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 the stratigraphy of sediment deposition through time. The inference of unknown parameters is challenging due to the scarcity of data, sensitivity of the parameter setting and complexity of the Badlands model. In this paper, we take a Bayesian approach to provide inference using Markov chain Monte Carlo sampling (MCMC). We present \textit{Bayeslands}; a Bayesian framework for Badlands that fuses information obtained from complex forward models with observational data and prior knowledge. As a proof-of-concept, we consider a synthetic and real-world topography with two parameters for Bayeslands inference, namely precipitation and erodibility. The results of the experiments show that Bayeslands yields a promising distribution of the parameters. Moreover, we demonstrate the challenge in sampling irregular and multi-modal posterior distributions using a likelihood surface that has a range of sub-optimal modes.
翻译:Badlands(盆地和地貌动态模型)是一种地貌演变模型,它模拟不同空间和时间尺度的地形发展。我们提出\ textit{Bayesland};一个Bayesian Badlands框架,它将从复杂的远期模型获得的信息与观测数据和先前的知识结合起来。作为概念的证明,我们考虑的是合成和真实世界地形学,其中有两种参数,即降水和可辨性。实验结果显示,Bayeslands地的参数分布前景良好。 此外,我们用一种不规则的地表分布方式展示了在海面上进行抽样的概率。