How incoherent can we be? Phase-encoded linearised inversion with random boundaries

How incoherent can we be? Phase-encoded linearised inversion with random boundaries

Chris Leader and Ali Almomin

Abstract:

To perform linearised inversion on seismic exploration scale datasets we are continually looking for methods to accelerate computation and reduce data handling overhead. One option to accelerate reverse time imaging is to use random domain boundaries for the source wavefield computation, alleviating much of the required IO in favour of some additional computation. Additionally, data handling problems can be addressed by phase-encoding data (weighting, shifting, summing) and then inverting for a common model between realisations. Both random boundary and phase-encoding methods rely on wavefield incoherency during correlation and stacking to build a clean image. Here we investigate if these can be effectively used together, or if these techniques combined create wavefields that are too incoherent, slowing convergence as a function of cost when compared to linearised inversion without phase-encoding. We show that by using multiple realisations per iteration we can improve convergence and create cleaner reflectivity images.

How incoherent can we be? Phase-encoded linearised inversion with random boundaries