Simultaneous joint migration inversion for accurate time-lapse analysis of sparse monitor surveys

We propose that Simultaneous Joint Migration and Inversion (SJMI) as an effective time-lapse tool, does not require dense source/receiver sampling and is suitable for inexpensive sparse monitoring surveys, because of both the inversion process inside the imaging/inversion scheme and the extra illumination provided by multiples. Furthermore, with the help of SJMI, we design an interleaved survey by shooting the sparse surveys without exact acquisition repetition, which provides us further extra illumination of the subsurface to handle the geometry sparsity. With one complex synthetic example based on the Marmousi model, we demonstrate that the extra illumination provided by both imaging/inversion with multiples and changing the survey during monitoring can provide us better time-lapse results, if the proper method is applied to the datasets.