@article{eage:/content/journals/10.1111/bre.12273, author = "Skauvold, Jacob and Eidsvik, Jo", title = "Data assimilation for a geological process model using the ensemble Kalman filter", journal= "Basin Research", year = "2018", volume = "30", number = "4", pages = "730-745", doi = "https://doi.org/10.1111/bre.12273", url = "https://www.earthdoc.org/content/journals/10.1111/bre.12273", publisher = "European Association of Geoscientists & Engineers", issn = "1365-2117", type = "Journal Article", abstract = "Abstract We consider the problem of conditioning a geological processā€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.", }