BayesLands: A Bayesian inference approach for parameter uncertainty quantification in Badlands

@article{Chandra2019BayesLandsAB,
  title={BayesLands: A Bayesian inference approach for parameter uncertainty quantification in Badlands},
  author={Rohitash Chandra and Danial Azam and R. M{\"u}ller and T. Salles and Sally Cripps},
  journal={Comput. Geosci.},
  year={2019},
  volume={131},
  pages={89-101}
}
  • Rohitash Chandra, Danial Azam, +2 authors Sally Cripps
  • Published 2019
  • Computer Science, Physics, Geology
  • Comput. Geosci.
  • Abstract Bayesian inference provides a rigorous methodology for estimation and uncertainty quantification of parameters in geophysical forward models. Badlands (basin and landscape dynamics model) is a landscape evolution model that simulates topography development at various space and time scales. Badlands consists of a number of geophysical parameters that needs estimation with appropriate uncertainty quantification; given the observed present-day ground truth such as surface topography and… CONTINUE READING
    9 Citations
    Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success
    • 16
    • PDF

    References

    SHOWING 1-10 OF 65 REFERENCES
    Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem
    • 299
    • PDF
    Characterization of groundwater contaminant source using Bayesian method
    • H. Wang, X. Jin
    • Computer Science
    • Stochastic Environmental Research and Risk Assessment
    • 2012
    • 32
    Bayesian Mixture Modelling in Geochronology via Markov Chain Monte Carlo
    • 50
    • PDF
    Modelling landscape evolution
    • 345