97,365 research outputs found
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 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
A Manifesto for the Equifinality Thesis.
This essay discusses some of the issues involved in the identification and predictions of hydrological models given some calibration data. The reasons for the incompleteness of traditional calibration methods are discussed. The argument is made that the potential for multiple acceptable models as representations of hydrological and other environmental systems (the equifinality thesis) should be given more serious consideration than hitherto. It proposes some techniques for an extended GLUE methodology to make it more rigorous and outlines some of the research issues still to be resolved
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