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

Related authors

Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
Richard Scalzo, David Kohn, Hugo Olierook, Gregory Houseman, Rohitash Chandra, Mark Girolami, and Sally Cripps
Geosci. Model Dev., 12, 2941–2960, https://doi.org/10.5194/gmd-12-2941-2019,https://doi.org/10.5194/gmd-12-2941-2019, 2019
Short summary
Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models
Hugo K. H. Olierook, Richard Scalzo, David Kohn, Rohitash Chandra, Ehsan Farahbakhsh, Gregory Houseman, Chris Clark, Steven M. Reddy, and R. Dietmar Müller
Solid Earth Discuss., https://doi.org/10.5194/se-2019-4,https://doi.org/10.5194/se-2019-4, 2019
Revised manuscript not accepted

Related subject area

Numerical methods
A comparison of Eulerian and Lagrangian methods for vertical particle transport in the water column
Tor Nordam, Ruben Kristiansen, Raymond Nepstad, Erik van Sebille, and Andy M. Booth
Geosci. Model Dev., 16, 5339–5363, https://doi.org/10.5194/gmd-16-5339-2023,https://doi.org/10.5194/gmd-16-5339-2023, 2023
Short summary
AutoQS v1: automatic parametrization of QuickSampling based on training images analysis
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 16, 5265–5279, https://doi.org/10.5194/gmd-16-5265-2023,https://doi.org/10.5194/gmd-16-5265-2023, 2023
Short summary
Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions
Siting Li, Ping Wang, Hong Wang, Yue Peng, Zhaodong Liu, Wenjie Zhang, Hongli Liu, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 16, 4171–4191, https://doi.org/10.5194/gmd-16-4171-2023,https://doi.org/10.5194/gmd-16-4171-2023, 2023
Short summary
A dynamical core based on a discontinuous Galerkin method for higher-order finite-element sea ice modeling
Thomas Richter, Véronique Dansereau, Christian Lessig, and Piotr Minakowski
Geosci. Model Dev., 16, 3907–3926, https://doi.org/10.5194/gmd-16-3907-2023,https://doi.org/10.5194/gmd-16-3907-2023, 2023
Short summary
GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation
Emma J. MacKie, Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs, and Allan Zhang
Geosci. Model Dev., 16, 3765–3783, https://doi.org/10.5194/gmd-16-3765-2023,https://doi.org/10.5194/gmd-16-3765-2023, 2023
Short summary

Cited articles

Adams, J. M., Gasparini, N. M., Hobley, D. E. J., Tucker, G. E., Hutton, E. W. H., Nudurupati, S. S., and Istanbulluoglu, E.: The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds, Geosci. Model Dev., 10, 1645–1663, https://doi.org/10.5194/gmd-10-1645-2017, 2017. a
Ampomah, W., Balch, R., Will, R., Cather, M., Gunda, D., and Dai, Z.: Co-optimization of CO2 EOR and Storage Processes under Geological Uncertainty, Energy Proc., 114, 6928–6941, 2017. a
Asher, M. J., Croke, B. F., Jakeman, A. J., and Peeters, L. J.: A review of surrogate models and their application to groundwater modeling, Water Resour. Res., 51, 5957–5973, 2015. a
Bittner, E., Nußbaumer, A., and Janke, W.: Make life simple: Unleash the full power of the parallel tempering algorithm, Phys. Rev. Lett., 101, 130603, https://doi.org/10.1103/PhysRevLett.101.130603, 2008. a
Bottou, L.: Stochastic gradient learning in neural networks, Proc. Neuro-Nımes, 91, 12, 1991. a
Download
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.