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dc.contributor.authorSkauvold, Jacob
dc.contributor.authorEidsvik, Jo
dc.date.accessioned2018-04-12T07:12:06Z
dc.date.available2018-04-12T07:12:06Z
dc.date.created2018-01-16T10:55:13Z
dc.date.issued2017
dc.identifier.issn0950-091X
dc.identifier.urihttp://hdl.handle.net/11250/2493745
dc.description.abstractWe 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.nb_NO
dc.language.isoengnb_NO
dc.publisherWileynb_NO
dc.titleData assimilation for a geological process model using the ensemble Kalman filternb_NO
dc.typeJournal articlenb_NO
dc.description.versionsubmittedVersionnb_NO
dc.source.journalBasin Researchnb_NO
dc.identifier.doi10.1111/bre.12273
dc.identifier.cristin1543850
dc.relation.projectNorges forskningsråd: 234001nb_NO
dc.description.localcodeThis is the pre-peer reviewed version of the following article: [Data assimilation for a geological process model using the ensemble Kalman filter], which has been published in final form at [https://onlinelibrary.wiley.com/doi/abs/10.1111/bre.12273]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.nb_NO
cristin.unitcode194,63,15,0
cristin.unitnameInstitutt for matematiske fag
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode2


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