Multi-core parallel tempering Bayeslands for basin and landscape evolution

Understand

The Bayesian paradigm is becoming an increasingly popular framework for estimation and uncertainty quantification of unknown parameters in geo-physical inversion problems.

• Badlands is a basin and landscape evolution forward model for simulating topography evolution at a large range of spatial and time scales.
• Our previous work presented Bayeslands that used the Bayesian paradigm to make inference for unknown parameters in the Badlands model using Markov chain Monte Carlo (MCMC) sampling.
• Bayeslands faced challenges in convergence due to multi-modal posterior distributions in the selected parameters of Badlands.