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

TL;DR

Bayeslands employs Bayesian inference with MCMC to estimate and quantify uncertainty of parameters in the Badlands landscape evolution model, effectively integrating observational data and prior knowledge despite complex, multi-modal posterior distributions.

Contribution

This paper introduces Bayeslands, a novel Bayesian framework for parameter inference in Badlands, addressing challenges of data scarcity and model complexity.

Findings

Bayeslands produces promising parameter distributions.

The framework handles complex, multi-modal posteriors.

Sampling irregular distributions remains challenging.

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 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.