Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution

The Bayesian paradigm is becoming an increasingly popular framework for estimation and uncertainty quantification of unknown parameters in geophysical inversion problems. Badlands is a landscape evolution model for simulating topography evolution at a broad range of spatial and temporal scales. Our previous work presented Bayeslands that used the Bayesian inference for estimating unknown parameters in the Badlands model using Markov chain Monte Carlo sampling. Bayeslands faced challenges in terms of computational issues and convergence due to multimodal posterior distributions. Parallel tempering is an advanced Markov chain Monte Carlo method suited for irregular and multimodal posterior distributions. In this paper, we extend Bayeslands using parallel tempering with high‐performance computing to address previous limitations in Bayeslands. Our results show that parallel tempering Bayeslands not only reduces the computation time‚ but also provides an improvement in sampling multimodal posterior distributions, which motivates future application to continental scale landscape evolution models. Key Points We enhance the Bayeslands framework for estimation of free parameters in landscape evolution models We use high‐performance computing with parallel tempering MCMC for enhancing Bayeslands The enhanced Bayeslands framework not only reduces the computation time, but also provides better exploration of multimodal distributions