Data assimilation for a geological process model using the ensemble Kalman filter
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 poste...
| Published in: | Basin Research |
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| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2017
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| Subjects: | |
| Online Access: | http://hdl.handle.net/11250/2493745 https://doi.org/10.1111/bre.12273 |
| Summary: | 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. submittedVersion This 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. |
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