We consider the problem of conditioning a geological process-based computer
simulation, which produces basin models by simulating transport and deposition
of sediments, to data. Emphasising uncertainty quantification, we frame this as
a Bayesian inverse problem, and propose to characterize the posterior
probability distribution of the geological quantities of interest by using a
variant of the ensemble Kalman filter, an estimation method which linearly and
sequentially conditions realisations of the system state to data.
A test case involving synthetic data is used to assess the performance of the
proposed estimation method, and to compare it with similar approaches. We
further apply the method to a more realistic test case, involving real well
data from the Colville foreland basin, North Slope, Alaska.Comment: 34 pages, 10 figures, 4 table
