Bathymetry Reconstruction by Bayesian Inference

TL;DR

This paper introduces a Bayesian inference method for reconstructing bathymetry from water height measurements, demonstrating improved accuracy and uncertainty quantification over traditional methods.

Contribution

The paper presents a novel Bayesian framework for bathymetry reconstruction that outperforms existing optimization techniques in accuracy and uncertainty estimation.

Findings

Bayesian approach improves NRMSE of bathymetry reconstructions.

Method provides better qualitative features of the reconstructed bathymetry.

Framework successfully applied to real-world data from a wave flume.

Abstract

Bathymetry reconstruction is an important problem in various fields, including oceanography and environmental monitoring. This paper presents a Bayesian inference approach to reconstructing bathymetries from point measurements of the water height. We test the method for parameterized and discretized bathymetries with synthetic data to evaluate its performance and limitations. Our results indicate that the Bayesian framework provides a robust approach to bathymetry reconstruction. Finally, we use the framework to reconstruct a real-world bathymetry in a wave flume from experimental measurements and compare its performance to an adjoint optimization method. The Bayesian approach improves the normalized root mean squared error (NRMSE) of the reconstruction and provides better qualitative features, while also quantifying uncertainty.