Deblended-data reconstruction for time-lapse seismic monitoring

We introduce a generalized concept of the so-called blending and deblending, and establish the generalizedblending and -deblending models. Accordingly, we establish a method of deblending, or deblended-data reconstruction, using these models. The generalized blending can handle real-life situations; this includes random encoding both in the space and time domain, both at the source and receiver side, thus all incoherent shooting, inhomogeneous shooting, non-uniform and under sampling. Similarly, the generalized deblending includes data reconstruction that works for all shot-generated-wavefields separation, spectrum recovery and balancing, regularization and interpolation, again both at the source and receiver side. However, we do face a challenging question: how to fully reconstruct deblended data from the fully generalized blended data. To address this challenge, we consider an iterative optimization scheme using a so-called closed-loop approach. We use the properties of blended signal specified by the blending code: the coherency of blended signal versus the incoherency of blending noise in the pseudo-deblended domain. This can be posed as an inverse problem with quantifying the coherency and its solutions by selecting optimal metrics of the coherency. We applied this method to synthetic datasets. The results show that our method succeeded to fully reconstruct deblended data from the fully generalized blended data. We discuss its applicability to time-lapse seismic monitoring as it ensures high repeatability of the surveys. Our methodology should reduce the repeatability problem because reconstructing deblended data in monitor surveys is much more realistic and reliable than positioning sources and receivers exactly as the baseline survey.