  Time-lapse full-waveform inversion (FWI) has become a powerful tool for
characterizing and monitoring subsurface changes in various geophysical
applications. However, non-repeatability (NR) issues caused, for instance, by
GPS inaccuracies, often make it difficult to obtain unbiased time-lapse models.
In this work we explore the portability of combining a receiver-extension FWI
approach and Bayesian analysis to mitigate time-lapse noises arising from NR
issues. The receiver-extension scheme introduces an artificial degree of
freedom in positioning receivers, intending to minimize kinematic mismatches
between modeled and observed data. Bayesian analysis systematically explores
several potential solutions to mitigate time-lapse changes not associated with
reservoir responses, assigning probabilities to each scenario based on prior
information and available evidence. We consider two different subsurface models
to demonstrate the potential of proposed approaches. First, using the Marmousi
model, we investigate two NR scenarios associated with background noise in
seismic data. Second, using a challenging deep-water Brazilian pre-salt
setting, we investigate several NR scenarios to simulate real-world challenges.
Our results demonstrate that combining Bayesian analysis with the
receiver-extension FWI strategy can mitigate adverse NR effects successfully,
producing cleaner and more reliable time-lapse models than conventional
approaches. The results also reveal that the proposed Bayesian weighted
procedure is a valuable tool for determining time-lapse estimates through
statistical analysis of pre-existing models, allowing its application in
ongoing time-lapse (4D) projects.
