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Data assimilation for a geological process model using the ensemble Kalman filter
- Source: Basin Research, Volume 30, Issue 4, Jul 2018, p. 730 - 745
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- 26 Apr 2017
- 03 Dec 2017
- 27 Dec 2017
Abstract
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 characterise 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.