Articles | Volume 13, issue 7
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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Rohitash Chandra on behalf of the Authors (26 Nov 2019)  Author's response    Manuscript
ED: Reconsider after major revisions (17 Mar 2020) by Richard Neale
AR by Anna Wenzel on behalf of the Authors (28 Apr 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (10 May 2020) by Richard Neale
RR by Anonymous Referee #2 (18 May 2020)
ED: Publish as is (06 Jun 2020) by Richard Neale
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.