Time-lapse inversion plays an important role in monitoring applications. Uncertainties in seismic inversion mean that there are many time-lapse changes in subsurface properties consistent with a given time-lapse data set and monitor survey, including changes that are implausible given our prior knowledge. Many existing time-lapse inversion methodologies that aim to minimize spurious differences while preserving real changes are equivalent to undirected navigation about the inversion nullspace. We develop an approach that explicitly navigates the inversion nullspace to find the data-consistent time-lapse model that best satisfies our prior knowledge. In synthetic examples, this approach demonstrates a significant capacity to mitigate the effects of nonreproducible noise and changing acquisition and to identify when time-lapse differences fall below the confidence threshold described by nullspace shuttling.

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