Articles | Volume 13, issue 7
https://doi.org/10.5194/gmd-13-2959-2020
https://doi.org/10.5194/gmd-13-2959-2020
Methods for assessment of models
 | 
08 Jul 2020
Methods for assessment of models |  | 08 Jul 2020

Surrogate-assisted Bayesian inversion for landscape and basin evolution models

Rohitash Chandra, Danial Azam, Arpit Kapoor, and R. Dietmar Müller

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Short summary
Forward landscape and sedimentary basin evolution models pose a major challenge in the development of efficient inference and optimization methods. Bayesian inference provides a methodology for estimation and uncertainty quantification of free model parameters. In this paper, we present an application of a surrogate-assisted Bayesian parallel tempering method where that surrogate mimics a landscape evolution model. We use the method for parameter estimation and uncertainty quantification.