in the coherence‐pass process. We show that these data estimation errors can be handled properly and the best result is obtained using a τ‐p filter as a coherence‐pass filter. Furthermore, very promising results are obtained on numerically‐blended land data.

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